ارائه مدلی داده‌محور برای زمانبندی و تعیین توالی وظایف درسیستم‌های تولید منطبق بر صنعت 4.0 با جریان کارگاهی
محورهای موضوعی : صنعتی
دانیال حاتمی
1
,
علیرضا ایرج پور
2
,
رضا احتشام راثی
3
1 - دانشجوی دکتری، گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
2 - گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
3 - گروه مدیریت صنعتی، واحد قزوین، دانشگاه آزاد اسلامی، قزوین، ایران
کلید واژه: توالی وظایف, جریان کارگاهی , زمانبندی, صنعت 4.0, مدل داده‌محور ,
چکیده مقاله :
مساله زمانبندی و توالی جریان کارگاهی همواره مساله مهمی در تمامی صنایع و کارخانه‌ها می‌باشد که با ظهور الگو‌های مختلف تغییرات اساسی در آن رخ می‌دهد. این مقاله تلاش می‌کند مساله زمانبندی و تعیین توالی وظایف در سیستم‌های تولید با جریان کارگاهی را متناسب با مولفه‌های انقلاب صنعتی چهارم مورد بررسی و تحلیل قرار دهد. در ابتدا یک مدل برنامه‌ریزی ریاضی چند هدفه داده‌محور ارائه شد که به دنبال کمینه کردن زمان ساخت، تاخیر کلی و مصرف انرژی کلی می‌باشد. سپس، برای مقابله با عدم قطعیت، در این مطالعه از یک رویکرد بهینه‌سازی استوار داده‌محور برای نخستین بار در مسائل جریان کارگاهی استفاده شده است. پارامترهای مهم مدل با استفاده از الگوریتم‌های SARIMA و SVR برآورد شده و سپس مدل مساله با استفاده از چند الگوریتم فراابتکاری ترکیبی حل گردید. نوآوری اصلی این مقاله ارائه رویکرد بهینه‌سازی داده‌محور استوار و استفاده از الگوریتم SVR در برآورد پارامتر و بررسی تاثیر مولفه‌های انقلاب صنعتی چهارم بر بهینه‌سازی جریان کارگاهی می‌باشد. نتایج نشان داد که LP-GA-SA توسعه یافته بهترین عملکرد را بر اساس معیار کیفیت پاسخ‌ها در مسائل آزمایشی با اندازه‌های کوچک و بزرگ دارد. دو مساله اصلی که صنعت 4.0 بر جریان کارگاهی تاثیر مستقیم می‌گذارد ضریب یادگیری و نرخ زوال‌پذیری می‌باشد که طبق تحلیل حساسیت مشاهده می‌شود افزایش ضریب یادگیری که به دلیل استفاده از فناوری‌های صنعت 4.0 حاصل می‌شود موجب بهبود تمامی توابع هدف می‌شود. هم‌چنین اثر زوال‌پذیری را نیز حداقل کرده که مجددا موجب بهبود توابع هدف می‌گردد.
The issue of scheduling flow shop is always an important issue in all industries and factories, which undergoes fundamental changes with the emergence of different paradigms. This article aimed to analyze the problem of scheduling and determining the sequence of tasks in production systems with flow shop according to the components of the industry4.0. For this purpose, a data-driven model and its integration with meta-heuristic hybrid algorithms are presented to solve the problem. In the first step the problem model is designed and to deal with uncertainty the data-driven robust optimization approach has been used for the first time in flow shop problems. The important parameters of the model were estimated using SARIMA and SVR algorithms, and then the problem model was solved using hybrid algorithms, and the findings showed that LP-GA-SA algorithm has the best performance. The main innovation of this article is to present a data-driven optimization approach and use the SVR algorithm in parameter estimation and investigate the impact of industry4.0 on flow shop optimization. The findings show that the use of robotics and AI from Industry 4.0 in the flow shop will improve the execution time and costs in the long run. The two main issues that Industry 4.0 directly affects the workshop flow are the learning coefficient and the deterioration rate. The increase in the learning coefficient that is obtained due to the use of Industry 4.0 technologies improves all the target functions. It also minimizes the deterioration effect, which again improves the target functions.
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