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Optimization of Production-Distribution Problem in Supply Chain Management under Stochastic and Fuzzy Uncertainties

机译：随机和模糊不确定性下供应链管理中生产分配问题的优化

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摘要

Production-Distribution Problem (PDP) in Supply Chain Management (SCM) is an important tactical decision. One of the challenges in this decision is the size and complexity of supply chain system (SCS). On the other side, a tactical operation is a mid-term plan for 6-12 months; therefore, it includes different types of uncertainties, which is the second challenge. In the literature, the uncertain parameters were modeled as stochastic or fuzzy. However, there are a few studies in the literature that handle stochastic and fuzzy uncertainties simultaneously in PDP. In this paper, the modeling and solution approaches of PDP which contain stochastic and fuzzy uncertainties simultaneously are investigated for a SCS that includes multiple suppliers, multiple products, multiple plants, multiple warehouses, multiple retailers, multiple transport paths, and multiple time periods, which, to the best of the author's knowledge, is not handled in the literature. The PDP contains deterministic, fuzzy, fuzzy random, and random fuzzy parameters. To the best of the author's knowledge, there is no study in the literature which considers all of them simultaneously in PDP. An analytic solution approach has been developed by using possibilistic programming and chance-constrained programming approaches. The proposed modeling and solution approaches are implemented in a numerical example. The solution has shown that the proposed approaches successfully handled uncertainties and produce robust solutions for PDP.
机译：供应链管理（SCM）中的生产分配问题（PDP）是一项重要的战术决策。该决策面临的挑战之一是供应链系统（SCS）的规模和复杂性。另一方面，战术行动是6-12个月的中期计划；因此，它包括不同类型的不确定性，这是第二个挑战。在文献中，不确定参数被建模为随机或模糊。但是，有一些研究在PDP中同时处理随机和模糊不确定性。本文针对包含多个供应商，多个产品，多个工厂，多个仓库，多个零售商，多个运输路径和多个时间段的SCS，研究了同时包含随机和模糊不确定性的PDP建模和求解方法。据作者所知，并未在文献中处理。 PDP包含确定性，模糊，模糊随机和随机模糊参数。据作者所知，在文献中没有研究在PDP中同时考虑所有这些因素。通过使用可能性编程和机会受限的编程方法，已经开发了一种解析解决方案方法。数值示例中实现了所提出的建模和解决方案方法。该解决方案表明，所提出的方法成功处理了不确定性，并为PDP提供了可靠的解决方案。

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