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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part B. Journal of engineering manufacture >Optimization of material requirement planning by fuzzy multi-objective linear programming
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Optimization of material requirement planning by fuzzy multi-objective linear programming

机译:基于模糊多目标线性规划的物料需求计划优化

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摘要

Material requirement planning (MRP) has evolved from a simplistic representation in the 1980s to today's manufacturing resource planning (MRPII) and enterprise resource planning (ERP) systems in order to meet changing business demands. The persisting momentous drive for lowest costs and highest quality dictates that MRP is deployed in an optimal manner. Multi-objective linear programming (MOLP), which is used simultaneously to optimize decisions through trade-offs between two or more conflicting objectives, has not been reported in MRP-related literature. As an extension of work reported by Yenisey [1], where optimization of material flow in MRP had been presented, a fuzzy multi-objective linear programming (f-MOLP) model is used where two objectives, namely minimization of total cost and minimization of total time of MRP, are targeted. The objective is to find the optimum production rate for each end-product at each period in accordance with the objectives and related constraints. The proposed f-MOLP is solved for two sets of conditions consisting of symmetric and asymmetric cases. The corresponding results show that the proposed models can help manufacturers make better decisions when facing uncertainty about objective functions as well as the constraints set. Degrees of satisfaction demonstrate the applicability of the proposed approach in the context of MRP.
机译:为了满足不断变化的业务需求,物料需求计划(MRP)已从1980年代的简化表示演变为当今的制造资源计划(MRPII)和企业资源计划(ERP)系统。持续不断的最低成本和最高质量驱动力要求以最佳方式部署MRP。与MRP相关的文献尚未报道多目标线性规划(MOLP),该规划同时用于通过两个或多个冲突目标之间的折衷来优化决策。作为Yenisey [1]报告的工作的扩展,其中提出了MRP中物料流的优化,使用了模糊多目标线性规划(f-MOLP)模型,其中有两个目标,即总成本的最小化和MPM的最小化。 MRP的总时间为目标。目的是根据目标和相关约束条件,在每个时期找到每种最终产品的最佳生产率。拟议的f-MOLP解决了由对称和不对称情况组成的两组条件。相应的结果表明,所提出的模型可以帮助制造商在面对目标函数和约束条件的不确定性时做出更好的决策。满意程度证明了该方法在MRP中的适用性。

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