Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty
International Journal of Mechanical Engineering and Applications
Volume 5, Issue 1, February 2017, Pages: 28-40
Received: Sep. 4, 2016;
Accepted: Sep. 13, 2016;
Published: Feb. 17, 2017
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M. S. Al-Ashhab, Design & Production Engineering Dept., Faculty of Engineering, Ain-Shams University, Cairo, Egypt; Dept. of Mechanical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, Kingdom Saudi Arabia
This research is a development of a stochastic mixed integer linear programming (SMILP) model considering stochastic customer demand, to tackle the multi-product SCND problems. It also considers multi-period, multi-echelons, products inventories, considering locations capacities and associated cost elements. The model represents both location and allocation decisions of the supply chain which maximize the total expected profit. The effect of demand mean on the total expected profit and the effect of the number of scenarios on the CPU time are studied. The results have shown the effect of customers’ demands for each product in each period on the quantities of material delivered from each supplier to each factory, the quantities of products delivered from each factory and factory store to each distributor, the inventory of each product in each factory and distributor, the quantities of each type of product delivered from each distributor to each customer in each period. The model has been verified through a detailed example.
M. S. Al-Ashhab,
Supply Chain Network Design Optimization Model for Multi-period Multi-product Under Uncertainty, International Journal of Mechanical Engineering and Applications.
Vol. 5, No. 1,
2017, pp. 28-40.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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