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Matheuristic for the decentralized factories scheduling problem

机译：分散工厂调度问题的数学方法

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In this study, a novel mixed integer linear programming (MILP) model is developed for the decentralized factories scheduling problem, where a set of transporters is used for shipping goods among parallel factories to minimize the production costs over all of the factories. Due to its typical features, such as multiple heterogeneous factories and transportation times, this problem is extremely difficult to solve, especially for large-scale problems. In order to address this difficulty, the main aim of this study is to develop a new solution algorithm based on the interoperation of metaheuristics and mathematical programming techniques to minimize the production costs for jobs. The algorithm comprises an electromagnetism-like algorithm and variable neighborhood search. In this hybridization based on MILP relaxation, the guiding principle involves the ordering of neighborhood structures. The results obtained by the proposed algorithm and MILP are analyzed and compared for various test problems.
机译：在这项研究中，针对分散工厂调度问题开发了一种新颖的混合整数线性规划（MILP）模型，该模型中使用一组运输工具在并行工厂之间运输货物，以最小化所有工厂的生产成本。由于其典型特征（例如多个异类工厂和运输时间），此问题极难解决，尤其是对于大规模问题。为了解决这个难题，本研究的主要目的是开发一种基于元启发法和数学编程技术的互操作的新的求解算法，以最大程度地减少作业的生产成本。该算法包括类电磁算法和可变邻域搜索。在基于MILP松弛的杂交中，指导原则涉及邻域结构的排序。针对各种测试问题，分析并比较了由所提出的算法和MILP获得的结果。

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