زمان‌بندی تک‌ماشینه با الگوریتم فراابتکاری کرم شب‌تاب و پیش‌بینی خرابی ماشین با رویکرد داده‌کاوی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه سمنان، سمنان، ایران

2 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشکده مهندسی، دانشگاه سمنان، سمنان، ایران

چکیده

یکی از مشکلات امروزه در مسائل صنعت، پیش‌بینی اتفاقات غیرمترقبه است. ما در این مقاله، به مسأله زمان‌بندی تک‌ماشینه با درنظر گرفتن اتفاق خرابی­ها می‌پردازیم. همچنین، به‌دنبال حداقل کردن جریمه­های تحویل زودتر از موعد و دیرتر از موعد هستیم. در این پژوهش، یک مدل ریاضی برای این مسأله ارائه شده که درآن زمان­های پردازش، بیار بودن ماشین، زمان‌ آزادسازی، زمان خرابی و همچنین زمان در دسترس قرار گرفتن ماشین بعد از نگهداری و تعمیرات لحاظ شده است. زمان خرابی­ها بااستفاده از الگوریتم­های یادگیری ماشین پیش‌بینی شده­اند. نتایج نشان می­دهد که مدل ارائه شده برای ابعاد کوچک با پارامترهای مدنظر مناسب است. اما برای حل مسأله با ابعاد بزرگ‌تر، از الگوریتم فراابتکاری استفاده شده است. این تحقیق در دو بخش به این مسأله پرداخته: بخش اول مربوط‌به پیش‌بینی خرابی­ها می­شود و بخش دوم، توالی عملیات زمان‌بندی تک‌ماشینه را دربر دارد. ابتدا، بااستفاده از داده­های گذشته، خرابی­های ماشین‌آلات توسط الگوریتم­های یادگیری ماشین پیش‌بینی شده و به مجموعه­ای از قواعد برای اصلاح فرآیند رسیده­ایم. سپس زمان‌بندی تک‌ماشینه، بااستفاده از مدل برنامه­ریزی عددصحیح مختلط و با درنظر گرفتن این خرابی­ها، انجام شده است.

کلیدواژه‌ها

عنوان مقاله [English]

Single Machine Scheduling with Firefly Algorithm and Machine Failure Prediction with Data Mining Approach

نویسندگان [English]

  • Reza Kamranrad 1
  • Ali Ghorbani 2
  • Yousef Rabbani 1
  • Peyman Falsafi 2
1 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, Iran
2 M. A. student in Industrial Engineering, Faculty of Engineering, Semnan University, Semnan, Iran
چکیده [English]

One of the problems in the industry is the forecast of unexpected events, and in this article we consider the single machine scheduling problem with considering to machine failure, and also looking for the minimizing tardiness and earliness penalties. In this research, a mathematical model for this problem is presented in which the processing times, idle time, release time and failure time as well as the availability time of the machine after repairs and maintenance are taken into article that failure times are predicted using machine learning algorithms. The results show that the proposed model is suitable for small dimensions with the desired parameters and to solve this problem in larger dimensions, the meta-heuristic algorithm has been used in this research. This research presents this problem in two parts: the first part is related to failure prediction and the second part is the sequence of single machine scheduling operations. In the first part, after receiving the previous data, we predict machine failures using machine learning algorithms and achieve a set of rules for process modification, and in the second part, we use the number programming model. Correctly mixed and considering these breakdowns and lack of access to machines in a single machine schedule.

کلیدواژه‌ها [English]

  • Machine Failure
  • Single Machine Scheduling
  • Data Mining
  • Just in Time

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نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 9، شماره 19
اسفند 1400
صفحه 189-203

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