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Solution algorithms for dynamic lot-sizing in remanufacturing systems

机译:再制造系统中动态批量的解决方案算法

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We consider the problem of determining the lot sizes that satisfy the demands of remanufactured products over a given planning horizon with discrete time periods. Remanufacturing, in which used or end-of-life products are restored to like-new condition, typically consists of disassembly, reprocessing and reassembly processes, and hence the lot sizes are determined for each of the three processes. The objective is to minimise the sum of setup and inventory holding costs occurring at the three processes. To represent the problem mathematically, we suggest a mixed integer programming model by combining the existing ones for disassembly and assembly systems. After proving that the problem is NP-hard, we suggest two dynamic programming based heuristics, called the aggregation and the decomposition type heuristics in this paper. Computational experiments were done on various test instances, and the results show that the two heuristics give near-optimal solutions in a short amount of computation time. Also, the performances of the heuristics are compared according to different values of problem parameters.
机译:我们考虑在给定的计划范围内以离散时间段确定满足再制造产品需求的批量大小的问题。再制造中,用过的或报废产品恢复到新的状态,通常由拆解,再加工和再组装过程组成,因此,针对这三个过程中的每一个,都确定批量。目的是最小化在这三个过程中发生的设置和库存持有成本的总和。为了用数学方法表示问题,我们建议通过组合现有的拆装和装配系统的混合整数规划模型。在证明问题是NP难的之后,我们提出两种基于动态规划的启发式方法,在本文中称为聚合和分解类型启发式方法。在不同的测试实例上进行了计算实验,结果表明,这两种启发式算法在较短的计算时间内给出了接近最优的解决方案。同样,根据问题参数的不同值比较启发式算法的性能。

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