شماره مدرك :
3154
شماره راهنما :
2886
پديد آورنده :
عتيقه چيان، آرزو
عنوان :

ارائه مدل و روشي براي زمانبندي خطوط فولادي- ريخته گري پيوسته

مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
﴿صنايع﴾
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها
سال دفاع :
1384
صفحه شمار :
ده، 146، [II]ص.: مصور، جدول، عكس، نمودار
استاد راهنما :
مهدي بيجاري
استاد مشاور :
قاسم مصلحي
توصيفگر ها :
تعاملات انسان- ماشين , روشهاي هوش مصنوعي , هارجانكوسكي و گراس من، تانگ و همكاران
استاد داور :
رضا حجازي
تاريخ ورود اطلاعات :
1396/09/05
كتابنامه :
كتابنامه
رشته تحصيلي :
صنايع و سيستم ها
دانشكده :
مهندسي صنايع و سيستم ها
كد ايرانداك :
ID2886
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
147AbstractSteel making continuous casting scheduling problem is in the class of hybrid flow shopscheduling problems with some extra constraints which make it more complicated Sinceclassic optimization methods fail to obtain optimal solution for this problem this thesisproposes two approaches to find sufficiently good solutions The first approach uses Genetic algorithm GA with a good initial population generatedthrough a set of good heuristic rules The second approach Hybrid Ant Colony and Classic Optimization methods HACCO uses the advantages of meta heuristic algorithms and classic optimization methodssimultaneously HACCO consists of two phases job sequencing and job scheduling Thefirst phase is done via ant colony optimization algorithm ACO The output of this phaseis a feasible sequence of the jobs Based on the obtained sequencing in the second phasethe job scheduling is determined through a non linear optimization algorithm Concurrent use of ACO and non linear optimization methods use the efficient heuristicinformation to guide the ACO search and preparing the good initial solution for solving thenon linear model of the second phase are the main characteristics of the HACCO The efficiency of these approaches is evaluated in comparison with a heuristic algorithmwhich is used in Mobarakeh steel company of Isfahan In addition the proposedapproaches are compared through solving several instances Numerical results indicate theefficiency of the proposed approaches in comparison with the mentioned heuristicalgorithm Furthermore the efficiency of HACCO in comparison with GA is shown HACCO has better performance in more than 95 of the instances and have similarperformance in remained instances
استاد راهنما :
مهدي بيجاري
استاد مشاور :
قاسم مصلحي
استاد داور :
رضا حجازي
لينک به اين مدرک :

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