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Genetic algorithm optimisation of an integrated aggregate production-distribution plan in supply chains

机译:供应链中集成的总生产分配计划的遗传算法优化

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A production plan concerns the allocation of resources of the company to meet the demand forecast over a certain planning horizon and a distribution plan involves the management of warehouse storage assignments, transport routings and inventory management issues. A production-distribution plan integrates the decisions in production, transport and warehousing as well as inventory management. The overall performance of a supply-chain is influenced significantly by the decisions taken in its production-distribution plan and hence one key issue in the performance evaluation of a supply chain is the modelling and optimisation of the production-distribution plan considering its actual complexity. Based on the integration of Aggregate Production Plan and Distribution Plan, this article develops a mixed integer non-linear formulation for a two-echelon supply network (i.e. a production-distribution network) considering the real-world variables and constraints. Genetic Algorithm (GA), known as a robust technique for solving complex problems, is employed for the optimisation of the developed mathematical model due to its ability to effectively deal with a large number of parameters. To demonstrate the applicability of the methodology, a real-life case study will be finally studied incorporating the production of different types of products in several manufacturing plants and the distribution of finished products from plants to a number of end-users via multiple direct/indirect transport routes.
机译:生产计划涉及公司的资源分配以满足特定计划范围内的需求预测,而分配计划涉及仓库存储分配,运输路线和库存管理问题的管理。生产分配计划将生产,运输和仓储以及库存管理中的决策整合在一起。供应链的整体绩效受其生产分配计划中做出的决定的很大影响,因此,供应链绩效评估中的一个关键问题是考虑其实际复杂性的生产分配计划的建模和优化。在综合总生产计划和分配计划的基础上,本文考虑了实际变量和约束条件,为两级供应网络(即生产-分配网络)开发了混合整数非线性公式。遗传算法(GA)被称为解决复杂问题的可靠技术,由于其有效处理大量参数的能力而被用于优化已开发的数学模型。为了证明该方法的适用性,将最终进行实际案例研究,包括在多个制造工厂中生产不同类型的产品,以及通过多个直接/间接方法将成品从工厂分配给多个最终用户运输路线。

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