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An interactive approach for designing a robust disaster relief logistics network with perishable commodities

机译:设计具有易腐商品的健壮救灾物流网络的交互式方法

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Pre-positioning of relief commodities in appropriate locations and quantities at a pre-disaster phase assists a disaster relief logistics network in order to efficiently distribute the commodities to demand points; however, a disaster may occur after a long time, and generally perishable commodities (e.g., medical commodities and packed milk) have a fixed lifetime for use. Hence, this paper aims at developing a new integrated model in order to determine the optimum location-allocation and distribution plan, along with the best ordering policy for renewing the stocked perishable commodities at the pre-disaster phase. The uncertain nature of the problem leads to the utilization of a scenario-based robust stochastic approach. The proposed model simultaneously seeks to minimize: (1) the average of the weighted response times and (2) the total operational cost at the pre-disaster phase and the penalty costs of unmet demand and unused commodities at a post-disaster phase. The reservation level Tchebycheff procedure (RLTP) as an interactive approach is also applied to cope with the presented bi-objective model. The significance of the presented model and the efficiency of the RLTP method are then tested via a real case study in Iran.
机译:在灾前阶段将救灾商品预先放置在适当的位置和数量,以协助救灾后勤网络,以有效地将商品分配到需求点;但是,很长一段时间后可能会发生灾难,并且一般易腐商品(例如医疗商品和包装牛奶)的使用期限是固定的。因此,本文旨在开发一种新的集成模型,以确定最佳的位置分配和分配计划,以及在灾难前阶段更新易腐商品库存的最佳订购策略。问题的不确定性导致使用基于场景的鲁棒随机方法。提出的模型同时寻求最小化:(1)加权响应时间的平均值,以及(2)灾难前阶段的总运营成本以及灾难后阶段需求和未使用商品的损失成本。保留级别的Tchebycheff过程(RLTP)作为一种交互式方法也被用来应对提出的双目标模型。然后,通过在伊朗的一个实际案例研究来检验所提出模型的重要性和RLTP方法的效率。

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