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

拟议的研究将遵循集成系统的方法来研究闭环供应链管理(CLSCM)及相关问题。前向供应链涉及新产品的材料和零部件供应、新产品生产、产品配送、运输和交付给客户。逆向供应链(RSC)关注的是废旧材料和可重复使用材料的收集、运输、分类和检验以及再制造，以使它们像新产品一样好。针对与FSC和RSC相关的不同问题，已经进行了广泛的研究。近年来，人们对再制造相关问题也进行了大量的研究。然而，对物流供应链管理的研究主要集中在与物流运输相关的问题上。拟议的研究将使用一种优化协调制造和再制造活动的综合方法来解决与CLSCM有关的问题，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很有限，所研究的问题往往过于简单化，不切实际。我们计划按照集成的方法来开发优化数学模型和求解方法，以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。CLSCM的其他问题，包括复合材料、物流、成本、客户需求和环境效益等先进材料的使用，将被纳入扩展的优化模型。将利用成熟和先进的建模工具、优化方法和计算技术来开发有效和高效的解决方案，以解决CLSCM的实际问题。

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.