Optimizing triple bottom-line objectives for sustainable health-care waste collection and routing by a self-adaptive evolutionary algorithm: A case study from tehran province in Iran
2021
Ghannadpour, Seyed Farid | Zandieh, Fatemeh | Esmaeili, Faraneh
The methods to collect and transport various types of medical waste are vital to the management of healthcare waste. The lack of a safe plan for the timely collection and transportation of such wastes may have an undesirable impact on the environment and public health. The present research addresses a real-life healthcare waste collection vehicle routing problem (HWCVRP) for small medical centers in Iran that produce minor amounts of waste and lack on-site facilities to treat wastes of various kinds. This problem involves social, environmental, and economic objective functions, aiming to achieve sustainable development. The social objective function is optimized based on a novel definition of risk in medical waste collection and designed to reduce public health risk by minimizing the time of waste collection from centers that produce a larger volume of more hazardous waste. Furthermore, the present paper provides a comprehensive estimate of fuel consumption in vehicles to be accurately minimized to diminish the environmental hazards involved in sustainable transportation. Also, the economic objective function is set to minimize the variable and fixed costs of transportation. A self-adaptive multi-objective evolutionary algorithm is generated with numerous effective operators to solve the model. Specific metrics are employed to compare the performance of the proposed meta-heuristic algorithm with those of multiple other evolutionary algorithms. The results reveal the effectiveness of the proposed method in reaching high-quality non-dominated solutions. Ultimately, a real-life case study is used to implement the proposed approach and to evaluate its performance.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS