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Fuzzy-set qualitative comparative analysis applied to the design of a network flow of automated guided vehicles for improving business productivity

机译:模糊集定性比较分析应用于自动导引车网络流程设计,以提高企业生产力

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摘要

Designing efficient warehouse management systems is essential to improve business performance. The use of autonomous guided vehicles (AGVs) in logistic processes and material handling systems (MHS) improves productivity and reduces costs. However, determining the appropriateness and financial feasibility of acquiring a fleet of AGVs, together with the definition of their path layout, routing schemes, operation tasks, and network flow, becomes a complex problem when designing flexible manufacturing systems (FMS). This study aids the design of a fleet of AGVs by means of a fuzzy-set qualitative comparative analysis (fsQCA), which makes it possible to measure the level of satisfaction of managerial decision makers. It enables us to identify a combination of factors that lead to stakeholders' satisfaction while dealing with uncertain environments due to the heterogeneous nature of decision makers and factors. Our methodology has been applied to multi-criteria decision-making analysis, resulting in greater transparency, fairness, social equity, and consensus among stakeholders.
机译:设计有效的仓库管理系统对于提高业务绩效至关重要。在物流流程和物料搬运系统(MHS)中使用自动驾驶车辆(AGV)可以提高生产率并降低成本。但是,在设计柔性制造系统(FMS)时,确定购买AGV的适当性和财务可行性以及其路径布局,路由方案,操作任务和网络流的定义,成为一个复杂的问题。这项研究通过模糊集定性比较分析(fsQCA)辅助了AGV车队的设计,这使得可以衡量管理决策者的满意度。它使我们能够确定导致利益相关者满意的因素的组合,同时由于决策者和因素的异质性而应对不确定的环境。我们的方法已应用于多标准决策分析,从而提高了透明度,公平性,社会公平性以及利益相关者之间的共识。

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