IJE TRANSACTIONS A: Basics Vol. 31, No. 4 (April 2018) 640-647    Article in Press

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M. Soolaki, J. Arkat and F. Ahmadizar
( Received: August 04, 2017 – Accepted in Revised Form: January 04, 2018 )

Abstract    Nowadays, we are witnessing the growth of firms that distribute the production capacity of their products to a wide geographic range to supply the demand of several markets. In this article, the relationships and interactions between cell design and supply chain design are investigated. For this purpose, a novel integrated model is presented for designing dynamic cellular manufacturing systems in supply chain design. Different components in the supply chain design, such as location of production facilities at a number of candidate sites, procurement of raw materials from suppliers, shipment of raw materials to production facilities, manufacturing of products, and distribution of products to markets are considered in dynamic environments. The costs concerning these components are minimized. Since the proposed problem is NP-hard, however, a genetic algorithm is presented for application of the model to real-sized instances. Numerical examples demonstrate that the algorithm performs successfully in searching for optimal or near-optimal solutions.


Keywords    dynamic cellular manufacturing systems, cell formation problem, supply chain design, genetic algorithm


چکیده    امروزه شاهد افزایش روزافزون بنگاه‌های‌اقتصادی هستیم که برای تأمین تقاضای چندین بازار، ظرفیت تولیدی محصولات خودرادرگستره جغرافیایی وسیعی توزیع می‌نمایند.به منظور بررسی چنین شرکت‌هایی، دراین مقاله ارتباطات وتعاملات بین طراحی سلولی تسهیلات تولیدی و طراحی زنجیره تأمین، بررسی می‌شود. برای این منظور، یک مدل یک پارچه ریاضی جدید برای طراحی سیستم‌‌های تولیدسلولی پویادرطراحی زنجیره تأمین ارائه می‌شود. مولفه‌های مختلفی از طراحی زنجیره تأمین مانندانتخاب مکان تسهیلات تولیدی ازبین تعدادی سایت کاندیدا،تهیه موادخام ازتامین کنندگان، حمل ونقل مواداولیه به تولیدکنندگان، تولیدوتوزیع محصولات به بازارهادریک محیط پویادرنظرگرفته می‌شودوهزینه‌های مربوط به این مولفه‌ها، کمینه‌سازی می‌گردد. از آن جاییکه مساله پیشنهادی ناچندجمله‌ای سخت است برای کاربرد مدل دراندازه‌های دنیای واقعی، یک الگوریتم ژنتیک ارائه می‌گردد. مثال‌های عددنشان می‌دهد که الگوریتم درجستجوی راه‌حل‌های بهینه یا نزدیک به بهینه، به گونه‌ای موفق و کارا عمل می‌کند.


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