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A Multi-Echelon Network Design in a Dual-Channel Reverse Supply Chain Considering Consumer Preference
1Faculty of Economics and Management, Xi'an University of Technology, Xi'an 710048, China.
Dual-channel reverse supply chains (DRSCs) integrate online and offline recycling. This study models DRSC network design, finding consumer preference positively impacts collection quantity and system profits for economic and environmental sustainability.
Area of Science:
- Operations Research
- Supply Chain Management
- Environmental Sustainability
Background:
- E-commerce growth necessitates integrating online recycling channels with traditional ones, creating dual-channel reverse supply chains (DRSCs).
- DRSCs face challenges in stability and complexity due to factors like consumer preference and service levels, impacting system efficiency.
- Effective network design is crucial for DRSCs to maintain competitiveness and profitability.
Purpose of the Study:
- To develop a quantitative model for designing multi-echelon, dual-objective DRSC networks.
- To incorporate consumer preference for online recycling channels into the network design model.
- To investigate the impact of uncertainty on DRSC network design and find optimal Pareto solutions.
Main Methods:
- Formulated a mixed integer linear programming (MILP) model for DRSC network design.
- Incorporated consumer preference and uncertainty into the MILP model.
- Utilized the ε-constraint method to derive optimal Pareto solutions.
Main Results:
- Consumer preference for online recycling channels positively correlates with total collection quantity.
- Online recycling prices show a positive relationship with system profits.
- The model provides insights into balancing economic and environmental objectives in DRSCs.
Conclusions:
- The proposed MILP model and ε-constraint method effectively address DRSC network design challenges.
- Consumer preference and pricing strategies are key levers for optimizing DRSC performance.
- The findings support recyclers in making informed pricing and service decisions for sustainable operations.
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