Grant: Discovery Grants Program - Individual

Reverse Logistics (RL) is defined as the logistics activities that encompass the return of used products by users and the recovery of products that are then made usable again in markets. Recovery activities include those that are performed in relation to the return of used products, such as acquisition, product disposition, repair, remanufacturing, recycling, and remarketing. A Closed-Loop Supply Chain (CLSC) contains both forward logistics and RL. There are some major decisions to consider in RL and CLSC network design and optimization, such as: which facilities (e.g., collection centres, plants) are open, and how many products and parts are there in each part of the network. The focus in this field so far has been on developing optimization models without considering data-driven techniques and forecasting methods to determine the values of the parameters in the optimization models. In addition, most publications failed to consider uncertainty and sustainability factors at the same time during RL and CLSC design and optimization. Furthermore, very few investigations have considered supplier selection and order allocation during RL and CLSC configuration. The aim of this research program is to develop original optimization models and new solution approaches for designing and optimizing different RL and CLSC networks in Canada. In this context, three dimensions will be explored: (1) Developing forecasting methods based on data science techniques (e.g., machine learning, deep learning, and neural networks) and combining them with RL and CLSC design and optimization. The values of some model parameters (e.g., demand, and return) will be determined based on those developed methods. To this goal, real-world datasets will be utilized (2) Considering uncertainty and sustainability in RL and CLSC design and optimization and finding managerial insights. In this part, several sources of uncertainty, such as demand, return, capacity, and cost will be considered together, in addition to the environmental, economic, and social pillars of sustainability, as the objective functions. (3) Integrating supplier selection and order allocation processes in RL and CLSC design and optimization. In this dimension, new multi-objective mathematical models and appropriate solution approaches will be developed. This research program will make significant research contributions to the RL and CLSC fields and enhance Canada's profile in the global academic community. Moreover, it will help practitioners in solving RL and CLSC problems, such as network optimization, leading to significant economic benefits. The knowledge transfer from this study could therefore have long-term implications on informing government regulations and policies around the management of waste. Finally, HQP training is an important part of this research, as PhD, master's, and undergraduate students will be able to learn, develop, and apply knowledge of RL and CLSC networks in their future careers.