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A hybrid adaptive decision system for supply chain reconfiguration

机译：供应链重构的混合自适应决策系统

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摘要

Due to short product life cycle, it is expedient to reconfiguration an existing supply chain from time to time. Companies need to impose the standards on operational units for finding the best or the near best alternative configuration. Thus, it becomes imperative to effectively adapt various enablers in a supply chain by understanding the dynamics between them that help to reconfigure a supply chain for high levels of performance. This paper presents an integration of agent-based simulation and decision tree learning as the data mining techniques to determine adaptive decisions of operational units of a mobile phone supply chain. Agent-based simulation output is subjected to data mining analysis to understand system behaviour in terms of interactions and the factors influencing the performance. An entropy-based formulation is proposed as the basis for comparing different operational units in the supply chain. The insights obtained are then encapsulated as operational rules and guidelines supporting better decision-making.
机译：由于产品生命周期短，因此不时重新配置现有供应链是很方便的。公司需要对运营部门强加标准，以找到最佳或接近最佳的替代配置。因此，当务之急是通过了解各个促成因素之间的动态关系来有效地适应供应链中的各种因素，这有助于重新配置供应链以实现高水平的性能。本文提出了一种基于代理的仿真和决策树学习的集成，作为数据挖掘技术来确定手机供应链运营单位的自适应决策。对基于代理的模拟输出进行数据挖掘分析，以根据交互作用和影响性能的因素来了解系统行为。提出了一种基于熵的公式，作为比较供应链中不同运营单位的基础。然后将获得的见解封装为可支持更好决策的操作规则和准则。

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