پديد آورنده :
خاتمي بيدگلي، محمدباقر
عنوان :
برنامه ريزي محدوديت-شانس براي مسئله مكان يابي هاب با محدوديت ظرفيت در شرايط فازي
مقطع تحصيلي :
كارشناسي ارشد
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها
صفحه شمار :
دوازده، 82ص.: مصور، جدول، نمودار
يادداشت :
ص.ع. به فارسي و انگليسي
استاد راهنما :
علي شاهنده نوك آبادي
توصيفگر ها :
تقاضاي فازي , معيار اطمينان , شبيه سازي فازي , الگوريتم ژنتيك تركيبي
تاريخ نمايه سازي :
30/1/91
استاد داور :
رضا حجازي، نادر شتاب بوشهري
تاريخ ورود اطلاعات :
1396/10/12
رشته تحصيلي :
صنايع و سيستم ها
دانشكده :
مهندسي صنايع و سيستم ها
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Chance Constrained Programming for the Capacitated Single Allocation Hub Location Problem in Fuzzy Environment Sayyed Mohammad Bagher Khatami Bidgoli m khatamibidgoli@in iut ac ir Department of Industrial and Systems Engineering Isfahan University of Technology 84156 83111 Isfahan IranDegree M Sc Language FarsiA Shahandeh Assoc Prof Supervisor E mail ali nook@cc ac irAbstractHub location problems are widely studied in the area of location theory where they involve locating the hubfacilities and designing the hub networks Typical applications of hub and spoke networks have arisen inpassenger airlines express package delivery firms message delivery networks trucking industry telecommunication system supply chain of chain stores and many other areas Hubs are special facilities thatserve as switching transshipment and sorting points in many to many distribution systems Instead ofserving each origin destination pair directly hub facilities concentrate flows in order to take advantage ofeconomies of scale Flows from the same origin with different destinations are consolidated on their route tothe hub and are combined with flows that have different origins but the same destination The consolidationis on the route from the origin to the hub and from the hub to the destination as well as between hubs Thehub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in orderto route the traffic between origin destination pairs There are two basic types of hub networks singleallocation and multiple allocation They differ in how non hub nodes are allocated to hubs In singleallocation all the incoming and outgoing traffic of every demand center is routed through a single hub inmultiple allocation each demand center can receive and send flow through more than one hub In othercategory hub location problems is categorized to hub median and hub center In the hub median problem theobjective is to minimize the total transportation cost and main objective in hub center problem main is tominimize the maximum distance or cost between o d pairs In this study we consider the capacitated singleallocation hub location problem The objective function is to minimize the total transportation cost and fixedhub locating cost Here we also consider capacity restrictions on incoming flow that must be sorted Most ofthe proposed methods in hub network are based upon the condition that all time parameters are knownexactly This is a stringent assumption which can cause difficulties in practice In fact there are manyvaguely formulated relations and imprecisely quantified physical data values in real world descriptions sinceprecise details are simply not known in advance There could be an uncertainty in a number of factors suchas flow capacity and costs Thus in these cases the solutions generated using deterministic models may notbe very accurate In this research flow between origin destination pairs is considered fuzzy parameters Weformulate this problem with fuzzy chance constrained programming and credibility measure Chance constrained programming CCP offers a powerful means of modeling stochastic decision systems withassumption that the stochastic constraints will hold at least of time where is referred to as the confidencelevel provided as an appropriate safety margin by the decision maker By applying this formulation there aretwo uncertain function objective function and hub capacity restriction A fuzzy simulation based geneticalgorithm is provided for solving the proposed model problems Fuzzy simulation is used for estimationcapacity restriction and objective function and genetic algorithm is applied for search of optimum solution To illustrate the modeling idea and the effectiveness of the proposed algorithm the fuzziness Australian Post AP data was applied The result show that with increasing in confidence level of capacity restriction thebest solution point may be changed and with increasing confidence level of objective
استاد راهنما :
علي شاهنده نوك آبادي
استاد داور :
رضا حجازي، نادر شتاب بوشهري