Closed-loop supply chain configuration and optimization

在前向供应链中，产品从供应商发送到制造商，最后发送到客户。在逆向供应链中，一些产品被顾客退回。闭环供应链（CLSC）包括正向和反向供应链。CLSC（例如轮胎再制造）的目标是从新产品和退回产品中获得经济和环境价值。该项目将在CLSCs方面进行三个主要领域的研究。首先，本研究关注clsc内部的几个不确定性来源，包括需求、退货率和退货产品的质量。在这个例子中，我们的目标是识别和分析在CLSC网络中同时运行的不确定性的不同来源。其次，本研究将考虑如何最大限度地减少CLSC网络的浪费，研究相关的环境因素，以期找到可持续的解决方案。第三，本研究将探讨大数据对CLSC网络配置和优化的影响，考虑到CLSC参数中有时可以获得大量数据。***本研究项目将考察加拿大CLSCs的真实案例研究，重点关注四种产品：纸张、轮胎、计算机和有害材料。它将考虑典型的CLSC产品网络，相关的回收方案（如回收），以及加拿大的联邦和省政策。此外，它将基于运筹学技术，如鲁棒优化，多目标规划和元启发式算法，提出数学模型并开发适当的解决方案。最后，为关注加拿大行业的从业者提供相关的管理见解和建议。**************

In a forward supply chain, products are sent from suppliers to manufacturers and finally, to customers. In a reverse supply chain, some of the products are returned by customers. A closed-loop supply chain (CLSC) encompasses both forward and reverse supply chains. The goal of a CLSC (e.g. tire remanufacturing), is to gain economic and environmental values from both new and returned products. This program will pursue three primary areas of research in regards to CLSCs. First, this research is concerned with several sources of uncertainty within CLSCs, including the demand, return rate and quality of returned products. Our goal in this instance is to identify and analyze the different sources of uncertainty operating simultaneously in CLSC networks. Second, this research will consider how to minimize waste across CLSC networks, studying relevant environmental factors with a view to uncovering sustainable solutions. Third, this research will investigate the impact of Big Data on CLSC network configuration and optimization, given that large amounts of data are sometimes available within CLSC parameters. ***This research program will examine real case studies in Canada, CLSCs with a focus on four types of products: paper, tires, computers and hazardous materials. It will consider the typical CLSC networks of products, related recovery options (e.g. recycling), and federal and provincial policies in Canada. Moreover, it will propose mathematical models and develop appropriate solutions based on operations research techniques, e.g. robust optimization, multi-objective programming, and metaheuristic algorithms. Finally, it will provide relevant managerial insights and suggestions for practitioners focusing on Canadian industries.**************