Design of the Reverse Logistics System for Medical Waste Recycling Part I: System Architecture and Disposal Site Selection Algorithm

TL;DR

This paper proposes a comprehensive reverse logistics system architecture for medical waste recycling, focusing on a novel site selection algorithm that reduces costs and improves efficiency, especially during COVID-19.

Contribution

It introduces a new system architecture with a multi-objective optimization model for disposal site selection, integrating clustering and linear programming techniques.

Findings

The site selection method significantly reduces operational costs.

The approach improves waste collection efficiency during epidemics.

Validated with real-world data and case studies.

Abstract

With social progress and the development of modern medical technology, the amount of medical waste generated is increasing dramatically. The problem of medical waste recycling and treatment has gradually drawn concerns from the whole society. The sudden outbreak of the COVID-19 epidemic further brought new challenges. To tackle the challenges, this study proposes a reverse logistics system architecture with three modules, i.e., medical waste classification & monitoring module, temporary storage & disposal site (disposal site for short) selection module, as well as route optimization module. This overall solution design won the Grand Prize of the "YUNFENG CUP" China National Contest on Green Supply and Reverse Logistics Design ranking 1st. This paper focuses on the description of architectural design and the module on site selection. Specifically, regarding system architecture, a framework diagram is provided, together with brief descriptions of the three proposed modules and a case study under the COVID-19 epidemic with the customized model. Regarding the disposal site selection module, a multi-objective optimization model is developed, and considering different types of waste collection sites (i.e., prioritized large collection sites and common collection sites), a hierarchical solution method is developed employing linear programming and K-means clustering algorithms sequentially. The proposed site selection method is verified with a case study using real-world data, and compared with the baseline, it can immensely reduce the daily operational costs and working time. Limited by length, detailed descriptions of the whole system as well as the remaining medical waste classification & monitoring module and route optimization module can be found at https://shorturl.at/cdY59.