7658

7130

كارشناسي ارشد

صنايع

اصفهان: دانشگاه صنعتي اصفهان، دانشكده صنايع و سيستم ها

1391

دوازده،85ص.: مصور،جدول،نمودار

ص.ع.به فارسي و انگليسي

مهدي بيجاري

28/1/92

قاسم مصلحي، فريماه مخاطب رفيعي

مهندسي صنايع و سيستم ها

ID7130

به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي

Reentrant Flexible Job Shop Scheduling Problem With Parallel Batch Processing Machines considering Non Identical Job Size Omid Rajabi o rajabi@in iut ac ir Mehdi Bijari Supervisor bijari@cc iut ac ir Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Persian Date 2013 01 07 Abstract Scheduling and sequencing are important problems in production planning and have many applications in manufacturing and service companies Flexible Job shop Scheduling Problem FJSP with parallel batch processing machines caused increasing production rate decrease bottleneck improve system performance and also reduce the volume of capital investment Such as applications of this problem are integrated circuits and steel pieces factories Many studies have been achieved on FJSP with different objectives function but until now don t have been observed about Reentrant Flexible Job shop Scheduling Problem RFJSP with parallel batch processing machines considering non identical job size with Total Weighted Tardiness TWT In this study 1 problem has been researched In this thesis has been developed a Mixed Integer Linear Programming MILP for single batch processing machine i e Then a MILP model has developed for RFJSP i e By using these models two MILP have been developed for problem in case of all of work station machines are batch processing and also case of some station machines are batch processing machines and other are discrete machines This problem is strongly NP hard so two Meta heuristic Genetic Algorithm GA and Simulated Annealing SA developed Finally to analyse of Meta heuristic methods efficiency 144 instance problems with different difficulty generated randomly Instance problems classified in two large and small scales to evaluate efficiency of solution methods Solution obtains from algorithms compared with solution obtains from mathematical model by GAMS software in 36000 seconds limit time in small scales In large scales solution methods efficiency evaluate as solution quality and time perspective Computational result in small scale showed that SA and GA solution just in 9 instances from 72 worse than mathematical model solution Furthermore GAMS software solved 39 instances optimally while SA and GA find 43 instances optimally Also SA and GA average solution time in comparison with GAMS average solution time is so low in small scale Computational result in large scale showed SA is more efficient than GA Keywords Reentrant flexible job shop Parallel machines Batch processing Total weighted tardiness Genetic algorithm Simulated annealing Non identical sizePDF created with pdfFactory trial version www pdffactory com

مهدي بيجاري

قاسم مصلحي، فريماه مخاطب رفيعي