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Clustering model and algorithm for production inventory and distribution problem

机译：生产库存和分销问题的聚类模型和算法

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摘要

One of the most challenging problem for optimizing supply chain is the coordination of production and distribution decision. This paper considers a customer clustering problem for a large scale production, inventory and distribution problem (P1DRP) where the demands at customers need to be satisfied in each period in the planning horizon with limited production and transportation capacity. The clustering aspect of the distribution problem is an important component for reducing complexity of the dispatch operation especially when the number of customers is large. In this paper an enhanced clustering model that considers factors (e.g. demand pattern and holding costs) that could affect the operating cost throughout the planning horizon is introduced. The algorithm based on a reactive tabu search for solving the clustering problem for PIDRP is proposed. A novel feature of the algorithm is to create adaptive core clusters which are used in the clustering process instead of the original data points. With this approach the complexity of the original problem is reduced and the proposed algorithm is able to solve the PIDRP much more efficient. Computational testing on instances up to 200 customers and 20 time periods demonstrates the effectiveness of the proposed model and algorithm in term of solution quality and runtime. Special cases of the problem are also considered to provide useful insights on how to apply the model with different settings of model's parameters.
机译：优化供应链最具挑战性的问题之一是生产和分销决策的协调。本文考虑了大规模生产，库存和分销问题（P1DRP）的客户聚类问题，该问题需要在计划和生产能力有限的规划范围内的每个时期内满足客户的需求。分配问题的群集方面是降低调度操作复杂性的重要组成部分，尤其是在客户数量较大时。本文介绍了一种增强的聚类模型，该模型考虑了可能影响整个计划范围内的运营成本的因素（例如需求模式和持有成本）。提出了一种基于反应禁忌搜索的PIDRP聚类算法。该算法的一个新颖特征是创建自适应核心集群，该集群将在集群过程中使用而不是原始数据点。通过这种方法，降低了原始问题的复杂性，并且所提出的算法能够更有效地解决PIDRP。在最多200个客户和20个时间段的实例上进行的计算测试证明了所提出的模型和算法在解决方案质量和运行时间方面的有效性。还考虑了问题的特殊情况，以提供有用的见解，以了解如何应用具有不同模型参数设置的模型。

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