A genetic algorithm for supply chain configuration with new product development
A genetic algorithm for supply chain configuration with new product development
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DOI：
10.1016/j.cie.2016.09.008
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发表时间：
2016-11-01
影响因子：
7.9
通讯作者：
Mahdavi, Iraj
中科院分区：
文献类型：
--
作者：
Afrouzy, Zahra Alizadeh;Nasseri, Seyed Hadi;Mahdavi, Iraj
近年来，由于市场竞争激烈和经济原因，新产品开发变得越来越重要。在计划范围内开发和生产新产品需要一个高效和快速响应的供应链网络。随着新产品的出现，旧产品可能会过时，然后被淘汰。供应链中新产品和已开发产品问题的一个有说服力的参数是已开发产品的引入时间和旧产品的淘汰时间，以及在计划期内新产品的引入时间，以使总利润最大化。本文提出了一个多产品多阶段供应链模型的设计，该模型包括产品开发和新产品生产，以及它们对供应链配置的影响。技术，为了克服所提出的模型的NP-困难，基于优先级的遗传算法被应用于寻找合适的时间引入开发和新产品在计划的水平，生产调度和供应链网络设计，以最大限度地提高总利润在一个合理的计算时间。通过LINGO软件求解的小、中、大型算例验证了所提遗传算法的准确性，以评价算法的性能。然后，模糊交叉和变异控制器的实现进行了说明。它能够在搜索过程中调节交叉和变异算子的速率。最后，对常规遗传算法和控制遗传算法进行了比较。(C)2016由Elsevier Ltd.出版
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. (C) 2016 Published by Elsevier Ltd.