مقایسه سه روش فراابتکاری برای کمینه نمودن زمان چرخه در مسئله زمانبندی جریان کارگاهی مختلط دوره‌ای با در نظر گرفتن اثر یادگیری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه صنایع دانشکده مهندسی دانشگاه بوعلی سینا

2 دانشجو دکتری مهندسی صنایع، دانشکده مهندسی، دانشگاه بوعلی سینا، همدان، ایران

چکیده

زمانبندی کار‌ها در صنایعی که روند حرکت کار‌ها بر روی ماشین‌ها به صورت دوره‌ای می‌باشد، همچون صنایعی که محصولات آنها فاسد شدنی نظیر صنایع غذایی و یا دارای طول عمر همانند مواد شیمیایی، رادیواکتیو و غیره هستند، از اهمیت زیادی برخوردار است، زیرا که این صنایع به دلیل محدودیت‌های زمانی و یا رقابت با سایر شرکت‌ها سعی در کمینه نمودن بازه زمانی انجام کار‌ها دارند. از آنجا که غالباً محیط تولیدی این صنایع به صورت تولید جریان کارگاهی مختلط دوره‌ای می‌باشد و اثر یادگیری اپراتور در سرعت تولید مشهود است، این پژوهش در نظر دارد که زمان چرخه بر روی هر ماشین را با وجود اثر یادگیری به ‌کمک چینش فعالیت‌ها کمینه نماید. برای این منظور در روند این پژوهش، ابتدا تحقیقات پیشین در این حوزه مورد مطالعه قرار گرفت. سپس مدل ریاضی این مسأله نوشته و به دلیل آنکه ماهیت کمینه نمودن زمان انجام کار‌ها در محیط تولید جریان کارگاهی مختلط دوره‌ای، جزء مسائل سخت (NP-Hard) می‌باشد، برای حل این مسأله از سه روش فراابتکاری الگوریتم ژنتیک، الگوریتم شبیه‌سازی تبرید و الگوریتم شبیه‌سازی تبرید مبتنی بر جمعیت استفاده شد. نتایج نشان می‌دهند که الگوریتم شبیه‌سازی تبرید مبتنی بر جمعیت به دلیل ساختار جمعیتی آن، به‌طور میانگین نسبت به دو الگوریتم دیگر کارایی بهتری دارد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison between Three Metaheuristic Algorithms for Minimizing Cycle Time in Cyclic Hybrid Flow Shop Scheduling with Learning Effect

چکیده [English]

Jobs scheduling in industries with cyclic procedure on machines, such as perishable products (food industries) or products with a limited lifetime (chemicals, radio actives, etc), is very important. Due to time limitation or competition with other companies, these industries try to minimize thecycle time of jobs processing. Since most productive environments of the industries are cyclic hybrid flow shop and operator’s learning effect is obvious in speed of productions, the aim of this study is to minimize cycle time of each machine with learning effect by consequence of jobs. After proposing a mathematical model and since the cyclic hybrid flow shop environment is NP-hard, three metaheuristics, i.e., genetic algorithm, simulated annealing algorithm and population based simulated annealing algorithm, have been proposed for solving this problem. Results show that on average, population based simulated annealing algorithm due to its population-based structure has a better performance in comparison to other algorithms.

کلیدواژه‌ها [English]

  • Scheduling
  • Hybrid flow shop
  • Learning effect
  • Metaheuristic algorithm

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