نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران.

10.22105/dmor.2022.310377.1504

چکیده

هدف: در این مقاله، پس از به دست آوردن مدل یادگیری تقویتی زمانبندی با در نظر گرفتن نت پیشگویانه، چندین رویکرد ابتکاری برای ارزیابی مدل مطرح شده است. برای اینکه یک مدل یادگیری تقویتی آموزش داده شود، باید تابع پاداش و زیان آن با توجه به شرایط محیط کارگاه، تعیین شود.
روش‌شناسی پژوهش: این مدل یادگیری در حالت های مختلف ورود کار به کارگاه مورد بررسی قرار گرفته است.
یافته‎ها: نتایج به دست آمده از روش های دیگر زمانبندی، خروجی های بهتری را از خود نشان می‌دهد. مدل نت پیشگویانه، با چهار روش مدل سازی یادگیری مورد ارزیابی و کیفیت مدل ها مورد بررسی قرار می گیرد. با انتخاب و اضافه کردن بهترین مدل خرابی ماشین به مدل یادگیری تقویتی زمانبندی، کارهای بلادرنگ وارد شده به کارگاه، به ماشین ها تخصیص داده می شوند. با مقایسه روش مطرح شده و روش های پیشین مشخص شد که بهترین عملکرد را از خود نشان داده است.
اصالت/ارزش افزوده علمی: یکی از نوآوری های مقاله ارائه تعریف تابع پاداش برای مسئله مورد نظر می باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Online Job Shop Scheduling and Dynamic Predictive Maintenance by Machine Learning

نویسندگان [English]

  • Haed Tavakkoli-Moghaddam
  • Seyed Hesamoddin Zegordi
  • Mohammad Reza Amin-Nasseri

Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

چکیده [English]

Purpose: This paper proposes several innovative approaches to model evaluation after obtaining the reinforcement learning model of scheduling with predictive maintenance. To train this model, its reward and loss function must be determined according to the conditions of the workshop environment.
Methodology: This learning model is examined in different modes of work entry into the workshop and the results obtained from other scheduling methods show better outputs.
Findings: The predictive maintenance model is evaluated by four learning methods and the quality of these models is examined. By selecting and adding the best machine failure model to the scheduling reinforcement learning model, the instant tasks entered into the workshop are assigned to the machines. By comparing the proposed method with the previous ones, the best performance is found and shown.
Originality/Value: One of the innovations of this paper is to provide a definition of the reward function for the issue.

کلیدواژه‌ها [English]

  • Real-time scheduling
  • Predictive Maintenance
  • Machine Learning
  • Reinforcement Learning
  • Data Mining
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