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Integrated Process Planning and Active Scheduling in a Supply Chain-A Learnable Architecture Approach | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| مقاله 7، دوره 12، شماره 2، تیر 2019، صفحه 307-333 اصل مقاله (866.47 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2019.255363.673086 | ||
| نویسندگان | ||
| Esmaeel Moradi1؛ Ashkan Ayough2؛ Mostafa Zandieh* 2 | ||
| 1School of Industrial Engineering and Management, Oklahoma State University, Stillwater, USA | ||
| 2Department of Industrial Management, Management and Accounting Faculty, Shahid Beheshti University, Tehran, Iran | ||
| چکیده | ||
| Through the lens of supply chain management, integrating process planning decisions and scheduling plans becomes an issue of great challenge and importance. Dealing with the problem paves the way to devising operation schedules with minimum makespan; considering the flexible process sequences, it can be viewed as a fundamental tool for achieving the scheme, too. To deal with this integration, the modeling approach to problem with MIP structure is common in the literature. These models take precedence constraints into consideration to select machines and to determine sequences. In order to obtain viable sequences, we employed a proposed transformation matrix (TM). We also took advantage of an evolutionary search, called Learnable genetic Architecture (LEGA). Based on LEGA, we developed an integrated process planning and scheduling learnable genetic algorithm (IPPSLEGA). Our approach was evaluated with problems with various sizes. The experimental results show that our proposed architecture outperforms prior approaches, or it performs, at least, as efficiently as they do. | ||
| کلیدواژه‌ها | ||
| Supply Chain Management؛ Process planning؛ Scheduling؛ Transformation matrix؛ Evolutionary search | ||
| عنوان مقاله [English] | ||
| برنامه ریزی فرایند و زمان بندی فعال یکپارچه در زنجیره تامین- رویکرد معماری قابل یادگیری | ||
| نویسندگان [English] | ||
| اسماعیل مرادی1؛ اشکان عیوق2؛ مصطفی زندیه2 | ||
| 1مدرسه مهندسی صنایع و مدیریت، دانشگاه ایالتی اوکلاهوما، استیلواتر، ایالات متحده آمریکا | ||
| 2گروه مدیریت صنایع، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران | ||
| چکیده [English] | ||
| از منظر مدیریت زنجیره تامین، یکپارچه سازی تصمیمات برنامه ریزی فرایند و برنامه های زمان بندی موضوعی چالش برانگیز و حائز اهمیت به شمار می آید. حل این مساله یکپارچه، زمینه دست یابی به زمان بندی های عملیات با کمترین زمان تکمیل کارها را فراهم می آورد. مدلسازی با ساختار برنامه ریزی عدد صحیح، رویکردی متداول در ادبیات موضوع به منظور حصول چنین یکپارچگی ای است. این مدل ها محدودیت های پیش نیازی را برای انتخاب ماشین ها و تعیین توالی کارها مورد ملاحظه قرار می دهند. برای دست یابی به توالی های موجه، در این تحقیق یک ماتریس تبدیل(TM) پیشنهاد شده است. همچنین از یک روش جستجوی تکاملی با عنوان معماری ژنتیک قابل یادگیری(LEGA) استفاده شده است. بر اساس LEGA یک الگوریتم ژنتیک با قابلیت یادگیری برای زمان بندی و برنامه ریزی فرایند به صورت یکپارچه(IPPSLEGA) توسعه داده شده است. رویکرد این تحقیق روی مسائل با اندازه های متنوع ارزیابی شده است. نتایج محاسبات نشان می دهد که معماری پیشنهادی یا عملکرد بهتری از رویکردهای پیشین داشته یا دست کم کارائی یکسانی ارائه می دهد. | ||
| کلیدواژه‌ها [English] | ||
| مدیریت زنجیره تامین, برنامه ریزی فرایند, زمان بندی, ماتریس تبدیل, جستجوی تکاملی | ||
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