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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming
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Capacities-based supply chain network design considering demand uncertainty using two-stage stochastic programming

机译:使用两阶段随机规划考虑需求不确定性的基于容量的供应链网络设计

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The design of a supply chain (SC) aims to minimize cost so the product can reach the customer at the cheapest cost with flexible demand. The demand of a product is variable with time and environment. Most of the researchers have considered investment cost, processing cost, and transportation cost as variable costs to minimize the cost while considering a constant demand. In actual practice, the demands are flexible. In this paper, a two-stage stochastic programming model has been proposed for a capacities-based network design of a supply chain for flexible demands while considering inventory carrying cost and missed opportunity cost in addition to the abovementioned costs. It will enhance the logistic planning and seek the location network optimally. Furthermore, in the first stage, decision variables represent different nodes (facility locations of echelons) of the supply chain, with the assumption that they will be considered at the design stage before uncertain parameters are unveiled. On the other hand, decision variables related to the amount of products to be produced and stored in the nodes of the SC, the flows of materials among the entities of the network, and shortfalls and excess at the customer centers are considered as second-stage variables. The methodology has been illustrated by solving an example. It was found that the proposed model yields more feasible and advantageous results.
机译:供应链(SC)的设计旨在最大程度地降低成本,从而使产品能够以最灵活的需求以最便宜的成本到达客户手中。产品的需求随时间和环境而变化。大多数研究人员已将投资成本,加工成本和运输成本视为可变成本,以便在考虑恒定需求的同时最大程度地降低成本。在实际中,需求是灵活的。在本文中,提出了一种两阶段随机规划模型,用于基于容量的供应链网络设计,以满足灵活的需求,同时除了上述成本外还考虑了库存成本和错过的机会成本。它将增强后勤计划并优化寻找位置网络。此外,在第一阶段,决策变量代表供应链的不同节点(梯队的设施位置),并假设在揭露不确定参数之前将在设计阶段考虑它们。另一方面,与SC节点中要生产和存储的产品数量,网络实体之间的物料流以及客户中心的短缺和过剩相关的决策变量被认为是第二阶段。变量。通过举例说明了该方法。发现所提出的模型产生了更多可行和有利的结果。

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