Co-Optimization of Supply Chain Reconfiguration and Assembly Process Planning for Factory-in-a-Box Manufacturing
Co-Optimization of Supply Chain Reconfiguration and Assembly Process Planning for Factory-in-a-Box Manufacturing
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盒装工厂制造的供应链重构与装配工艺规划协同优化
DOI：
10.1115/1.4054519
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Minor, Kayla
中科院分区：
文献类型：
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作者：
Nwodu, Arriana;Pasha, Junayed;Jiang, Zhengqian;Guo, Weihong;Dulebenets, Maxim;Wang, Hui;Minor, Kayla
箱式工厂（FiB）是一项新兴技术，通过在车辆上携带工厂模块，在客户所在地附近进行现场生产，满足动态和多样化的市场需求。它适用于满足时间敏感的需求，例如灾害或流行病/大流行病的爆发。与传统制造业相比，FiB提出了一个新的挑战，即频繁地重新配置供应链网络，因为最终生产地点会随着载着工厂的车辆的行驶而发生变化。供应链网络重构涉及有关供应商或制造商是否可以保留在供应链中或被替换的决策。这样的供应链重构问题与制造过程规划相结合，制造过程规划将影响供应链网络中的物料流的任务分配给每个制造商。考虑到供应链的可重构性，本文建立了一个基于非线性整数规划的供应链重构与装配计划联合优化的数学模型。针对工艺规划与供应商/制造商选择的联合优化问题，提出了一种进化算法。EA的性能进行了验证与非线性求解器的一个放松的版本的问题。生产医疗产品的案例研究表明，在指导供应链重构和流程规划的方法，作为最终的生产基地搬迁，以满足当地的需求。该方法可以推广到供应链和服务流程规划的移动的医院提供现场医疗服务。
Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services.