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首页> 外文期刊>International Journal of Production Research >Minimisation of supply chain cost with embedded risk using computational intelligence approaches
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Minimisation of supply chain cost with embedded risk using computational intelligence approaches

机译：使用计算智能方法将供应链成本最小化并隐含风险

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摘要

Global supply chains are vulnerable towards different types of risks and are dynamically expanding with the increase in globalisation. Costs are associated with every risk factor that causes disturbances in the allocation of certain goods at the required place and time, and with the required quality and quantity. In this paper, we consider a multi-echelon global supply chain model, where raw material suppliers, manufacturers, warehouses and markets are located in different countries. The paper first identifies all types of operational risk factors, their expected value and probability of occurrence, and associated additional cost. Based on initial information for the risk factors, optimal decisions regarding the inter-echelon quantity flow in the supply chain are made for a single planning horizon. Then, with the change in the expected value of the risk factors, the intra-echelon shift of flow is determined in order to minimise the total cost and risk factors. Considering the complexity involved with the problem, various computational intelligence techniques such as genetic algorithms, particle swarm optimisation and artificial bee colony are applied in the solution evaluation phase. The results obtained using the developed model illustrate that the ability to react to changes in risk factors offers potential solutions to robust supply chain design.
机译：全球供应链易受不同类型风险的影响，并且随着全球化的发展而动态地扩展。成本与导致在所需地点和时间分配某些商品的扰动的每个风险因素以及所需的质量和数量有关。在本文中，我们考虑了多级全球供应链模型，其中原材料供应商，制造商，仓库和市场位于不同的国家。本文首先确定了所有类型的操作风险因素，它们的期望值和发生的可能性以及相关的额外成本。基于风险因素的初始信息，针对单个计划范围做出关于供应链中梯队间数量流的最佳决策。然后，随着风险因素的期望值的变化，确定梯队内部流量的变化，以使总成本和风险因素最小化。考虑到问题的复杂性，在解决方案评估阶段应用了各种计算智能技术，例如遗传算法，粒子群优化和人工蜂群。使用开发的模型获得的结果表明，对风险因素变化做出反应的能力为稳健的供应链设计提供了潜在的解决方案。

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