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A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

机译:供应链管理中基于矩阵的贝叶斯方法用于制造资源分配计划

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摘要

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.
机译:如今,制造资源的供应链通常是一个大型的复杂网络,其管理需要基于网络的资源分配计划。本文提出了一种新颖的基于矩阵的贝叶斯方法,用于推荐在用户指定的上下文中具有最大概率作为最优选择的最优资源分配计划。提议的基于矩阵的资源分配计划表示形式为供应链建模提供了一个良好的基础,以了解问题的复杂性,支持计算机推理,促进资源的重新分配并添加定量信息。所提出的贝叶斯方法通过解决多准则决策问题而产生了最优,强大的制造资源分配计划,该问题不仅解决了基于本体的静态制造资源能力,而且还解决了制造业供应链的统计性质,即资源执行和资源交互执行。采用遗传算法有效地解决了多准则决策问题。我们使用来自制造领域的案例研究来证明所提出的方法对最佳制造资源分配计划的适用性。

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