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Integrated partner selection and production-distribution planning for manufacturing chains

机译:制造链的综合合作伙伴选择和生产分配计划

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摘要

This paper presents an integrated approach to solve the partner selection, and production-distribution planning problem in the design of manufacturing chains operating under a multi-product, multi-stage, multi-production route, multi-machine, and multi-period manufacturing environment. Such a problem often occurs when manufacturing companies and material suppliers establish partnerships to form a virtual enterprise, a manufacturing chain, in which its members cooperate to capture rising market opportunities. An optimization model and a hybrid algorithm which combines particle swarm optimization and genetic algorithm with learning scheme are developed to derive the optimal decisions. The performance of the developed approach is illustrated by using a simple case problem and a set of randomly generated test problems. Indeed, it is shown that the proposed hybrid algorithm can outperform the conventional genetic algorithm, the particle swarm optimization and the genetic algorithm with learning scheme, and is therefore an excellent tool for designing optimal manufacturing chains.
机译:本文提出了一种综合方法来解决在多产品,多阶段,多生产路线,多机器和多周期制造环境下运行的制造链设计中的合作伙伴选择和生产分配计划问题。 。当制造公司和材料供应商建立合作伙伴关系以形成虚拟企业(制造链)时,通常会发生此问题,其成员在其中进行合作以抓住不断增长的市场机会。结合粒子群优化和遗传算法与学习方案,建立了优化模型和混合算法,得出了最优决策。通过使用一个简单的案例问题和一组随机生成的测试问题来说明所开发方法的性能。的确,表明所提混合算法的性能优于传统遗传算法,粒子群优化算法和带学习方案的遗传算法,是设计最优制造链的优良工具。

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