هموار سازي توليد در تعيين اندازه دسته و زمان بندي توليد به صورت همزمان

چكيده انگليسي :

93 Production Smoothing in General Lot Sizing and Scheduling Problem Nasibe Salari n salari@in iut ac ir Date of Submission 2211 24 26 Department of Industrial Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language FarsiSupervisor Dr Mehdi Bijeri bijari@cc iut ac irAbstract Production smoothing process has a significant role in reducing the costs and efficient meeting of thecustomers demand for a wide variety of products so that an intense research attention has been focused onthis topic In this study the production smoothing has been considered in general lot sizing and schedulingproblem GLSP in presence of non zero setup and processing times and setup costs which also vary amongthe products in single machine system The GLSP deals with the determining lot size of several products andtheir simultaneously scheduling on single machine to satisfy deterministic and dynamic demand over a finitehorizon plan The developed model in this research employs following dual objectives first minimizing theproduction costs including sequence dependent setup costs and holding inventory costs and secondminimizing the sum squared variation of the ideal production rate Since now the smoothing process has notbeen utilized in literature of the GLSP The two mathematical models have been presented based on Clarkand also Fleichman Meyer models for this problem and then the efficiency of these models has beendiscussed Considering the results of the models we could say that the Clark model shows the higherperformance in compare with Fleichman Meyer model Also the Clark model is able to search within muchwider solution space than Fleichman Meyer model in the same time and find a better solution We employedthe sum weighted method and constraint method for solving the mathematical model in a small scales Alsowe propose a genetic local search algorithm that some of its features were the preservation of dispersion inthe population elitism and utilizing of a parallel multi objective local search in order to intensify the searchin discrete regions The concept of Pareto dominance is used to assign the fitness to the solutions and in thelocal search procedure Two value parameter sets were used for genetic algorithm that the first set wassimilar to the Arroyo and Armentano research and second set was selected among the best results ofexecuting genetic algorithm with different parameter values Computational results show that in the smallscales the second parameter set has a more effective solution cases than the first one In the small scales including 4 jobs and 4 macro periods the best obtained results are the combination of the results obtainedfrom constraint method and the sum weighted method The performance of this comminuted result has beencompared with the performance of genetic algorithm method Computational results show that the Paretooptimal set which obtained by genetic local search algorithm is more effective than the solutions whichobtained by solving the mathematical model with a shorter computational time Considering thecomputational time of approximately 5222 seconds for solving a small scale problem by mathematicalmodel so it is not logical to use this model for solving a large scales production smoothing in GLSP e g including 42 jobs and 42 macro periods As a result the efficient solutions were obtained only by executinggenetic local search algorithm Genetic algorithm is executing by two parameter sets the Pareto frontiers thatwere constructed at the end of 12 32 52 82 and 122 iterations were compared together Computationalresults show that genetic local search algorithm has been improved the quality of solution in previousiterations and has been enhanced diversity of solutions in last iterations Finite Pareto frontier is constructedby combining the results of first and second parameter sets in last iteration Keywords Scheduling Lot sizing Production Smoothing GLSP Genetic Local Search

سالاري، نسيبه

هموار سازي توليد در تعيين اندازه دسته و زمان بندي توليد به صورت همزمان

مدل GLSP , الگوريتم ژنتيك همراه با جستجوي محلي