برنامه ریزی تولید چندمرحله ای در زنجیره تأمین حلقه بسته همراه با راه اندازی های وابسته به توالی و انتقال راه‌اندازی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 هیات علمی دانشکده مهندسی صنایع دانشگاه صنعتی امیرکبیر

2 دانشکده مهندسی صنایع-دانشگاه صنعتی امیرکبیر

چکیده

در این مقاله مسأله­ی برنامه­ریزی تولید چند مرحله­ای، چند محصولی، چند پریودی با راه اندازی‌های وابسته به توالی در زنجیره تأمین حلقه بسته مطالعه می‎‏شود. فرآیندهای تولید و تولید مجدد هر محصول به­طور متوالی درنظرگرفته شده­اند و اگر ماشین برای پردازش محصول موردنظر آماده باشد، هر دو فرآیند قابل اجرا هستند. برای فرمول­بندی مسأله  یک مدل برنامه­ریزی عدد صحیح مختلط ارایه شده  و به­منظور حل مدل مذکور چهار الگوریتم ابتکاری با استفاده از رویکرد افق متحرک و یک الگوریتم ژنتیک توسعه داده شده است. دو روش ابتکاری اول برمبنای مدل­ اصلی توسعه­ی یافته‏اند، اما به­منظور حل مسأله در ابعاد بزرگ، دو روش ابتکاری دیگر و الگوریتم ژنتیک، مبتنی بر مدل ساده­سازی شده می­باشند که از حذف توالی‏های غیرترتیبی فضای جواب مدل­ اصلی حاصل شده است. جهت تنظیم پارامترهای الگوریتم ژنتیک ارایه شده، روش تاگوچی به‏کارگرفته شده است. نتایج عددی نشان‏دهنده‏ی کارایی الگوریتم فراابتکاری ارایه‏شده نسبت به الگوریتم‏های ابتکاری مبتنی‏بر برنامه‏ریزی عددصحیح مختلط هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Multi-Stage Production Planning with Sequence-Dependent Setups and Setup Carry Over In Closed-Loop Supply Chain

نویسنده [English]

  • S. Torkaman 2
2 Department of Industrial Engineering,Amirkabir University of Technology
چکیده [English]

This paper studies multi-stage, multi-product, multi-period production planning problem with sequence dependent setups in closed-loop supply chain. Manufacturing and remanufacturing processes of each product are regarded consequently, and both of them could be performed if machine is ready for processing corresponding product. To formulate the problem, a mixed-integer programming (MIP) model is presented and four heuristic algorithms using rolling horizon and a genetic algorithm are developed to solve the model. First two heuristic algorithms are developed based on the original model, but to solve the large instances the other two heuristics and the genetic algorithm are based on the simplified model, which is obtained by elimination of non-permutation sequences of original model solution space. To calibrate the parameters of the proposed genetic algorithm, Taguchi method is applied. The numerical results indicate the efficiency of the proposed meta-heuristic algorithm against MIP-based heuristic algorithms.     

کلیدواژه‌ها [English]

  • Production planning Closed-loop supply chain Sequence dependent setup Setup carry over Rolling horizon
  • Flow shop Genetic Algorithm

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