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In this study a model for emergency medical services(EMS) in a smart city is proposed to enhance the EMS efficiency by coordinating IoT, Bluetooth technology, geographic information system (GIS), and the scheduling strategy of traveling salesman problem (TSP). The system collects real-time patient data from IoT-enabled medical devices attached with the beneficiaries and uses GIS to optimize the schedule dynamically for the emergency vehicle. The system dynamically collects and analyzes data on patient’s vitals to identify the houses needing medical assistance and the nature of assistance. Then medical assistance team is sent by selecting shortest route through the selected houses to provide medical assistance. Traffic congestion in the route is monitored using GIS facility and the selected route is modified accordingly. Route selection through the selected locations (houses) can be treated as a TSP, where distance between any two locations can be found from GIS. Hence, an efficient and consistent algorithm for the TSPs is suggested and is used to develop a smart EMS with the help of IoT and GIS. in the algorithm, at first a procedure is used to generate a set of potential solutions(Hamiltonian paths through the target houses). Then, another procedure is used to explore the search space properly with the help of some predefined perturbation rules. If a selected rule (randomly selected) for the perturbation of a solution fails to improve the same then K-opt is used for possible enhancement. Another procedure is used for the regeneration of the stagnant solutions to overcome any local optima. The second and third procedures are repeated iteratively for searching the best schedule. The testing of the approach is done using different test instances from the TSPLIB and its efficiency and accuracy for considerably large size TSPs is well established. Using this heuristic, a case study in an urban setting is done to demonstrate the effectiveness of the EMS. The integration of IoT, GIS, and the proposed heuristic for the TSPs not only reduces response time but also enhances overall EMS efficiency, suggesting a promising solution for urban health-care systems aiming to improve emergency response and public health outcomes.

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A Heuristic to Solve the TSPs Using Multiple Perturbation Techniques: An Application to Emergency Medical Service

  • 会议论文

  • 发表日期：

    2025年

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  • Prasanta Dutta,
  • Indadul Khan,
  • Krishnendu Basuli,
  • Manas Kumar Maiti

摘要

In this study a model for emergency medical services(EMS) in a smart city is proposed to enhance the EMS efficiency by coordinating IoT, Bluetooth technology, geographic information system (GIS), and the scheduling strategy of traveling salesman problem (TSP). The system collects real-time patient data from IoT-enabled medical devices attached with the beneficiaries and uses GIS to optimize the schedule dynamically for the emergency vehicle. The system dynamically collects and analyzes data on patient’s vitals to identify the houses needing medical assistance and the nature of assistance. Then medical assistance team is sent by selecting shortest route through the selected houses to provide medical assistance. Traffic congestion in the route is monitored using GIS facility and the selected route is modified accordingly. Route selection through the selected locations (houses) can be treated as a TSP, where distance between any two locations can be found from GIS. Hence, an efficient and consistent algorithm for the TSPs is suggested and is used to develop a smart EMS with the help of IoT and GIS. in the algorithm, at first a procedure is used to generate a set of potential solutions(Hamiltonian paths through the target houses). Then, another procedure is used to explore the search space properly with the help of some predefined perturbation rules. If a selected rule (randomly selected) for the perturbation of a solution fails to improve the same then K-opt is used for possible enhancement. Another procedure is used for the regeneration of the stagnant solutions to overcome any local optima. The second and third procedures are repeated iteratively for searching the best schedule. The testing of the approach is done using different test instances from the TSPLIB and its efficiency and accuracy for considerably large size TSPs is well established. Using this heuristic, a case study in an urban setting is done to demonstrate the effectiveness of the EMS. The integration of IoT, GIS, and the proposed heuristic for the TSPs not only reduces response time but also enhances overall EMS efficiency, suggesting a promising solution for urban health-care systems aiming to improve emergency response and public health outcomes.