A Multi‐Objective Periodic Vehicle Routing Model for Sustainable Healthcare Waste Collection
- 1Université de Tunis Institut Supérieur de Gestion de Tunis LARODEC Laboratory Tunis Tunisia
Abstract
The collection of infectious waste from healthcare facilities (HCF) constitutes a recurring operational challenge. Indeed, despite the inherent health risks posed by the storage and the transportation of infectious materials, most of the existing waste collection systems tends to prioritise cost optimization and to underaddress the safety dimension. Besides, the collection frequency must be tailored to the waste generation rate of each healthcare facility to avoid delays and inefficiencies. Therefore, it is clear that finding the best collection schedule that guarantees safety within HCF, transportation security, and economic profits is both critical and difficult to achieve. In order to more accurately model the described problem, this study introduces the Sustainable and Load‐dependent Periodic Vehicle Routing Problem (S‐PVRP). The proposed model aims to optimises three objectives: the travelled distance, the transportation risk and the storage risk. Besides, it is designed in a such a way to trigger the collection when a threshold value is reached. To solve the S‐PVRP, we design two solution approaches with very well selected ingredients in order to strike a balance between the exploration and exploitation aspects by reasoning upon multiple objectives. An experimental study is carried out to prove the efficiency of the algorithm on multi‐objective optimization. The algorithm is tested on several benchmark instances and evaluated via several performance metrics. Indeed, we have compared the performance of our algorithm against state‐of‐the‐art algorithms. The experimental results showed that the proposed approach MX‐GAOPT is very competitive and outperforms the competing algorithms in most cases.
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BibTeX
@article{benromdhane2026multiobjective,
title = {{A Multi‐Objective Periodic Vehicle Routing Model for Sustainable Healthcare Waste Collection}},
author = {Hajer Ben‐Romdhane},
journal = {The International Journal of Health Planning and Management},
year = {2026},
month = jul,
doi = {10.1002/hpm.70112},
url = {https://doi.org/10.1002/hpm.70112},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - A Multi‐Objective Periodic Vehicle Routing Model for Sustainable Healthcare Waste Collection
AU - Hajer Ben‐Romdhane
PY - 2026
DA - 2026/07/31
JO - The International Journal of Health Planning and Management
DO - 10.1002/hpm.70112
UR - https://doi.org/10.1002/hpm.70112
AB - The collection of infectious waste from healthcare facilities (HCF) constitutes a recurring operational challenge. Indeed, despite the inherent health risks posed by the storage and the transportation of infectious materials, most of the existing waste collection systems tends to prioritise cost optimization and to underaddress the safety dimension. Besides, the collection frequency must be tailored to the waste generation rate of each healthcare facility to avoid delays and inefficiencies. Therefore, it is clear that finding the best collection schedule that guarantees safety within HCF, transportation security, and economic profits is both critical and difficult to achieve. In order to more accurately model the described problem, this study introduces the Sustainable and Load‐dependent Periodic Vehicle Routing Problem (S‐PVRP). The proposed model aims to optimises three objectives: the travelled distance, the transportation risk and the storage risk. Besides, it is designed in a such a way to trigger the collection when a threshold value is reached. To solve the S‐PVRP, we design two solution approaches with very well selected ingredients in order to strike a balance between the exploration and exploitation aspects by reasoning upon multiple objectives. An experimental study is carried out to prove the efficiency of the algorithm on multi‐objective optimization. The algorithm is tested on several benchmark instances and evaluated via several performance metrics. Indeed, we have compared the performance of our algorithm against state‐of‐the‐art algorithms. The experimental results showed that the proposed approach MX‐GAOPT is very competitive and outperforms the competing algorithms in most cases.
SN - 0749-6753
SN - 1099-1751
ER -