Design and development of a fuzzy credibility-based reverse logistics network with buyback offers: A case study

Area of Science:

• Environmental Science
• Operations Research
• Supply Chain Management

Background:

• Urban waste generation poses significant environmental and logistical challenges.
• Reverse logistics (RL) offers a strategic framework for waste management and product recovery.
• Electronic waste (e-waste) presents unique recovery complexities due to its composition and rapid obsolescence.

Purpose of the Study:

• To develop an integrated fuzzy reverse logistics (RL) model for urban electronic waste (e-waste) product recovery.
• To incorporate buyback (BB) offers contingent on the condition of used products (UPs) upon return.
• To address the inherent uncertainty in return rates and network operations within RL systems.

Main Methods:

• Development of an integrated fuzzy RL model incorporating buyback offers.
• Introduction of a novel fuzzy probability function to estimate the return rate of used products (UPs).
• Application of a fuzzy credibility-based method to manage uncertainty in the RL network.
• Optimization of collection center location, allocation, and product flow, alongside determining optimal collection quantities and BB offers.

Main Results:

• The proposed fuzzy RL model effectively manages urban e-waste recovery.
• The model successfully integrates buyback offers based on used product condition.
• The fuzzy probability function accurately approximates uncertain return rates.
• The fuzzy credibility-based method enhances the robustness of the RL network.
• The case study in Mashhad city validated the model's practical utility and effectiveness.

Conclusions:

• The integrated fuzzy RL model provides a robust solution for urban e-waste management.
• The model's ability to handle uncertainty and incorporate flexible buyback strategies is crucial for successful product recovery.
• The findings demonstrate the potential for optimizing collection networks and maximizing resource recovery in metropolitan areas.