The mobile supply chain (MSC) is a new development that aims to help companies implement these ideas. In MSCs, production, distribution, and delivery of a product family is performed by a mobile factory (MF), which can be carried by truck and shared among different production sites. In this paper, a real-world application of mobile factories in the chemical industry is studied. Critical production assets (e.g. reactor) are carried by truck and can produce a product family locally. For this purpose, a mixed-integer mathematical model is developed to optimize the logistics costs of MSCs, concerning retailer/job-order allocation, production scheduling, and MF routing. The proposed optimization model is overly complex due to the three NP-hard sub-problems. Therefore, a neighborhood search and an evolutionary algorithm are developed to solve the problem in large-scale data sets. The experimental results show that the proposed algorithms can find a near-optimal solution in a reasonable time.

移动供应链 (MSC) 是一项旨在帮助公司实施这些想法的新发展。在 MSC 中，产品系列的生产、分销和交付由移动工厂 (MF) 执行，该工厂可以由卡车运输并在不同的生产站点之间共享。在本文中，研究了移动工厂在化工行业的实际应用。关键生产资产（例如反应堆）由卡车运输，可以在当地生产一个产品系列。为此，开发了一个混合整数数学模型来优化 MSC 的物流成本，涉及零售商/作业订单分配、生产调度和 MF 路由。由于三个 NP-hard 子问题，所提出的优化模型过于复杂。所以，开发了邻域搜索和进化算法来解决大规模数据集中的问题。实验结果表明，所提出的算法可以在合理的时间内找到接近最优的解。