A Carbon-Constrained Stochastic Model for Eco-Efficient Reverse Logistics Network Design

Purpose – This research introduces a novel multi-period, multi-echelon and multi-objective two-stage stochastic model (MOTSM) for eco-efficient reverse logistics network design (RLND) under environmental regulations. The primary goals of the optimization model are to maximize the expected profit, minimize landfilling activities and to increase the eco-efficiency of recycling activities. In comparison with the previous stochastic optimization models in this area, which mainly focus on the expected optimal value, this paper emphasizes the importance of source-separation of recyclable materials under the joint landfilling and greenhouse gases emission constraints. Methodology – To address this challenge, the decision model identifies the best strategies to operate and adjust the processing capacity of existing collection centers (CC) and open new ones with the appropriate size and the suitable location. The network structure includes source separation platforms (SSP) that allow the separation of the collected materials and shipments consolidation at an early stage of the reverse logistics channel. The problem is formulated in a stochastic mixed integer linear programming making a form. The decision-making model considers the uncertainties associated with input parameters including the quantity of recyclable materials and the recycling rates caused by quality issues. Also, the mathematical model deals with dynamic supply sources locations over a multi-period horizon. We solve this problem by using a sample average approximation (SAA) procedure to deal with a large number of scenarios, while the ε-constraint method is used to cope with the multiple objective functions and provide the best trade-offs. An application of the proposed model is illustrated through a case study targeting wood waste from the construction, renovation, and demolition (CRD) industry.Main findings – The results highlight the necessity for the local authorities to analyze environmental policies carefully to avoid contractionary impacts. For this specific study, the experimental results demonstrate the advantages of flexibility in reverse logistics network to achieve both compliance and eco-efficiency simultaneously. Indeed, although the trend is to encourage material recycling at the end of their lifecycle, the experiments revealed that landfilling in the CRD industry can be necessary to avoid high emission levels due to performing the recycling activities with poor quality materials. Finally, the case study underlines the positive impact of operating the SSC in an uncertain environment, showing in the meantime the critical role of source separation in the CRD industry. Contribution/originality – To the best of our knowledge, this is the first paper that quantitatively assesses the impact of uncertainties targeting the wood recycling processes in the CRD industry through RLND decisions. Moreover, this study enriches the literature of sustainable reverse logistics which presents a lack of quantitative modeling approaches targeting industries that represent an environmental burden for the society, such as the case of the CRD sector. Finally, the carbon-constrained stochastic model for Eco-Efficient reverse logistics network design addresses particularly the challenge brought by the dynamic locations of the supply sources which have an impact on the economic and environmental performance of many industries