الگوریتم ژنتیک شبیه‌سازی مبنا برای حل مسئله زمان‌بندی جریان کارگاهی با درنظرگرفتن هزینه انرژی تحت ‌شرایط عدم‌قطعیت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد، دانشگاه بوعلی سینا.

2 دانشیار، دانشگاه بوعلی سینا.

10.52547/jimp.10.2.9

چکیده

یک مسئله جریان کارگاهی با اهداف حداقل‌سازی زمان تکمیل و هزینه انرژی بررسی شده است. کاهش هزینه‌های تولید از اهدافی است که صنایع همواره در نظر دارند. بالا­رفتن آگاهی عمومی نسبت به مسئله انرژی باعث ایجاد نگرشی جدید در راستای کاهش هزینه انرژی شده است. برای نزدیک‌­ترشدن مسئله به دنیای واقعی، مسئله تحت عدم‌قطعیت بررسی شده است. شکاف پژوهشی موجود الهام‌­بخش پژوهش بوده است. فرض شده که ماشین‌ها می‌توانند از سه سرعت آهسته، نرمال و سریع برای پردازش کارها استفاده کنند. در سرعت بالا میزان مصرف افزایش یافته و زمان تکمیل کاهش می‌یابد و برعکس. این تفاوت در سرعت به ایجاد مقادیر متفاوت و متضاد در تابع اهداف منجر می‌شود؛ بنابراین باید راهکاری پیشنهاد شود که علاوه بر ترتیب کار، سرعت دستگاه‌ها به­عنوان متغیر تصمیم به­صورت بهینه مشخص شوند. یک مدل ریاضی ارائه شده و سپس از الگوریتم ژنتیک مبتنی برشبیه‌­سازی برای حل مسئله در ابعاد بزرگ استفاده شده است. به­ازای هربار ارزیابی تابع هدف در الگوریتم از شبیه­‌سازی استفاده شده است تا عدم­‌قطعیت موجود در پارامتر زمان پردازش درنظر گرفته شود. با توجه به تصادفی­‌بودن زمان پردازش، از مدل ارزش انتظاری برای مقابله با عدم­قطعیت استفاده شده است. نتایج محاسباتی نشان می‌دهد که الگوریتم و رویکرد حل پیشنهادی، عملکرد خوبی دارند.

کلیدواژه‌ها


عنوان مقاله [English]

A Simulation Based Genetic Algorithm for Flowshop Scheduling Problem Considering Energy Cost under Uncertainty

نویسندگان [English]

  • Mina Faraji Amiri 1
  • Javad Behnamian 2
1 M.Sc., Bu-Ali Sina University.
2 Associate Professor, Bu-Ali Sina University.
چکیده [English]

A flowshop problem with objective functions of minimizing makespan and energy cost has been investigated. Reducing production costs is one of the goals that industries always have in mind. Increasing public awareness about the energy issues creates a new attitude toward minimizing energy costs. In order to make the problem more compatible with the real-world conditions, the problem is considered under uncertainty. An existing research gap inspired this study. It is assumed that machines can use the three slow, normal and fast speeds to process jobs. At high speeds, consumption rate increases and completion time decreases, and vice versa. The difference in machine processing speeds yields different and contradictory values in the objective functions. Therefore, a method should be proposed in which, in addition to the order of jobs, the speed of machines could be determined. A mathematical model is presented, and then a simulation-based genetic algorithm is used to solve the problem on a large scale. Simulation is used for each evaluation of the objective function in the genetic algorithm to consider the uncertainty of processing times. Due to the stochastic processing time, the expected value model is used to deal with uncertainty. The computational results indicate that the algorithm and approach show a good performance.

کلیدواژه‌ها [English]

  • Green Scheduling
  • Stochastic Scheduling
  • Flowshop Problem
  • Simulation Based Genetic Algorithm
  • Uncertainty
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