Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

The technological and industrial revolution that the world has witnessed in recent decades has greatly improved the standard of living and simplified daily life. However, this progress has simultaneously led to the emergence of environmental concerns due to high rates of pollution and the mismanagement of waste resulting from industrial processes and healthcare facilities. Recently, waste management has received increasing attention because of the significant damage to natural resources and human health resulting from improper waste handling practices. The primary objective of this study is to address the management of waste generated from medical activities. In response to these challenges, this paper proposes a multi-objective programming model to improve medical waste management using an artificial genetic bee colony algorithm. The main objectives of this model are divided into two parts: first, to optimize the economic costs associated with medical waste management activities across the network, including collection, treatment, recycling, and transportation; secondly, to reduce carbon dioxide emissions resulting from transportation, processing, and recycling operations. By addressing these objectives, the optimized solution demonstrated superior performance compared to the original artificial bee colony algorithm. These results confirm the effectiveness of the proposed approach in enhancing the efficiency and sustainability of medical waste management practices, thereby contributing to the preservation of natural resources and human health.