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mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="70658"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor="white" lang="EN-US" link="blue" vlink="purple" style="tab-interval:36.0pt"> <div class="WordSection1"> <p class="MsoNormal"><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">ICAOR 2017 ABSTRACTS<o:p></o:p></span></b></p> <p class="MsoNormal"><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">9<sup>TH</sup> INTERNATIONAL CONFERENCE ON APPLIED OPERATIONAL RESEARCH<o:p></o:p></span></b></p> <p class="MsoNormal"><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">18-20 DECEMBER 2017, TAOYUAN, TAIWAN<o:p></o:p></span></b></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">FUZZY GREEDY SEARCH FOR COMBINATORIAL OPTIMIZATION PROBLEMS<o:p></o:p></span></p> <p class="MsoNormal"><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">Kaveh Sheibani<o:p></o:p></span></p> <p class="MsoNormal"><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, Vancouver BC Canada<o:p></o:p></span></p> <p class="MsoNormal"><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 presentation gives an overview of the fuzzy greedy search methodology for the integration of approaches for hard combinatorial optimisation problems. The use of a search technique on a solution space is central to the design a heuristic. Indeed, applying a robust search technique significantly improves the overall performance. The proposed methodology evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism, thereby exploiting a fuzzy solution space using greedy methods. More recent developments and applications will be also discussed during the presentation.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">DYNAMIC APPOINTMENT SCHEDULING IN PRIORITY QUEUEING SYSTEMS WITH ACCESS TIME TARGETS<o:p></o:p></span></p> <p class="MsoNormal"><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">Carrie Ka Yuk Lin<o:p></o:p></span></p> <p class="MsoNormal"><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 Management Sciences, College of Business, City University of Hong Kong, Hong Kong (SAR), China<o:p></o:p></span></p> <p class="MsoNormal"><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">Multi-class priority queuing systems with access time targets and dynamic arrivals are common in many environments, including emergency, healthcare, logistics and hospitality services. This study focuses on the waiting line management of new cases in the specialist outpatient clinics of the Hospital Authority of Hong Kong. The objectives are to assign appointment dates in a way to fulfill the access time targets for urgent (highest priority) and semi-urgent (second highest priority) new cases while maintaining acceptable access time for non-urgent (routine) new cases. A dynamic appointment scheduling algorithm is developed and tested through simulated instances created from the waiting time data and hospital attendance data for a one-year horizon. The experimental results are compared with the reported performance statistics. Performances are also compared with results of the deterministic problem assuming the arrival data were given. For certain ordered set of objective weights relevant to the current problem, the deterministic problem can be formulated as a transportation model and solved optimally by the classical transportation algorithm with an additional checking procedure.<b style="mso-bidi-font-weight:normal"><o:p></o:p></b></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">TRANSIT NETWORK DESIGN AND SCHEDULING PROBLEM FOR MULTI-DEPOT ROUND-TRIP FIXED-INTERVAL ROUTES<o:p></o:p></span></p> <p class="MsoNormal"><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">James C. Chu<o:p></o:p></span></p> <p class="MsoNormal"><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">Associate Professor, Department of Civil Engineering, National Taiwan University, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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 study formulates a mixed integer programming (MIP) model for the transit network<b style="mso-bidi-font-weight: normal"> </b>design and scheduling problem (TNDSP) considering multi-depot round-trip fixed-interval<b style="mso-bidi-font-weight:normal"> </b>routes and static travel time. TNDSP is particularly cumbersome to formulate and solve because it combines three major planning activities in transit network problems (TNP): network design, frequency setting, and timetabling. Therefore, the problem is rarely considered in the literature and the few models developed in the related studies are often size-limited or simplified (<span class="SpellE">Guihaire</span> and <span class="SpellE">Hao</span>, 2008). For example, Yan and Chen (2002) considered a rural bus system in which waiting time is ignored. <span class="SpellE">Quak</span> (2003) solved the problems of network design and scheduling in a sequential manner rather than as a single problem.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">JOB INSERTION FOR THE PICKUP AND DELIVERY PROBLEM WITH TIME WINDOWS<o:p></o:p></span></p> <p class="MsoNormal"><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">Yi <span class="SpellE">Qu</span> <sup>1</sup>, Timothy <span class="SpellE">Curtois</span><o:p></o:p></span></p> <p class="MsoNormal"><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"> Newcastle Business School, <span class="SpellE">Northumbria</span> University, Newcastle, UK<o:p></o:p></span></p> <p class="MsoNormal"><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">Two heuristic algorithms are proposed for a practical but relatively under-studied vehicle routing scenario. It requires the insertion of jobs into already planned routes. It occurs when new jobs arrive throughout a day but the current plans are already being performed. The benefit of solving such a problem is providing a better service for collection and delivery jobs whilst also providing better vehicle fill rates and increased revenue for delivery companies. Solutions must be generated quickly because of the dynamic nature of the problem. Two iterative heuristic algorithms are presented and tested on a well-known benchmark set. The algorithms are able to insert new jobs quickly and efficiently and even found some new best known solutions for the benchmark instances.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">STOCHASTIC SIMULATION BASED GENETIC ALGORITHM FOR A PRODUCTION REPAIR MODEL<o:p></o:p></span></p> <p class="MsoNormal"><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. K. Jana <sup>1</sup>, <span class="SpellE">Yuvraj</span> <span class="SpellE">Gajpal</span> <sup>2</sup>, and B. <span class="SpellE">Chakraborty</span> <sup>3</sup><o:p></o:p></span></p> <p class="MsoNormal"><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"> Indian Institute of Management Raipur, GEC Campus, <span class="SpellE">Sejbahar</span>, CG 492015, India<o:p></o:p></span></p> <p class="MsoNormal"><span class="GramE"><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">Supply Chain Management</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">Asper</span> School of Business, University of Manitoba, Canada<o:p></o:p></span></p> <p class="MsoNormal"><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 Mathematics Institute of Engineering &amp; Management, Kolkata, WB 700091, India<o:p></o:p></span></p> <p class="MsoNormal"><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 deals with a stochastic production repair model with chance constraints. A solution to such problems has great importance to the managers because such a solution serves as an invaluable tool for them in the process of developing a production plan to their companies. The common way of tackling such a problem is to derive the crisp equivalent of the original problem. This is possible if the parameters involved in the chance constraint follow some specific distribution or if we can estimate or approximate the chance constraints using some estimation procedure or numerical technique. In this paper, we first formulate a multi-product, single period and single plant chance constraint programming model having production and repair work simultaneously. The uncertainty about the cost, resources and man hour required <span class="GramE">to repair</span> a product, arrival of products for repair, resources and <span class="SpellE">manhour</span> availabilities bring randomness to the formulated model. To solve the mathematical model, we propose a stochastic simulation based genetic algorithm. This algorithm can deal the stochastic parameters having any possible distribution. Finally, a numerical example is presented to prove the efficiency of the proposed algorithm.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">MODELING EMERGENCY MEDICAL RESPONSE TO A MASS CASUALTY INCIDENT IN MULTIPLE LOCATIONS<o:p></o:p></span></p> <p class="MsoNormal"><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">Yun</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">-Zhu Lin <sup>1</sup>, Pei-Jung Liao <sup>2</sup><o:p></o:p></span></p> <p class="MsoNormal"><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"> Associate Professor, <span class="SpellE">Taoyuan</span> City, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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"> Project assistant, <span class="SpellE">Taoyuan</span> City, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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">During the emergency response to mass casualty incidents decisions relating to the extrication, transporting and treatment of casualties are made in a real-time, sequential manner. In order to increase survival rates of casualties, the total response time of casualties, including waiting times at emergency sites, transportation times, waiting times at hospitals, and treatment times, needs to be minimized. The stochastic nature of injury degree and treatment time of casualties is considered in this study. We developed a multi-stage heuristic on a rolling time horizon to help decisions-makers make rescue decisions of emergency response to mass casualty incidents. Simulation of a multi-server <span class="SpellE">queueing</span> system with heterogeneous service times is embedded in the algorithm. The heuristic is evaluated over several potential problems, with results confirming its effectiveness.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">SIMULATED ANNEALING OPTIMIZATION OF TUNED MASS DAMPERS FOR VIBRATION CONTROL OF SEISMIC-EXCITED BUILDINGS<o:p></o:p></span></p> <p class="MsoNormal"><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">Ming-Yi Liu <sup>1</sup>, <span class="SpellE">Wen-Che</span> Liang <sup>2</sup>, and <span class="SpellE">Yun</span>-Zhu Lin <sup>1</sup><o:p></o:p></span></p> <p class="MsoNormal"><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 Civil Engineering, Chung Yuan Christian University, <span class="SpellE">Taoyuan</span> City, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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">Department</span> of Civil Engineering, National Cheng Kung University, Tainan City, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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 objective of this paper is to investigate the simulated annealing optimization of tuned mass dampers (TMDs) for vibration control of high-rise buildings under seismic excitations. The computational procedure for an analytical model, including the frequency domain analysis, optimization analysis and time domain analysis, is presented for this purpose. Numerical examples, including the model validation and effectiveness assessment, are also provided to illustrate the analytical model. The numerical results determined by the simulated annealing are consistent with the exact solutions obtained from the gradient-based algorithm, suggesting that the simulated annealing provides the sufficient accuracy for such problems. Furthermore, the dynamic displacements of the main structure can be successfully controlled by the TMD, indicating that the vibration energy transferred from the main structure to the TMD is attributed to the frequency loci veering and mode localization when the natural frequency of the main structure and that of the TMD approach one another.<b style="mso-bidi-font-weight:normal"><o:p></o:p></b></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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 APPLICATION OF MATRIX GAMES WITH TRAPEZOIDAL INTUITIONISTIC FUZZY PAY OFFS TO TRANSPORTATION PROBLEM<o:p></o:p></span></p> <p class="MsoNormal"><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">Ratnesh</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"> R. <span class="SpellE">Saxena</span> <sup>1</sup>, <span class="SpellE">Ritika</span> Chopra <sup>2</sup>, <span class="SpellE">Sanjiv</span> Kumar <sup>2</sup><o:p></o:p></span></p> <p class="MsoNormal"><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 Mathematics, <span class="SpellE">Deen</span> <span class="SpellE">Dayal</span> <span class="SpellE">UpadhyayA</span> College, Delhi University, Delhi, India<o:p></o:p></span></p> <p class="MsoNormal"><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">Department</span> of Mathematics, Delhi University, Delhi, India<o:p></o:p></span></p> <p class="MsoNormal"><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 two person zero sum game, with payoffs expressed as trapezoidal <span class="SpellE">intuitionistic</span> fuzzy numbers is considered. Trapezoidal <span class="SpellE">intuitionistic</span> fuzzy numbers (<span class="SpellE">TrIFNs</span>) are defined in a more general way by relaxing the normality condition in the definition. The generalized trapezoidal <span class="SpellE">intuitionistic</span> fuzzy numbers (<span class="SpellE">GTrIFNs</span>) are converted to interval valued fuzzy numbers by taking (</span><i style="mso-bidi-font-style:normal"><span style="font-size: 9.0pt;font-family:Symbol;mso-ascii-font-family:Cambria;mso-ascii-theme-font: major-latin;mso-hansi-font-family:Cambria;mso-hansi-theme-font:major-latin; mso-char-type:symbol;mso-symbol-font-family:Symbol"><span style="mso-char-type: symbol;mso-symbol-font-family:Symbol">a</span></span></i><i style="mso-bidi-font-style: 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">,</span></i><i style="mso-bidi-font-style: normal"><span style="font-size:9.0pt;font-family:Symbol;mso-ascii-font-family: Cambria;mso-ascii-theme-font:major-latin;mso-hansi-font-family:Cambria; mso-hansi-theme-font:major-latin;mso-char-type:symbol;mso-symbol-font-family: Symbol"><span style="mso-char-type:symbol;mso-symbol-font-family:Symbol">b</span></span></i><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">)-cuts. Then a new ranking index is introduced which gives a saddle point (if it exists) in an effective way. Numerical examples are provided to illustrate the proposed methodology. Further, the proposed ranking technique is applied to a transportation problem to get a fuzzy optimal solution using MODI method.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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 ESTIMATING THE OPTIMAL INTRA-COMPANY WAGE GAPS FOR IMPROVING PRODUCTIVITY -EVIDENCE FROM JAPANESE LISTED COMPANY-<o:p></o:p></span></p> <p class="MsoNormal"><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">Motohiro</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"> Hagiwara<o:p></o:p></span></p> <p class="MsoNormal"><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 Commerce, Meiji University, Tokyo, Japan<o:p></o:p></span></p> <p class="MsoNormal"><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 examined the contribution on the productivity of the companies by the intra-company wage gaps as a measure of the performance-based wage systems and got some significant results and implications for management. In order to remove reverse effect by productivity on wage gap, instrumental variable methods are applied. Analyses on all industries implied that larger wage gaps do not motivate employees of Japanese companies. Analyses by nonlinear models to confirm the existence of the optimal wage gaps implied the existence of the optimal wage gaps in a few industries, but aggregately worst wage gaps are recognized. Concretely, effects by wage gaps on the productivity decrease, as wage gaps expand. These findings are contrary to those by <span class="SpellE">Cirillo</span> et al. (2017) in Europe, Dai et al. (2017) in China and Park et al. (2017) in Korea as most recent previous researches.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">BENCHMARK FUNCTIONS BASED PERFORMANCE EVALUATION BY INFERENCE MODEL PYRAMID TREE (PT) AND OPERATION TREE (OT)<o:p></o:p></span></p> <p class="MsoNormal"><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">Li-Chuan Lien <sup>1<span class="GramE">,4</span></sup>, <span class="SpellE">Shou</span>-Bin Chen <sup>2</sup>, Jing-Yi <span class="SpellE">Xu</span> <sup>3</sup>, Yan-Ni Liu <sup>4</sup> and <span class="SpellE">Qi-Sheng</span> Wang <sup>1</sup><o:p></o:p></span></p> <p class="MsoNormal"><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"> Chung Yuan Christian University, <span class="SpellE">Taoyuan</span>, Taiwan<o:p></o:p></span></p> <p class="MsoNormal"><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"> Fuzhou <span class="SpellE">CityConstruction</span> Design and Research Institute Co., Ltd., Fuzhou, China<o:p></o:p></span></p> <p class="MsoNormal"><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"> China Construction Fifth Engineering Branch Co., Ltd, Fuzhou, China<o:p></o:p></span></p> <p class="MsoNormal"><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">4</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"> Fujian University of Technology, Fuzhou, China<o:p></o:p></span></p> <p class="MsoNormal"><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">Structure equation model (SEM) approaches based on the historical data and perform its formula. Thus, the researcher can observed internal knowledge on problems and ensure sensitivity analysis or decision making via formula. This study used an SEM inference model approach, named pyramid tree (PT), model structure idea was from pyramid and operation trees (OTs). Four triangle layers organize a pyramid object. OT models formula based on the numerical historical data. Particle bee algorithm (PBA) optimize PT model s structure based on the structure behavior that designed by this study. In order to measure PT performance, this study evaluated PT performance based on proposed benchmark functions. This study evaluated performance of PT model s structure with fourteen 2- or 4-dimensional benchmark functions by 100, 400, 700 and 1000 of PBA iterations. Results showed PT performance was satisfied to fit those benchmark functions formula and better than single OT. PT is a self-modeling formula inference model method and more accurate than single OT. It is suitable to solve the problem while human would to figure out the formula based on the historical data such as material composite or other practical problems.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </div> </div> <p class="MsoNormal"><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">BENCHMARKING STATE ROAD TRANSPORT UNDERTAKINGS OF INDIA: A DEA-BASED STEPWISE APPROACH<o:p></o:p></span></p> <p class="MsoNormal"><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">Punita</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">Saxena</span><o:p></o:p></span></p> <p class="MsoNormal"><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">Associate Professor, Department of Mathematics, <span class="SpellE">Shaheed</span> <span class="SpellE">Rajguru</span> College of Applied Sciences for Women (University of Delhi), India<o:p></o:p></span></p> <p class="MsoNormal"><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">Benchmarking is the practice of any decision making unit to compare key metrics of its operations with the peers. It has a twofold advantage. Firstly, it allows the units to see how efficiently they are performing in comparison to others. Secondly, it allows the units to identify and analyze the areas of potential improvements to become more competitive. Data Envelopment Analysis (DEA) is a non-parametric tool used for efficiency evaluations and to yield a reference benchmark target for each inefficient unit in the data set under study. However, the benchmarks provided are at times practically infeasible to attain. The inefficient units need to strategize the best practices to be adopted in a stepwise manner so that gradually they can raise themselves up to the level of the reference target. In the present paper, a stepwise benchmarking approach has been developed by selecting intermediate targets. The transitional target units have been selected on the basis of similarity between uses of inputs, yield of outputs and peer appraisal. Initially the units were classified as efficient, marginally efficient, below average and highly inefficient units based on their DEA scores. For a highly inefficient unit, an intermediate target from the next cluster was selected using the criterion of distance between the input and output values and maverick index. In this manner a feasible efficient path has been identified to be adopted by a highly inefficient unit to grow to an efficient unit.<o:p></o:p></span></p> <div> <div> <div> <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> </div> </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> </body> </html> <!-- FILE ARCHIVED ON 15:10:17 Jul 12, 2020 AND RETRIEVED FROM THE INTERNET ARCHIVE ON 15:04:19 Feb 26, 2022. JAVASCRIPT APPENDED BY WAYBACK MACHINE, COPYRIGHT INTERNET ARCHIVE. ALL OTHER CONTENT MAY ALSO BE PROTECTED BY COPYRIGHT (17 U.S.C. 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