Optimizing The Reverse Logistics Network of Returned Electronic Products:  Hybrid Facilities

Proceedings of the 6th International Conference on Advanced Research in Management, Economics and Accounting

Year: 2024

DOI:

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Optimizing The Reverse Logistics Network of Returned Electronic Products:  Hybrid Facilities

Masoud Yavari, Marko Jakšič

 

 

ABSTRACT:

Recent reports highlight a significant rise in global electronic waste, reaching 62 million tonnes in 2022, an 82% increase from 2010, with projections of 82 million tonnes by 2030. This surge is driven by increased electronics consumption and shorter product lifespans. Although Europe has a higher recycling rate of 42.8% compared to the global average of 22.3%, much e-waste is still improperly disposed of. The estimated value of metals in e-waste in 2022 was $91 billion, with only a small fraction recovered through recycling. There’s an urgent need for improved e-waste management, recycling systems, and efforts to promote repair, reuse, and reduce illegal exports to mitigate environmental and health risks. This paper aims to develop a mathematical model for designing a multi-echelon reverse logistics network. Refurbished products are considered in this study. End products are categorized into products with low and high residual value, with sorting costs factored into the decision-making process. A mixed integer linear programming approach is employed to formulate and solve the problem, using mobile phones and digital cameras as case studies for validation. The purpose of this model is to optimize the arrangement of facilities required in reverse logistics. In addition, the routing and inventory of the facilities are also taken into account concerning the optimal location. The results provide valuable insights into reverse logistics network design decisions about the arrangement of facilities and emphasize the importance of product-specific considerations in reverse logistics.

keywords: Reverse logistics, Network design, Refurbished Product, Location-routing-inventory problem