Integrated approach for manufacturing-remanufacturing systems and closed-loop supply chain optimization

本研究将以系统整合的方法探讨闭环供应链管理及其相关议题。前向供应链是指新产品的原材料和零部件供应、新产品生产、产品分销、运输和交付给客户的供应链。逆向供应链（RSC）涉及收集使用过的和可重复使用的材料，运输，分类和检查以及再制造，以使它们“像新产品一样好”。针对与FSC和RSC相关的不同问题进行了广泛的研究。近年来，许多与再制造相关的问题也得到了研究。然而，CLSCM的研究主要集中在物流和运输相关的问题。拟议的研究将解决与CLSCM相关的问题，使用综合方法优化制造和再制造活动的协调，因为它们可能共享机器，设备，劳动力和其他生产资源。目前对这类问题的研究是有限的，所研究的问题往往过于简化，是实际的。我们计划遵循一种集成的方法来开发优化数学模型和解决方法，用于解决混合制造和再制造系统中的生产计划，作业调度，质量控制和改进等问题。其他CLSCM问题，包括使用先进材料，如复合材料，物流，成本，客户需求和环境效益将被纳入扩展的优化模型。完善和先进的建模工具，优化方法和计算技术将被用来开发有效和高效的解决方案，解决实际CLSCM问题的方法。

The proposed research will follow an integrated system approach to study closed-loop supply chain management (CLSCM) and related issues. A forward supply chain (FSC) is concerned with material and components supply for new products, new product production, product distribution, transportation and delivery to customers. A reverse supply chain (RSC) is concerned with collection of used and reusable materials, transportation, sorting and inspection and remanufacturing to make them "as good as new" products. Extensive research has been done addressing different problems related to FSC and RSC. In recent years, many remanufacturing related problems have also been studied. However, research on CLSCM has been mainly focused on problems related to logistics and transportation. The proposed research will address problems related to CLSCM using an integrated approach for optimal coordination of manufacturing and remanufacturing activities as they may share machines, equipment, labor and other production resources. Current research on such problems is limited and the studied problems are often too simplified to be practical. We plan to follow an integrated approach to develop optimization math models and solution methodology for solving production planning, job scheduling, quality control and improvement and other problems in hybrid manufacturing and remanufacturing systems. Other CLSCM issues including the use of advanced materials such as composite materials, logistics, costs, customer demands and environmental benefits will be incorporated into the extended optimization models. Well-established and advanced modeling tools, optimization methods and computing technology will be utilized to develop effective and efficient solution methods for solving practical CLSCM problems.