Designing a flexible multi-commodity distribution network in a multi-level supply chain. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Sadeghi, Kosar; Nookabadi, Ali S.
• Is Part Of: International journal of management science and engineering management. Volume 12:Issue 3(2017)
• Page Start: 155
• Page End: 164
• Publication Date: 2017-07-03
• Subjects: Supply chain management -- optimization -- particle swarm optimization -- network; C44 -- C52 -- C61; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2016.1204959
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract: In response to the market competitive conditions today, organizations are increasingly striving to deliver products to customers in the correct volumes, at the right time, and in the right place. An important success factor in this competitive environment is employing an appropriate and efficient distribution network that is designed around the objectives of optimum numbers, locations, and capacities of production and distribution centers and an optimum strategy for the timely transfer of products from the plant or the warehouse to the customer.The present study aims to investigate the problem of designing a flexible distribution network in a multi-level supply chain by assuming direct shipment from the manufacturer to the customer. The problem is considered as a multi-commodity, static, and certain model in the form of mixed integer programming, with each of the production and distribution facilities having multi-capacity levels and their numbers considered as variables in the model. The restriction of distribution radius for production plant and distribution centers (warehouses) has been considered. Since the distribution network design problem is an NP-hard problem, a meta-heuristic approach based on the particle swarm optimization method was adopted to solve the problem. To evaluate the performance of the proposed approach, 32 test problems were solved. Comparison of the results indicates the efficiency of the proposed method.