A Novel Reverse Logistics Network Design Considering Multi-Level Investments for Facility Reconstruction with Environmental Considerations

Abstract

Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products. Many studies have been developed for decision-making at tactical, practical, and operational levels of the reverse supply chain. However, many enterprises face a challenge that is how to design the reverse logistics networks into their existing forward logistics networks to account for both economic and environmental sustainability. In this case, it is necessary to design a novel reverse logistics network by reconstructing the facilities based on the existing forward logistics network. Multi-level investments are considered for facility reconstruction because more investment and more advanced remanufacturing technologies need to be applied to reduce the carbon emissions and improve facility capacities. Besides, uncertain elements include the demand for new products and return quantity of used products, making this problem challenging. To handle those uncertain elements, a bi-objective stochastic integer nonlinear programming model is proposed to facilitate this novel reverse logistics network design problem with economic and environmental objectives, where tactical decisions of facility locations, investment level choices, item flows, and vehicle assignments are involved. To show the applicability and computational efficiency of the proposed model, several numerical experiments with sensitivity analysis are provided. Finally, the trade-off between the profit and carbon emissions is presented and the sensitive analysis of changing several key input parameters is also discussed.

Reverse Logistics Network Design and Simulation for Automatic Teller Machines Based on Carbon Emission and Economic Benefits: A Study of the Anhui Province ATMs Industry

“…Therefore, the value of ω 1 has minimal effect on the final optimization result of the model. This is consistent with the conclusion drawn in the literature [15,47]. Their research results indicated that the reduction in economic costs inevitably leads to an increase in carbon emissions.…”

“…Hao Yu and Wei Deng Solvang [4] presented a stochastic mixed integer programming model for designing and planning a generic multi-source, multi-echelon, capacitated, and sustainable reverse logistics network for WEEE management under uncertainty. Xuehong Gao [15] designed a novel stochastic reverse logistics network by reconstructing the facilities based on the existing forward logistics network. Kannan et al [16] built an RL network design model based on carbon footprint, which can be applied to real-time case studies, such as the tire, battery, and bottling industries.…”

“…Constraint (9) means that the flow from module disassembly and recycling centers (L) to suppliers (S) must not exceed the capacity of S. Constraint (10) is the capacity constraint of warehouse (W). Lastly, Constraints (11) to (15) are the capacity constraints of C, D, R, L, and M, respectively.…”

Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)

“…Over the last two decades, quantitative optimization models and computational methods have been extensively investigated in order to improve the decision-making on reverse logistics network design at both strategic and operational levels [22,24]. With the implementation of different techniques, these decision-support models focused mainly on the optimization of economic efficiency [25][26][27][28], environmental impact [23,[28][29][30], potential job creation as well as other socio-economic impact factors [31]. The effectiveness of these optimization models and computational methods have been validated in a large variety of industries and business sectors [32][33][34][35].…”

A Systematic Literature Review of Green and Sustainable Logistics: Bibliometric Analysis, Research Trend and Knowledge Taxonomy

“…Reverse logistics is convincingly one of the most efficient solutions to reduce environmental pollution and waste of resources by capturing and recovering the values of the used products [94]. Legislation, social image, corporate citizenship, and market competence force enterprises to integrate RL into their supply chains [95].…”