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Multiscale production routing in multicommodity supply chains with complex production facilities

机译:具有复杂生产设施的多商品供应链中的多规模生产路线

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In this work, we introduce the multiscale production routing problem (MPRP), which considers the coordination of production, inventory, distribution, and routing decisions in multicommodity supply chains with complex continuous production facilities. We propose an MILP model involving two different time grids. While a detailed mode-based production scheduling model captures all critical operational constraints on the fine time grid, vehicle routing is considered in each time period of the coarse time grid. In order to solve large instances of the MPRP, we propose an iterative MILP-based heuristic approach that solves the MILP model with a restricted set of candidate routes at each iteration and dynamically updates the set of candidate routes for the next iteration. The results of an extensive computational study show that the proposed algorithm finds high quality solutions hi reasonable computation times, and in large instances, it significantly outperforms a standard two-phase heuristic approach and a solution strategy involving a one-time heuristic pre-generation of candidate routes. Similar results are achieved in an industrial case study, which considers a real-world industrial gas supply chain.
机译:在这项工作中,我们介绍了多尺度生产路径问题(MPRP),该问题考虑了具有复杂连续生产设施的多商品供应链中生产,库存,分销和路径决策的协调。我们提出了一个包含两个不同时间网格的MILP模型。尽管基于模式的详细生产计划模型捕获了精细时间网格上的所有关键操作约束,但在粗糙时间网格的每个时间段内都会考虑车辆路线。为了解决MPRP的大型实例,我们提出了一种基于MILP迭代的启发式方法,该方法通过在每次迭代中使用一组受限的候选路由来求解MILP模型,并为下一次迭代动态更新一组候选路由。大量计算研究的结果表明,提出的算法可在合理的计算时间内找到高质量的解决方案,并且在很大的情况下,它的性能明显优于标准的两阶段启发式方法和一次包含一次启发式预生成的解决方案。候选路线。在工业案例研究中获得了类似的结果,该案例研究考虑了现实世界中的工业气体供应链。

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