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Coordinated optimisation of platform-driven product line planning by bilevel programming

机译:通过双层编程协调优化平台驱动的产品线计划

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摘要

Product line planning (PLP) aims at an optimal combination of product feature offerings, suggesting itself to be a determinant decision for a company to satisfy diverse customer needs and gain competitive advantages. Fulfilment of planned product lines must make trade-offs between product variety and production costs. To balance the costs of product lines, manufacturers often adopt a product platform configuration (PPC) approach to redesign product and process platforms by adding new modules to the legacy platforms. The PPC is an effective means of providing product variety while controlling the manufacturing costs. The PLP and PPC problems have traditionally been investigated separately in the marketing research and engineering design fields. It is important to coordinate PLP and PPC decisions within a coherent optimisation framework. This paper proposes a bilevel mixed 0-1 nonlinear programming model to formulate coordinated optimisation for platform-driven product line planning. The upper level deals with the PLP problem by maximising the profit of an entire product line, whilst the lower level copes with the multiple product platforms optimisation for the optimal PPC in accordance with the upper level decisions of product line structure. To solve this bilevel programming model, a bilevel genetic algorithm is developed to find the optimal solution. A case study of coordinated optimisation between an automobile line and its product platforms is presented to demonstrate the feasibility and effectiveness of the proposed bilevel programming in comparison with a typical all-in-one' approach and a non-joint optimisation programming.
机译:产品线计划(PLP)旨在将产品功能产品进行最佳组合,这表明它本身是公司满足各种客户需求并获得竞争优势的决定性决定。要实现计划的产品线,必须在产品种类和生产成本之间进行权衡。为了平衡产品线的成本,制造商通常采用产品平台配置(PPC)方法,通过向旧平台添加新模块来重新设计产品和流程平台。 PPC是在控制制造成本的同时提供产品多样性的有效手段。传统上,PLP和PPC问题是在市场研究和工程设计领域中单独进行调查的。在一致的优化框架内协调PLP和PPC决策很重要。本文提出了一种双层混合0-1非线性规划模型,为平台驱动的产品线规划制定协调优化。上层通过最大化整个产品线的利润来处理PLP问题,而下层则根据产品线结构的上层决策来应对针对最佳PPC的多个产品平台优化。为了解决该双层规划模型,开发了一种双层遗传算法以找到最优解。提出了汽车生产线及其产品平台之间协调优化的案例研究,以证明与典型的多合一方法和非联合优化编程相比,拟议的双层编程的可行性和有效性。

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