Ensuring sustainability in the reverse supply chain in case of the ripple effect: A two-stage stochastic optimization model

Abstract

The devastating impact of the ripple effect increases the importance of the reverse supply chain (RSC) design to ensure sustainability in the long-term. That being the case, in this study, a two-stage stochastic mixed-integer optimization model is proposed to design an RSC network under uncertainty sourcing from the ripple effect (i.e. external side of RSC) by considering the environmental and economic dimensions of sustainability. The environmental and economic disruptions of the ripple effect are represented by the increase in the carbon emission levels and the distance of roads, and the decrease in the capacity of facilities, respectively. Accordingly, a set of scenarios is considered based on the disruption levels (low- and high-impact) in case of the ripple effect. Furthermore, an -reliability constraint is integrated into the model to further analyze the occurrence of scenarios. The model allows us to make integrated operational and strategic decisions by placing an emphasis on the carbon emission levels (i.e. environmental dimension) and the total cost (i.e. economic dimension). To obtain some remarkable insights, the proposed model is validated through computational experiments based on data extracted from a real case. The computational results show that the ripple effect increases the emission level and total cost up to 40%. For this reason, it suggested that the regulations regarding WEEE (Waste Electrical and Electronic Equipment) should be prepared by considering sustainability in the entire RSC network. Besides, it is realized that the centralized distribution strategy leads to a more resilient RSC network design.

Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review

“…Modeling Approach [60] Robust Programming [90] Robust Mixed Integer Linear Programming (ROMILP) [55] Mixed Integer Linear Programming (MILP) [91] Mixed Integer Linear Programming (MILP) [92] Mixed Integer Linear Programming (MILP) [93] Mixed Integer Non-Linear Programming (MINLP) [67] Mixed Integer Linear Programming (MILP) [94] Mixed Integer Linear Programming (MILP) [95] Mixed Integer Non-Linear Programming (MINLP) [68] Mixed Integer Linear Programming (MILP) [96] Stochastic Mixed-Integer Programming [97] Robust Programming [98] Mixed Integer Linear Programming (MILP) Table 7. Modeling approach and objective-Part b.…”

“…Considering the technology industry, there are the cases for households appliances by [96,97], LCD and LED TVs by [47], communications technology by [82], and medical devices by [93].…”

“…Sustainable and resilient closed-loop and reverse supply chains contribute in this sense. In addition to reducing the environmental impact in its operations, the SRSCND provide a structure for value recovery, second uses, or adequate final disposal of products [47,49,57,61,62,[66][67][68][70][71][72][78][79][80]83,84,86,87,92,96,97].…”

Examining the balance between efficiency and resilience in closed-loop supply chains

“…However, with few exceptions (e.g. Ivanov 2018 ; Yılmaz et al 2021 ), most of the efforts have focused on exploring traditional supply chains. The same applies to those papers investigating the trade-offs between efficiency and resilience, which have primarily focused on traditional systems.…”

Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times

“…The uncertainty in the parameters is generally handled with two approaches (i) stochastic optimization and (ii) robust optimization. Stochastic optimization describes the uncertainty with probability [24]; however, the parameter uncertainty is set-based and the optimization model is deterministic in the robust optimization [5]. Therefore, robust optimization allows addressing the parameter uncertainty more comprehensively compare to stochastic optimization, in which the scenario-based approach is applied [17].…”