Modeling and Optimization of Sustainable Reverse Logistics Enterprise

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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.