Nowadays, evolving toward sustainable operations among supply chains is a critical need for the near future and the well-being of the upcoming generations. The term sustainability commonly refers to the interactions between the economic, environmental and social dimensions of development. A sustainable development usually refers to: “A development that meets the needs of the present without compromising the ability of future generations to meet their own needs". Practitioners and academics all over the world are working toward this goal since the last three decades. Thus, this thesis comes to complement this effort toward achieving sustainability in supply chain operations.
My dissertation, entitled “quantitative models for sustainable reverse logistics network design under uncertainty”, focuses on the importance of developing decision-making models that include critical uncertainties inherent to the reverse logistics operations in the industry. It studies more specifically the trade-offs that are necessary to design efficient reverse logistics networks while considering various environmental aspects, thus improving our chances to take this step toward sustainability. In the three articles presented below, we will use the construction, renovation and demolition (CRD) industry as a reference to validate our models through several case studies.
The first article, titled “Reverse logistics network redesign under uncertainty for wood waste in the CRD industry” presents a detailed case study of the challenges related to the wood building material waste management in Quebec, Canada. In this paper, the main objective is to determine the location and the capacities of the sorting facilities to ensure compliance with the regulation and prevent the wood from being massively landfilled. We formulate the problem as a mixed-integer linear programming model (MILP) to minimize the total cost of the wood recycling processes collected from CRD sites. We start from the real collection centers’ locations from the Quebec CRD industry and we propose a scenario-based approach to redesign the reverse logistics network based on various realizations of the randomness targeting the uncertain parameters. The results demonstrate that efforts toward obtaining accurate information about the supply sources’ locations, the collected wood quantity and its quality would guarantee a more efficient reverse logistics network redesign. Although environmental and social considerations are not addressed in this article, it represents a first step toward sustainability by optimizing waste management operations in a sector that is among the biggest waste generators worldwide.
Thus, in the second article, titled “A two-stage stochastic optimization model for reverse logistics network design under dynamic suppliers’ locations”, we introduce a new advanced model formulation that addresses multiple scenarios at the same time in order to cope with uncertainty in the best manner over a multi-period planning horizon. The availability of each material collected from the supply sources and the recycling rates at the collection centers are the main sources of uncertainty considered in this study. This time, not only we optimize the reverse logistics network design, but we also evaluate the integration of logistics platforms called source-separation centers (SSC), that we use to perform source-separation of the materials before shipping them to the main collection centers. We perform a sensitivity analysis on the number of supply sources (i.e. waste generators) to compare low-density rural collection zones versus high-density urban areas, where the waste collection activities are often more challenging. Although the SSC improve the network performance in both rural and urban zones, the flexibility provided by these dynamic platforms reaches its best efficiency in the high-density urban areas. The results suggest significant RLND adjustments that lead to increase both the average expected profit and the amount of materials recycled through the reverse logistics channel.
Finally, in the third article, titled “A carbon-constrained stochastic model for eco-efficient reverse logistics network design under environmental regulations in the CRD Industry”, we adapt the stochastic model of the previous article to include environmental considerations by adding a second objective function. In this research, we evaluate the optimal eco-efficient reverse logistics network design for the wood waste recycling from the CRD industry under both landfilling restrictions and emission control by a cap-and-trade system, such as the one effective in Quebec these days. In this paper, we emphasize the importance of the source separation strategy to address the challenge caused by the unpredictable quality of the wood collected and its impact on the efficiency of the recycling processes. Indeed, by accounting the emissions released by the various recycling processes, it turns out that the landfilling option may be the best option depending on the quality level of the collected waste. Finally, in this paper we establish the relation between the quality level uncertainty of the collected materials and the difficulty to comply with governmental recycling targets.
Overall, the scenario-based approach in the first article allows establishing the problematic of multiple uncertainties for designing an optimal reverse logistics network that performs under each scenario. Based on this finding, in the second article we develop a two-stage stochastic model in order to find the best expected RLND to cope with a large number of possible scenarios in a multi-period planning horizon. Lastly, in the third article we adapt this model to fit with the reality of the CRD industry for the wood waste recycling case study. Such adaptations imply emissions accounting from the wood recycling processes and complying with the legal framework regarding the recycling targets
