Modeling and Optimization of Sustainable Reverse Logistics Enterprise

逆向物流（RL）是从消费者到零售商或制造商的收集、检查、分类和处置退货和/或使用过的产品的过程。过去几十年来，产品回收和处置在逆向物流中的重要性稳步增长（2016年，仅在美国，RL成本就达到9020亿美元，见https://www.hollingsworthllc.com/guide-reverse-logistics/）。逆向物流被视为可持续发展的重要贡献者，可定义为“满足当代人的需求而不损害后代人满足自身需求的能力”的能力。制造商收集二手产品进行再利用或再循环的主要原因有三个：(i)政府或环保组织实施的产品回收法，如对原始设备制造商（OEM）的废弃电子电气设备（WEEE）指令。根据该指令，OEM有责任收集其使用过的产品并保持环境清洁；（ii）再利用或回收物料可产生的利润；（三）树立一个具有环保意识的公司旗帜的重要性。这些动机包括公司在其概况中包括RL活动的必要性，以抓住再利用或回收使用过的产品的生态，经济和社会效益。制造商只有在做出正确的处理选择和有效地退货的情况下才能从RL中获益。在执行RL计划时，导致复杂性的因素包括：退货时间、数量和质量的可变性。虽然制造系统已经达到了高水平的效率和有效性，RL活动或再制造系统仍然远远落后。这是由于一系列原因造成的，包括：缺乏完善的绩效衡量模型，独特的预测模型和框架，以及采用新的或新兴的技术。本研究计划旨在通过采用更全面的建模技术和方法来理解和解决强化学习的复杂性。将使用基于代理的仿真和建模技术、元启发式方法、随机运筹学以及其他技术和技术，如数字孪生。它还提倡再制造商之间以及再制造商与学术界之间加强合作，以提高RL设计和运营的效率和效果。综上所述，提出的研究计划旨在支持可持续性和最大限度地提高逆向物流企业的盈利能力。这反过来又会增强制造商的社会、“绿色”或“生态”形象。

Reverse logistics (RL) is the process of collection, inspection, sorting and disposition of returned and/or used products from the consumer to a retailer or manufacturer. The importance of product recovery and disposition in reverse logistics is steadily growing since the past few decades (in 2016, in USA alone, RL costs have been $902 billion, see https://www.hollingsworthllc.com/guide-reverse-logistics/). Reverse logistics is seen as a vital contributor to sustainability, which can be defined as the ability to "meet the needs of the present generation without compromising the ability of future generations to meet their own needs." There are three main reasons for a manufacturer to collect used products for reuse or recycle: (i) the product take-back laws imposed by government or environmental organizations, like the waste electrical and electronic equipment (WEEE) directive on the original equipment manufacturers (OEM). Under this directive, OEM take responsibility to collect their used products and keep the environment clean; (ii) the profit that can be generated by reusing or recovering material; and (iii) the importance of falling under the banner of an environmentally conscious company. These motives encompass the necessity for a company to include RL activities in its profile to seize the ecological, economic and social benefits from reusing or recycling a used product. Manufactures may reap benefits of RL only if correct disposition options are made and the return of products is done effectively. The factors that cause complexity in carrying out a RL program include: variability of the time, the quantity, and the quality of returned products. While manufacturing systems have reached a high level of efficiency and effectiveness, RL activities or remanufacturing systems are still far behind. This is due to an array of reasons including: lack of well-established performance measurement models, distinctive forecasting models and frameworks, and adoption of new or emerging technologies. This research program aims at understanding and addressing RL intricacies by employing more comprehensive modeling techniques and approaches. Agent-based simulation and modeling techniques, meta-heuristics methods, stochastic operations research, and other techniques and technologies such as digital twin will be used. It also advocates for more collaboration among remanufacturers and between remanufacturers and academics to improve efficiency and effectiveness of RL design and operation. To recapitulate, the proposed research program aims at supporting sustainability and maximizing reverse logistics enterprise profitability. This in turn shall enhance the social, "green," or "eco" image of the manufacturers.