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A hybrid genetic algorithm for a multilevel assembly replenishment planning problem with stochastic lead times
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.cie.2020.106794 Oussama Ben-Ammar , Philippe Castagliola , Alexandre Dolgui , Faicel Hnaien
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.cie.2020.106794 Oussama Ben-Ammar , Philippe Castagliola , Alexandre Dolgui , Faicel Hnaien
Abstract This paper on replenishment planning for multi-level assembly systems with several components at each level deals with the problem of calculating planned lead-times when the real lead-times for all components are assumed to be stochastic. This problem is already treated in the literature by using a recursive procedure and a Branch and Bound algorithm. Here, in order to decrease the computation time, a novel generalized probabilistic model based on an iterative approach is developed. The proposed model calculates the expected total cost, which is composed of the inventory holding cost for components and the backlogging and inventory holding costs for the finished product. An iterative approach and a hybrid genetic algorithm are introduced to determine the planned order release dates for components at the last level of the bill of materials that minimizes the expected total cost. Experimental results show that the proposed optimization algorithm efficiently finds good-quality approximate solutions regardless of the type of assembly system, the number of components at the last level and the variability of the finished product-related costs.
中文翻译:
具有随机提前期的多级装配补货计划问题的混合遗传算法
摘要 本文关于在每一级有多个组件的多级装配系统的补货计划处理了当所有组件的实际提前期被假定为随机时计算计划提前期的问题。文献中已经使用递归过程和分支定界算法处理了这个问题。在这里,为了减少计算时间,开发了一种基于迭代方法的新型广义概率模型。所提出的模型计算预期总成本,该成本由组件的库存持有成本和成品的积压和库存持有成本组成。引入迭代方法和混合遗传算法来确定物料清单最后一级组件的计划订单发布日期,从而最大限度地降低预期总成本。实验结果表明,无论装配系统的类型、最后一级的组件数量以及成品相关成本的可变性,所提出的优化算法都能有效地找到高质量的近似解。
更新日期:2020-11-01
中文翻译:
具有随机提前期的多级装配补货计划问题的混合遗传算法
摘要 本文关于在每一级有多个组件的多级装配系统的补货计划处理了当所有组件的实际提前期被假定为随机时计算计划提前期的问题。文献中已经使用递归过程和分支定界算法处理了这个问题。在这里,为了减少计算时间,开发了一种基于迭代方法的新型广义概率模型。所提出的模型计算预期总成本,该成本由组件的库存持有成本和成品的积压和库存持有成本组成。引入迭代方法和混合遗传算法来确定物料清单最后一级组件的计划订单发布日期,从而最大限度地降低预期总成本。实验结果表明,无论装配系统的类型、最后一级的组件数量以及成品相关成本的可变性,所提出的优化算法都能有效地找到高质量的近似解。