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vlink="purple" style="tab-interval:36.0pt"> <div class="WordSection1"> <div> <h1 align="left" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:left"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-fareast-font-family:&quot;Times New Roman&quot;;mso-hansi-theme-font:major-latin; color:windowtext">ICAOR 2014 ABSTRACTS</span><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-fareast-font-family: &quot;Times New Roman&quot;;mso-hansi-theme-font:major-latin"><o:p></o:p></span></h1> <p><b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin;color:windowtext">6<sup>TH</sup> INTERNATIONAL CONFERENCE ON APPLIED OPERATIONAL RESEARCH<o:p></o:p></span></b></p> <p><b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin;color:windowtext">29-31 JULY 2014, VANCOUVER, BC CANADA</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin"><o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">THE HEALTHCARE SECTOR NEEDS MORE OPERATIONAL RESEARCH<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Tomas Eric <span class="SpellE">Nordlander</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">SINTEF ICT, Department of Applied Mathematics, Oslo, Norway<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. More efficient use of resources and improved quality of services is needed in the health care sector, in order to meet the challenges of aging populations coupled with rising quality expectations due to technological advances and desire to cap or reduce budgets. In healthcare, complex decisions at strategic, tactical, and operational levels are coupled across organizational boundaries, with interdependency between plans that share many of the same resources and infrastructure. Decision support tools from Operations Research have for decades been successfully applied to complex resource management problems in other industries. While such tools are needed in the health sector, they are no panacea but maybe one of the most promising approach to ease their strain. A wide-spread application of such tools will increased efficiency at hospitals and patients will experience more streamlined coordination of activities, improved predictability and regularity getting a higher service levels and ultimately better quality of health care services.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">APPROXIMATED DYNAMIC PROGRAMMING ALGORITHMS WITH VARIABLE NEIGHBOURHOOD SEARCH FOR REFORMED DYNAMIC QUADRATIC ASSIGNMENT PROBLEMS <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Pongchanun</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Luangpaiboon</span> <sup>1</sup>, <span class="SpellE">Sirirat</span> <span class="SpellE">Muenvanichakul</span> <sup>2</sup> and <span class="SpellE">Peerayuth</span> <span class="SpellE">Charnsethikul</span> <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Industrial Statistics and Operational Research Unit (ISO-RU), Dept of Industrial Engineering, Faculty of Engineering, <span class="SpellE">Thammasat</span> University, <span class="SpellE">Pathumthani</span>, Thailand<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Dept of Industrial Engineering, Faculty of Engineering, <span class="SpellE">Kasersart</span> University, Bangkok, Thailand<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. When determining the optimal solution of the dynamic quadratic assignment problem (DQAP) it is extremely difficult since it is the NP hard problem. The reformed dynamic quadratic assignment problem (RDQAP) has been reformulated and applied in two alternatives of <span class="SpellE">linearised</span> and logic-based models after proving the model equivalence. This study follows the former and introduces the <span class="SpellE">hybridisation</span> of the conventional dynamic programming algorithm with the meta-heuristics of bee colony optimisation (ADPA-I) and simulated annealing (ADPA-II) algorithms called the approximated dynamic programming algorithms (ADPA).<span style="mso-spacerun:yes">  </span>In order to improve quality of solutions the variable <span class="SpellE">neighbourhood</span> search is also included with given initial solutions from the ADPA. In the context of ADPA, the searching procedures are incomplete as in the original DPA. For each period, a set of best solutions provided by metaheuristics is determined as the initial solution set. The number of all possible solutions is the product of initial solution set of all periods. Numerical results explain the superior quality of the ADPA-I obtained solutions when compared.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">IDENTIFYING AIRCRAFT AND PERSONNEL NEEDS TO MEET ON-STATION PATROL REQUIREMENTS<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">F-A Bourque <sup>1,</sup>*, R <span class="SpellE">Mirshak</span> <sup>1</sup>, PL <span class="SpellE">Massel</span> <sup>1</sup>, BU Nguyen <sup>1</sup> and Major JCW Ansell <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Centre for Operational Research and Analysis, Defence Research and Development Canada, Department of National Defence, Ottawa, ON, Canada<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="GramE">Directorate</span> of Naval Strategy, Department of National Defence, Ottawa, ON, Canada<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Determining the required number of aircraft and personnel to maintain a given presence on station is a common and important problem in military contexts.<span style="mso-spacerun:yes">  </span>In this paper, the proposed approach consists of finding schedules for multiple aircraft that meet the on-station requirement while minimizing the number of aircraft and crews required. Specifically, after discussing the schedule for a single aircraft, an integer program for the multiple aircraft case is formulated and presented. To capture the effect of unplanned maintenance on the overall number of required aircraft, a parameterized serviceability model is also introduced. As illustrated by a case study, this easy to implement methodology is able to provide quick and insightful results to the decision makers.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">BLOOD SUPPLY CHAIN WITH INSUFFICIENT SUPPLY:<span style="mso-spacerun:yes">  </span>A CASE STUDY OF LOCATION AND ROUTING IN THAILAND <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Pornpimol</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Chaiwuttisak</span>, <span class="SpellE">Honora</span> Smith, <span class="SpellE">Yue</span> Wu and Chris Potts<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">University of Southampton, <span class="SpellE">Highfield</span>, Southampton, United Kingdom<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Decision making on facility locations for blood services and blood distribution plan has an impact on the efficiency of blood supply chain and logistics systems. In the blood supply chain operated by the Thai Red Cross Society (TRCS), problems are faced with amounts of blood collected in different provinces of Thailand being insufficient to meet demand. A proposal has been made to extend this network of blood <span class="SpellE">centres</span> using low-cost collection and distribution <span class="SpellE">centres</span>. Increasing numbers of fixed collection sites can improve access for donors. In addition, some facilities can perform preparation and storage for blood that hospitals can receive directly. Selecting sites for these two types of facility within a limited investment budget informs the strategic plan of this non-profit <span class="SpellE">organisation</span>. Furthermore, we consider the blood delivery problem to hospitals under variable and insufficient supplies of blood. Hospitals are assigned either to fixed routes or variable routes according to their location. Blood is supplied weekly to hospitals in the fixed route, while the frequencies of blood distribution to hospitals in the variable routes changes with the quantity of blood available daily. In the paper, we present a novel binary integer programming model for this location-allocation problem based on objectives of improving supply of blood products while reducing costs of transportation.<span style="mso-spacerun:yes">  </span>Furthermore, we propose an online system for updating the delivery schedule over the planning horizon. Different policies for allocation and routing are compared, with a case study in northern Thailand. Computational results are reported, using real life data that are of practical importance to decision makers.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">DETERMINING OPTIMAL OSMOTIC DRYING PARAMETERS FOR PAPAYA USING THE FIREFLY ALGORITHM <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Julian Scott <span class="SpellE">Yeomans</span> <sup>1</sup> and <span class="SpellE">Xin</span>-She Yang <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> OMIS Area, <span class="SpellE">Schulich</span> School of Business, York University, Toronto, ON, Canada<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="GramE">School</span> of Science and Technology, Middlesex University, London, UK<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This study employs a Firefly Algorithm (FA) to determine the optimal osmotic dehydration parameters for papaya. The functional form of the osmotic dehydration model is established via a standard response surface technique. The format of the resulting optimization model to be solved is a non-linear goal programming problem. While various alternate solution approaches are possible, an FA-driven procedure is employed. For optimization purposes, it has been demonstrated that the FA is more computationally efficient than other such commonly-used metaheuristics as genetic algorithms, simulated annealing, and enhanced particle swarm optimization. Hence, the FA approach is a very computationally efficient procedure. It can be shown that the resulting solution determined for the osmotic process parameters is superior to those from all previous approaches.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">AN ANALYTICAL FRAMEWORK WITH A NETWORK-BASED OPTIMIZATION MODEL FOR RESCHEDULING A DELAY CONTAINERSHIP <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Hua</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin">-An Lu<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Department of Shipping and Transportation Management,<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">National Taiwan Ocean University, Keelung, Taiwan<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Schedule reliability is one of the advantages that container liner shipping can play an important role in the global logistics system. However, some environment conditions and special situations will occasionally cause ship delays. This research introduces some viable strategies that can be applied to get a delay containership back on track in actual practice. An analytical framework with a network-based optimization model is proposed herein to assess the possible alternatives for counteracting delays at various target ports. Analysis results for a short sea service present the relationships between extra costs and countermeasures.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A HEURISTIC APPROACH FOR AN INVENTORY ROUTING PROBLEM WITH BACKORDER DECISIONS <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Stella <span class="SpellE">Sofianopoulou</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Department of Industrial Management &amp; Technology, University of Piraeus, Greece<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. A multi-period inventory-routing problem is considered where a vendor serves multiple geographically dispersed customers who receive units of a single product from a depot, with adequate supply, using a capacitated vehicle. The class of problems arising from the combination of routing and inventory management decisions is known as the inventory routing problem (IRP). In this category of problems, the inventory routing problem with backorders (IRPB) deals with determining inventory levels when backorders are allowed. The aim is to minimize the total cost for the planning period, comprising of holding cost, transportation and backorder penalty cost while ensuring that inventory level capacity constraints are not violated. An Integer Programming model is first developed to provide an accurate description of the problem and then a Genetic Algorithm (GA) with suitably designed genetic operators is employed in order to obtain near optimal solutions. Computational results are presented to demonstrate the effectiveness of the proposed procedure.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A GENERALIZED ADDITIVE NEURAL NETWORK APPLICATION IN INFORMATION SECURITY <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Tiny du <span class="SpellE">Toit</span> and <span class="SpellE">Hennie</span> Kruger<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">North-West University, Potchefstroom Campus, Potchefstroom, South Africa<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Traditionally spam has been considered as an inconvenience requiring workers to sift through and delete large numbers of e-mail messages per day. However, new developments and the Internet have dramatically transformed the world and over the last number of years a situation has been reached where inboxes have been flooded with unsolicited messages. This has caused spam to evolve into a serious security risk with prominent threats such as spreading of viruses, server problems, productivity threats, hacking and phishing etc. To combat these and other related threats, efficient security controls such as spam filters, should be implemented. In this paper the use of a Generalized Additive Neural Network (GANN), as a spam filter, is investigated. A GANN is a novel neural network implementation of a Generalized Additive Model and offers a number of advantages compared to neural networks in general. The performance of the GANN is assessed on three publicly available spam corpora and results, based on a specific classification performance measure, are presented. The results showed that the GANN classifier produces very accurate results and may outperform other techniques in the literature by a large margin.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">REVERSE LOGISTICS DECISION MAKING FOR MODULAR PRODUCTS: THE IMPACT OF SUPPLY CHAIN STRATEGIES <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Thomas Nowak <sup>1</sup>, <span class="SpellE">Fuminori</span> <span class="SpellE">Toyasaki</span> <sup>2</sup>, Tina <span class="SpellE">Wakolbinger</span> <sup>1</sup> and David Ng <sup>3</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Institute for Transport and Logistics Management,<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Vienna University of Economics and Business, Vienna, Austria<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> School of Administrative Studies, York University, Toronto, Canada<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">3</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Department of Economics, York University, Toronto, Canada<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The importance of product modularity in mitigating negative product-related environmental effects has been widely recognized in practice and in research. This study analyzes how a company s supply chain strategy is linked with a product s optimal level of modularity and how this affects efficient reverse logistics decision making. We address this problem by formulating two optimization problems; one for a company adopting a push and one for a company adopting a pull supply chain strategy.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A DIET EXPERT SYSTEM UTILIZING LINEAR PROGRAMMING MODELS IN A RULE-BASED INFERENCE ENGINE <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Annette van <span class="SpellE">der</span> <span class="SpellE">Merwe</span>, <span class="SpellE">Hennie</span> <span class="SpellE">Krüger</span> and <span class="SpellE">Tjaart</span> <span class="SpellE">Steyn</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">North-West University, Potchefstroom Campus, Potchefstroom, South Africa<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Linear programming is commonly used for solving complex problems in various fields, such as dietetics. Expert systems use expertise and inference procedures to solve problems that require advanced expert knowledge and are also applied to health related problems. Over the years many variations and facets of the diet problem and other related problems have been solved by means of linear programming techniques as well as expert systems. In this research, an expert system was created for the purpose of solving multiple facets of the diet problem, by creating a rule-based inference engine consisting of goal programming- and multi-objective linear programming models. The program was successfully applied to case studies specific to South African teenage girls, which were obtained through the knowledge acquisition phase. The resulting system compiles an eating-plan for a girl that conforms to the nutritional requirements of a healthy diet, includes the personal food preferences of the girl, and consists of food items that result in the lowest total cost. The system also allows prioritization of the food preference and least cost factors by means of weighted priorities.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">KALMAN-FILTERING FORMULATION DETAILS FOR DYNAMIC OD PASSENGER MATRIX ESTIMATION <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Lídia</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Montero, <span class="SpellE">Esteve</span> <span class="SpellE">Codina</span> and <span class="SpellE">Jaume</span> Barceló<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Department of Statistics and Operations Research, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Barcelona TECH-UPC, <span class="SpellE">Carrer</span> <span class="SpellE">Jordi</span> <span class="SpellE">Girona</span> 1-3<span style="mso-spacerun:yes">  </span>08034 Barcelona, Spain<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. In this paper, we describe how to estimate time-sliced origin-destination (OD) matrices for passengers in a public transport network based on counts of ICT (Intelligent Communication Technology) devices carried by passengers at equipped transit-stops. The transit assignment framework is based on optimal strategy, which determines the subset of paths related to the optimal strategies between all OD pairs for the whole horizon of study. Details are provided on how to build the involved equations in a linear <span class="SpellE">Kalman</span> filtering model formulation, which is defined by the authors for a toy network that is proposed to validate the approach.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">DETERMINING FLEET SIZE FOR A CANADIAN MARITIME PATROL AIRCRAFT<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Richard McCourt <sup>1,</sup>*, Sean Bourdon <sup>1</sup> and Matthew MacLeod <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Defence Research and Development Canada,<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Centre for Operational Research and Analysis, Ottawa, Canada<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Defence Research and Development Canada,<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Centre for Operational Research and Analysis, Halifax, Canada<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Maintaining the ability to continuously track vessels in a maritime area of responsibility involves a mixture of factors relating to the performance of the aircraft, its sensor systems and the operators on board. This paper presents a series of simplifying assumptions and equations for considering trade-offs between three of the most important of these factors: aircraft speed, endurance and fleet size. The paper briefly presents the results obtained for a life-extended fleet of Canadian Maritime Patrol Aircraft, while also allowing the consideration of requirements for its replacement.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">HEURISTICS FOR SINGLE MACHINE SCHEDULING UNDER COMPETITION TO MINIMIZE TOTAL WEIGHTED COMPLETION TIME AND MAKESPAN OBJECTIVES <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Yuvraj</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Gajpal</span> <sup>1,</sup>*, <span class="SpellE">Alok</span> <span class="SpellE">Dua</span> <sup>1</sup> and <span class="SpellE">Shesh</span> <span class="SpellE">Narayan</span> <span class="SpellE">Sahu</span> <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Asper</span> School of Business, University of Manitoba, Winnipeg, Manitoba, R3T5V4, Canada<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> National Institute of Technology, <span class="SpellE">Agartala</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Department of Computer Science and Engineering, Tripura, 799055, India<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This paper considers a single machine scheduling problem, where two agents compete for the use of a single processing resource. Each of the agents needs to process a set of jobs with the common resource to optimize their own objective function which depends on the completion time of its own jobs. The goal is to minimize the total weighted completion time of first agent subject to an upper bound on the <span class="SpellE">makespan</span> of the second agent. The problem is binary NP-hard. We propose three simple heuristic for the problem. These heuristics are based on shortest processing time rule, highest weight first rule and weighted shortest process time first rule. Numerical experiment is performed on randomly generated problem instances. Heuristic performances are evaluated by comparing it with the exact solution.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A SIMPLIFIED MODEL FOR SCHEDULING SERVICES ON AUXILIARY BUS LINES <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">E. <span class="SpellE">Codina</span> and L. Montero<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Statistics and Operations Research Department, <span class="SpellE">Universitat</span> <span class="SpellE">Politècnica</span> de <span class="SpellE">Catalunya</span>,<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Campus Nord, Building C5, 08034, Barcelona, Spain<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. In this paper, a mathematical programming based model is described to assist with the schedule of services for a set of auxiliary bus lines that operate alleviating a disruption of the regular transportation system during a given time period. In contrast to other models, considered static, service schedules are set taking into account demand fluctuations that may happen in that time period. Passenger flows are represented with a multi-commodity structure and disseminate through paths on a diachronic capacitated network which lead them to their destination in the shortest time taking into account the available capacities of the bus units. The model permits to accommodate the schedules of the auxiliary bus lines in order to enhance transfers and to minimize total travel time. The model assumes that operational times at bus stops are constant and that buses do not queue at stops. The solution method can be considered an heuristic that combines searches along <span class="SpellE">subgradient's</span> projected directions with a pattern search based method for constrained optimization.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">PRICING, PRODUCT QUALITY, AND RETAIL SERVICE IN A THREE-ECHELON SUPPLY CHAIN <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Gerhard <span class="SpellE">Aust</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">TU Dresden, <span class="SpellE">Fakultaet</span> <span class="SpellE">Wirtschaftswissenschaften</span>, 01062 Dresden, Germany<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This paper proposes a model of a three-echelon supply chain consisting of one supplier, one manufacturer, and one retailer, who serve customer demand that is sensitive to retail price, product quality, and retail service. By means of game theory, the firms optimal strategies regarding margins, quality level, and service level are determined analytically for three different scenarios: (a) a symmetrical distribution of power within the supply chain (Nash game); (b) a dominant manufacturer (Manufacturer <span class="SpellE">Stackelberg</span> game); (c) a joint profit maximization of the firms (Co-operation game). The solutions are analyzed on the base of a first numerical example, which proves that the presented model yields logically consistent results.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">FROM MAXPLUS ALGEBRA TO GENERAL LOWER BOUNDS FOR THE TOTAL WEIGHTED COMPLETION TIME IN FLOWSHOP SCHEDULING PROBLEMS <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Nhat-Vinh</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Vo and Christophe <span class="SpellE">Lenté</span><o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Université</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> François-Rabelais de Tours, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">CNRS, LI EA 6300, OC ERL CNRS 6305, Tours, France<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The <span class="SpellE">flowshop</span> scheduling problem has been largely studied for 60 years. As a criterion, the total weighted completion time reflects the total weighted waiting time of all customers. There have not been many studies about this criterion and they are limited in the number of machines or constraints. <span class="SpellE">MaxPlus</span> algebra is also applied to the scheduling theory but the literature focuses on some concrete constraints. Therefore, this study addresses a general permutation <span class="SpellE">flowshop</span> problem, with several additional constraints such as delays, blocking or setup times, to elaborate on lower bounds for the total weighted completion time. These lower bounds imply solving a Traveling Salesman Problem. The principle, based on a <span class="SpellE">MaxPlus</span> modeling of <span class="SpellE">flowshop</span> problems, is developed and experimental results are presented.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A MATHEMATICAL PROGRAMMING APPROACH FOR SOLVING THE PROBLEM OF FIXING PRICES AND AUGMENTING CAPACITY FOR TWO COMPETING SUPPLIERS <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Gaytán</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Juan <sup>1</sup>, <span class="SpellE">García</span> Marco <sup>2</sup> and Arroyo <span class="SpellE">Pilar</span> <sup>3</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Facultad</span> de <span class="SpellE">Ingeniería</span>, Universidad <span class="SpellE">Autónoma</span> del Estado de México. Toluca, Mexico. Mexico<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Escuela</span> de <span class="SpellE">Ingeniería</span>, <span class="SpellE">Tecnológico</span> de Monterrey campus Morelia. <span class="SpellE">Michoacan</span>. Mexico<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">3</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Escuela</span> de <span class="SpellE">Ingeniería</span>, <span class="SpellE">Tecnológico</span> de Monterrey campus Toluca. Toluca, México. Mexico<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The decision to produce internally or outsource the manufacturing of certain goods is critical because of its effects on the utilized capacity, the production costs, the quality of products and the customer service. A bad performance of external suppliers represents a potential risk for an organization; however the internal production of low-value products involves the use of resources (physical and human) that may be used to manufacture products of higher value. Original Equipment Manufacturers (OEM) in the automotive sector need to identify reliable suppliers to who outsource the manufacturing of parts and components. In the case these suppliers do not have enough capacity to produce all the goods demanded, the OEMs may incentive them to invest in additional capacity by offering to increase the volume of production subcontracted.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">SOLVING THE GREEN CAPACITATED VEHICLE ROUTING PROBLEM USING A TABU SEARCH ALGORITHM <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Sergio <span class="SpellE">Úbeda</span> <sup>1</sup>, Javier <span class="SpellE">Faulin</span> <sup>1</sup>, <span class="SpellE">Adrián</span> Serrano <sup>1</sup> and Francisco J <span class="SpellE">Arcelus</span> <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Department of Statistics and Operations Research. Public University of Navarre, Pamplona, Spain<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Department of Business Administration. University of New Brunswick, Fredericton, Canada<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This paper analyses how the <span class="SpellE">tabu</span> search can be successfully applied to solve the Green Capacitated Vehicle Routing Problems- GCVRP. This kind of problems has been described as the classical Capacitated VRP with a criterion of environmental emissions <span class="SpellE">minimisation</span>. This criterion is based on the calculation of carbon dioxide emissions from mobile sources, which is highly dependent on several factors such as speed, weather conditions, load and distance. A case study is given to show how green routes can be obtained and to analyze whether those routes also meet the efficiency objectives or not. The results show that a <span class="SpellE">tabu</span> search approach adapts the environmental criterion better than other procedures and also produces routes which are distance effective and environmental-friendly.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A TRANSPORTATION BRANCH AND BOUND ALGORITHM FOR SOLVING THE GENERALIZED ASSIGNMENT PROBLEM <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Elias <span class="SpellE">Munapo</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Graduate School of Business and Leadership, University of KwaZulu-Natal, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Westville Campus, Durban, South Africa<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This paper presents a transportation branch and bound algorithm for solving the generalized assignment problem. This is a branch and bound technique in which the sub-problems are solved by the available efficient transportation techniques rather than the usual simplex based approaches. A technique for selecting branching variables that minimizes the number of sub-problems is also presented.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">HEURISTIC SCHEMES FOR THE EFFICIENT UTILIZATION OF 3D PRINTING STEREOLITHOGRAPHY APPARATUS <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Vassilios</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Canellidis</span>, John <span class="SpellE">Giannatsis</span> and <span class="SpellE">Vassilis</span> <span class="SpellE">Dedoussis</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">University of Piraeus 80 <span class="SpellE">Karaoli</span> &amp; <span class="SpellE">Dimitriou</span> str., 18534 Piraeus, Greece<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. 3D Printing (3DP) technologies are increasingly being employed for the production of consumer products and mechanical components in the manufacturing sector, because of the advantages they exhibit as far as fabrication speed and flexibility are considered. This shift of focus in the application of 3DP technologies puts a new emphasis on the study of some of the process planning problems and issues that are related with the cost efficient use of 3DP systems and the quality of their products. As a result, the packing or platform layout optimization problem for the simultaneous fabrication of different parts has been identified as one of the most crucial tasks encountered in the process planning phase of 3DP. In the present paper a study of this problem that focuses on 3DP technologies that due to technical or quality reasons exclude the fabrication of a part on top of another, e.g. <span class="SpellE">Stereolithography</span> (SL) is presented. The methodologies discussed in the paper, employ a heuristic optimization technique (Simulated Annealing) in conjunction with two placement schemes, appropriately adapted to the problem.<span style="mso-spacerun:yes">  </span>The reliability of the methodologies under discussion is evaluated via a case study concerning representative  real-world parts/objects with quite general free form geometry.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">THREE METAHEURISTICS FOR THE CONSTRUCTION OF CONSTANT GC-CONTENT DNA CODES <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">R. <span class="SpellE">Montemanni</span> <sup>1</sup>, D.H. Smith <sup>2</sup> and N. <span class="SpellE">Koul</span> <sup>1</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> IDSIA - <span class="SpellE">Università</span> <span class="SpellE">della</span> <span class="SpellE">Svizzera</span> <span class="SpellE">Italiana</span>/SUPSI, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Via G. <span class="SpellE">Buffi</span> 13, 6904 <span class="SpellE">Lugano</span>, Canton Ticino, Switzerland<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Division of Mathematics and Statistics, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">University of South Wales, <span class="SpellE">Pontypridd</span>, CF37 1DL, UK<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. DNA codes are sets of words of fixed length n over the alphabet {A, C, G, T} which satisfy a number of combinatorial conditions. The combinatorial conditions considered are (<span class="SpellE">i</span>) minimum Hamming distance d, (ii) fixed GC-content and, in some cases (iii) minimum distance d between any codeword and the reverse Watson-Crick complement of any codeword. The problem is to find DNA codes with the maximum number of <span class="SpellE">codewords</span>. In this paper three different meta-heuristic approaches for the problem are discussed, and the outcome of an extensive experimental campaign, leading to many new best-known codes, is presented.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">OPTIMIZING THE SUPPLY CHAIN CONFIGURATION WITH SUPPLY DISRUPTIONS <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Ruiqing</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Xia and Hiroaki Matsukawa<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Keio University, Yokohama, Japan<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The purpose of this paper is to investigate a supplier-retailer supply chain that experiences disruptions in supplier during the planning horizon. There might be multiple options to supply a raw material, to manufacture or assemble the product, and to transport the product to the customer.<span style="mso-spacerun:yes">  </span>While determine what suppliers, parts, processes, and transportation modes to select at each stage in the supply chain, options disruption must be considered. In this paper, we show that changes to the original plan induced by a disruption may impose considerable deviation costs throughout the system. When the production plan and the supply chain coordination scheme are designed in a static manner, as is most often the case, both will have to be adjusted under a disruption scenario. Using dynamic policies, we derive conditions under which the supply chain can be coordinated so that the maximum potential profit is realized.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A CLOSED-LOOP SUPPLY CHAIN REPACKAGING LINEAR OPTIMIZATION PROBLEM <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">José <span class="SpellE">Díaz</span> De La <span class="SpellE">Hoz</span> and Hiroaki Matsukawa<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Keio University, Yokohama, Japan<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This paper presents a model for optimizing the costs of a complex production, distribution and package reprocessing industry. Building on previous work, closed-loop supply chain activities are incorporated in the form of a reverse flow of empty packages and reprocessing of those packages for further utilization. A linear programming mathematical framework is proposed for representing the different elements of the industry, allowing for high flexibility when applying the model to different industries. The optimization model provides the optimal decision variables for inventory management, production and recycling planning and scheduling, transport routing and empty package purchasing. The proposed framework allows for the introduction and alteration of multiple parameters in each of the different elements of the chain. Finally, a numerical example, involving a bottling industry that deals with standardized packaging, is solved utilizing <span class="SpellE">Gurobi</span> software, and a minimal cost solution is found, thus proving the model s validity.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">HEURISTIC SOLUTION METHODS FOR THE FIBER TO THE HOME CABLING PROBLEM<o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Matthieu</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Chardy</span> <sup>1</sup> and <span class="SpellE">Dorra</span> <span class="SpellE">Dhouib</span> <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Orange Labs, <span class="SpellE">Issy</span>-Les-<span class="SpellE">Moulineaux</span>, France<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> ENSTA <span class="SpellE">Paristech</span>, <span class="SpellE">Palaiseau</span>, France <o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The deployment of Fiber To The Home (FTTH) technologies proves one of the most challenging issues for telecommunication operators. This paper focuses on the FTTH cabling problem which is modeled as a single-commodity flow problem with non-linear costs. Scalable heuristic approaches are presented and benchmarked on a real-life instances test set.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A FAST HEURISTIC FOR THE PRIZE-COLLECTING STEINER TREE PROBLEM <o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Murodzhon</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Akhmedov</span>, <span class="SpellE">Ivo</span> <span class="SpellE">Kwee</span> and Roberto <span class="SpellE">Montemanni</span><o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Dalle</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> <span class="SpellE">Molle</span> Institute for Artificial Intelligence IDSIA-USI/SUPSI<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Galleria 2, CH-6928 <span class="SpellE">Manno</span>, Switzerland<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. The Prize-Collecting Steiner Tree Problem (PCSTP) is a generalized version of the Steiner Tree Problem. PCSTP is well known and well studied problem in Combinatorial Optimization. Since PCSTP is NP-hard, it is computationally costly to achieve solutions for large instances. However, many real life network problems come with a wide range of variables and large instance sizes. Therefore, there is a need for efficient and fast heuristic algorithms to discover the hidden knowledge behind vast networks. There exists a fast heuristic algorithm for the Steiner Tree Problem in the literature, which is based on Minimum Spanning Trees. In this paper, we propose to extend the existing heuristic algorithm to solve PCSTP. The performance of the extended heuristic (MST-PCST) is evaluated on available benchmark instances from the literature. We also test MST-PCST on randomly generated huge graph instances with up to 40000 nodes and 120000 edges. We report the average gap percentage between the solutions of MST-PCST and existing solution approaches in the literature. Results show that overall performance of MST-PCST is promising with tolerable gap percentage and reasonable running time on larger instances. It has a significantly faster running time when graphs scale up which can shed light on large real world network instances.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">GIS-BASED OPTIMIZATION FOR ADVANCED BIOFUELS SUPPLY CHAINS: A CASE STUDY IN TENNESSEE<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">T. Edward Yu, <span class="SpellE">Lixia</span> He, Burton C. English and James A. Larson<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">University of Tennessee, Knoxville, USA<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. <span class="SpellE">Biofuel</span> production from <span class="SpellE">lignocellulosic</span> biomass (LCB) is being advocated as an alternative to fossil-based transportation fuels in the United States. Given the substantial technical barriers related to the harvest, storage, and transportation of the LCB feedstock, this study developed a GIS-based mixed integer programming model to evaluate how the spatial and geographic attributes affect the optimal placement and configuration of a <span class="SpellE">switchgrass</span>-based <span class="SpellE">biofuel</span> supply chain. Using west Tennessee as a case study, results indicate that the type of agricultural land converted to feedstock production and the transportation cost of hauling feedstock and <span class="SpellE">biofuels</span> were influential to the selection of the most profitable supply chain configuration.<span style="mso-spacerun:yes">  </span>The location of the conversion facilities and feedstock draw areas were also related to the choice of agricultural land use for feedstock production and the cost of hauling feedstock and advanced <span class="SpellE">biofuels</span>.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">MULTI-NODE OFFER STACK OPTIMIZATION OVER ELECTRICITY NETWORKS <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Anthony Downward, Danny Tsai and <span class="SpellE">Yibo</span> <span class="SpellE">Weng</span><o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">University of Auckland, Auckland, New Zealand<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. In this work we examine the problem that electricity generators face when offering power at multiple locations into an electricity market. The amount of power offered at each node can affect the price at the other node, so it is important to optimize all offers simultaneously. Even with perfect information (i.e. known demand, and known offers from competitors) this is a non-convex bi-level optimization problem. We first show how this can be formulated as an integer program using special ordered sets of type 2 (SOS2) enabling this problem to be solved efficiently. We then extend this work to allow for uncertainty, and hence find the profit <span class="SpellE">maximising</span> offer stacks at each node (as opposed to a single quantity, as in the deterministic case). We demonstrate the intuition that we can gain from this model in a simple two-node example, and discuss extensions to this work such as the co-optimization of reserve and generation, as well as demand-side bidding.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">PRICE, QUALITY AND ADVERTISING DECISIONS CONSIDERING REFERENCE QUALITY EFFECTS: SEARCH VERSUS EXPERIENCE GOODS<o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Yanyan</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> He <sup>1</sup>, <span class="SpellE">Qinglong</span> Gou <sup>1</sup>, Susan Li <sup>2</sup> and <span class="SpellE">Zhimin</span> Huang <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> School of Management, University of Science and Technology of China, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Hefei, Anhui, 230026, P.R. China<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Robert B. <span class="SpellE">Willumstad</span> School of Business, Adelphi University, <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Garden City, NY 11530-0701, USA<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. This research is stimulated from two facts. First, most products can be either search goods or experience goods. Consumers can learn the product quality of search goods before purchase, yet they can know that of experience goods until they have bought and used them for a certain period. Second, there is usually a kind of expectation on the quality of products, i.e. reference quality, formulated in a consumer's mind before he makes his purchase decision. Thus, when a consumer faces a search goods, he can compare its quality with his expectation and thus his decision will be influenced by the difference; yet he can make his decision just depending on his expectation when he occurs to an experience one since he cannot observe its quality before purchase. In this paper, we incorporate this fact with a modified <span class="SpellE">Nerlove</span> Arrow model and then investigate firms' joint decisions on price, quality and advertising. The firms' optimal decisions are derived out under a differential game theory framework. Our results show that when the firms make their decisions mentioned above, they should consider the characteristics of their products seriously.<o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">A DYNAMIC PORTFOLIO SELECTION MODEL USING A NONSTATIONARY INVESTMENT TARGET ACCORDING TO THE STOCK MARKET FORECAST<o:p></o:p></span></p> <p><span class="SpellE"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">Jongbin</span></span><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Jung and <span class="SpellE">Seongmoon</span> Kim<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">School of Business, <span class="SpellE">Yonsei</span> University, Seoul, South Korea<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. In this paper, we propose an adaptive investment strategy (AIS) based on a dynamic portfolio selection model (DPSM) that uses a time-varying investment target according to the market forecast. The DPSM allows for flexible investments, setting relatively aggressive investment targets when market growth is expected and relatively conservative targets when the market is expected to be less attractive. By dynamically determining the investment target, the DPSM allows construction of portfolios that are more responsive to market changes. When the proposed DPSM is implemented in real-life investment scenarios using the AIS, the portfolio is rebalanced according to a predefined rebalancing cycle and the model s input parameters are estimated on each rebalancing date using an exponentially weighted moving average (EWMA) estimator. To evaluate the performance of the proposed approach, a 7-year investment experiment was conducted using historical stock returns data from 10 different stock markets around the world. Performance was assessed and compared using diverse measures. Superior performance was achieved using the AIS proposed herein compared to various benchmark approaches for all performance measures. <o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">SIMHEURISTICS AND BIASED RANDOMIZATION OF HEURISTICS TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEMS IN LOGISTICS AND TRANSPORTATION<o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Javier <span class="SpellE">Faulin</span> <sup>1</sup> and Angel Juan <sup>2</sup><o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">1</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Department of Statistics and Operations Research, Public University of Navarre, Pamplona, Spain<o:p></o:p></span></p> <p><sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">2</span></sup><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin; mso-hansi-theme-font:major-latin"> Department of Computer Science, Open University of Catalonia, Barcelona, Spain <o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Combinatorial optimization problems are a very common research area in the Logistics and Transportation field and have been analyzed and solved using different types of procedures and algorithms. Here, we introduce two new interconnected methodologies called Biased Randomization and <span class="SpellE">Simheuristics</span>. The Biased Randomization of a heuristic is a way to try to escape from the local optima in a combinatorial optimization problem using biased probability distributions to generate a set of potential good solutions, not necessarily optimal, to a deterministic problem. When we consider stochastic problems and we use simulation techniques to produce a big number of potential good solutions, a <span class="SpellE">Simheuristics</span> method is being employed. Both kinds of methods can be applied to solve an assorted group of problems such as the capacitated vehicle routing problem, the vehicle routing problem with stochastic demands, the capacitated arc routing problem, permutation <span class="SpellE">flowshop</span> sequencing problem, or the electric vehicle routing problem, among others. The results obtained using these procedures are very competitive from the computational point of view and very practical in the generations of good solutions for real world cases. <o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">OPTIMIZE SCHEDULING AND ROUTING AT AN AIRPORT <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">Tomas Eric <span class="SpellE">Nordlander</span>, Patrick <span class="SpellE">Schittekat</span>, Dag <span class="SpellE">Kjenstad</span>, Carlo <span class="SpellE">Mannino</span> and <span class="SpellE">Morten</span> <span class="SpellE">Smedsrud</span> <o:p></o:p></span></p> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">SINTEF ICT, Oslo, Norway<o:p></o:p></span></p> <p><b style="mso-bidi-font-weight:normal"><span style="font-size:9.0pt; font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font:major-latin;mso-hansi-theme-font: major-latin">Abstract</span></b><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-hansi-theme-font:major-latin">. Air Traffic Management focus to provide efficient and safe movement of airplanes at and near the airports. This is a very complex problem that is normally divided into Arrival, Surface and a Departure Management Problem. While this division of responsibility may be practical for managing the complexity it does prevent the high level of coordination needed to ensure that global optimal decisions are made by each controller. The effect of one controller's decision propagates through to other controllers. E.g. one small valuable adjustment of one controller can very well create havoc for other controllers further down the trajectory. We present an integrated approach to the overall problem along with an optimization algorithm that heuristically decomposes the problem so routing, sequencing, and conflicts resolution are carried out in subsequent stages. Our approach has been validated in experiments on Hamburg airport which showed remarkable improvements in punctuality and taxi times compared to the expert controllers.<span style="mso-spacerun:yes">  </span><o:p></o:p></span></p> <div> <div class="MsoNormal"><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;; mso-ascii-theme-font:major-latin;mso-fareast-font-family:&quot;Times New Roman&quot;; mso-hansi-theme-font:major-latin;color:windowtext"> <hr size="2" width="100%" noshade style="color:#A0A0A0" align="left"> </span></div> </div> <p><span style="font-size:9.0pt;font-family:&quot;Cambria&quot;,&quot;serif&quot;;mso-ascii-theme-font: major-latin;mso-hansi-theme-font:major-latin">© ORLab Analytics Inc.<o:p></o:p></span></p> </div> </div> </body> </html> <!-- FILE ARCHIVED ON 15:10:19 Jul 12, 2020 AND RETRIEVED FROM THE INTERNET ARCHIVE ON 15:04:25 Feb 26, 2022. JAVASCRIPT APPENDED BY WAYBACK MACHINE, COPYRIGHT INTERNET ARCHIVE. 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