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A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing

Author

Listed:
  • Kubra Sar

    (Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland)

  • Pezhman Ghadimi

    (Mechanical and Materials Engineering, University College Dublin, D04 C1P1 Dublin, Ireland)

Abstract

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods : This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

Suggested Citation

  • Kubra Sar & Pezhman Ghadimi, 2024. "A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing," Logistics, MDPI, vol. 8(4), pages 1-21, November.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:119-:d:1523066
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    References listed on IDEAS

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    1. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    2. Hailin Wu & Fengming Tao & Bo Yang, 2020. "Optimization of Vehicle Routing for Waste Collection and Transportation," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
    3. Yong Wang & Shouguo Peng & Kevin Assogba & Yong Liu & Haizhong Wang & Maozeng Xu & Yinhai Wang, 2018. "Implementation of Cooperation for Recycling Vehicle Routing Optimization in Two-Echelon Reverse Logistics Networks," Sustainability, MDPI, vol. 10(5), pages 1-27, April.
    4. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    5. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    6. Çağrı Koç, 2019. "Analysis of vehicle emissions in location-routing problem," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 1-33, March.
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