دانلود مقاله ISI انگلیسی شماره 109481
ترجمه فارسی عنوان مقاله

بهینه سازی خط مشی های ایجاد شده به سهام از طریق یک مدل بزرگ اندازه بزرگ

عنوان انگلیسی
Optimizing make-to-stock policies through a robust lot-sizing model
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
109481 2018 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Volume 200, June 2018, Pages 302-310

ترجمه کلمات کلیدی
لات اندازه، ساخت به سهام، بهینه سازی قوی، برنامه ریزی خطی مختلط عدد صحیح،
کلمات کلیدی انگلیسی
Lot-sizing; Make-To-Stock; Robust optimization; Mixed-integer linear programming;
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی خط مشی های ایجاد شده به سهام از طریق یک مدل بزرگ اندازه بزرگ

چکیده انگلیسی

In this paper we consider a practical lot-sizing problem faced by an industrial company. The company plans the production for a set of products following a Make-To-Order policy. When the productive capacity is not fully used, the remaining capacity is devoted to the production of those products whose orders are typically quite below the established minimum production level. For these products the company follows a Make-To-Stock (MTS) policy since part of the production is to fulfill future estimated orders. This yields a particular lot-sizing problem aiming to decide which products should be produced and the corresponding batch sizes. These lot-sizing problems typically face uncertain demands, which we address here through the lens of robust optimization. First we provide a mixed integer formulation assuming the future demands are deterministic and we tighten the model with valid inequalities. Then, in order to account for uncertainty of the demands, we propose a robust approach where demands are assumed to belong to given intervals and the number of deviations to the nominal estimated value is limited. As the number of products can be large and some instances may not be solved to optimality, we propose two heuristics. Computational tests are conducted on a set of instances generated from real data provided by our industrial partner. The heuristics proposed are fast and provide good quality solutions for the tested instances. Moreover, since they are based on the mathematical model and use simple strategies to reduce the instances size, these heuristics could be extended to solve other multi-item lot-sizing problems where demands are uncertain.