A genetic algorithm for supply chain configuration with new product development. (November 2016)

• Record Type: Journal Article
• Authors: Alizadeh Afrouzy, Zahra; Nasseri, Seyed Hadi; Mahdavi, Iraj
• Is Part Of: Computers & industrial engineering. Volume 101(2016)
• Page Start: 440
• Page End: 454
• Publication Date: 2016-11
• Subjects: Supply chain -- New product development -- Priority based genetic algorithm -- Fuzzy logic controller; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.09.008
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Designing a multi-echelon multi-product multi-period supply chain model. Considering new product development effects in supply chain configuration. Developing a priority based genetic algorithm to find the suitable solution at reasonable time. Abstract: New product development has become increasingly important recently due to highly competitive market place and economic reasons. Development and production of new products in the planning horizon require an efficient and responsiveness supply chain network. As new products appear in the market, the old products could become obsolete, and then phased out. A generously persuasive parameter for new product and developed product problems in a supply chain is the time which the developed products are introduced and the old products are phased out and also the time new products are introduced in the planning horizon in order to maximum the total profit. With consideration of the factors noted above, this study proposes to design a multi echelon multi product multi period supply chain model which incorporates product development and new product production and their effects on supply chain configuration. In terms of the solution technique, to overcome NP-hardness of the proposed model, priority based genetic algorithm is applied to find the suitable time for introducing developed and new product in the planning horizon, production schedule and design of supply chain network in order to maximum the total profit in a reasonable computational time. The accuracy of the proposed genetic algorithm is validated on small, medium and large instances that have been solved using the software LINGO, in order to evaluate the performance of the algorithm. Then, the implementation of the fuzzy crossover and mutation controllers is described. It is able to regulate the rates of crossover and mutation operators during the search process. Finally, a comparison is done on conventional GA and the controlled GA.