The global demographic shift towards an ageing population and emerging pandemics such as COVID-19 have increased demand for Home Health Care (HHC) services, thereby extending medical waste management challenges from healthcare facilities to residential homes. This study proposes a multi-vehicle, multi-trip, smart capacitated vehicle routing model with time windows and threshold waste levels to optimise HHC waste collection. The model integrates the social, environmental, and economic dimensions of sustainability, leverages Internet of Things (IoT) technology for real-time waste-level monitoring, and incorporates real-time traffic conditions to enhance routing accuracy. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within the Distributed Evolutionary Algorithms in Python framework, we evaluated the model across small, medium, large, and extra-large datasets for both electric vehicle (EV) and internal combustion engine (ICE) scenarios. Results reveal significant sustainability trade-offs: for example, in the small dataset, ICE scenarios showed moderate-to-strong negative correlations between economic-environmental (-0.69) and environmental-social objectives (-0.54). EV deployment achieved zero emissions but increased travel time approximately 20% due to charging requirements, with strong negative correlations between economic-social objectives across all datasets (-0.95 for small, -0.98 for medium, -0.91 for large and extra-large datasets). The NSGA-II algorithm delivered near-optimal solutions in under one minute across all datasets. These findings provide critical insights for HHC waste management optimisation, highlighting inherent trade-offs between environmental sustainability, operational costs, and service quality.
Sar et al. (Tue,) studied this question.