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Time-dependent MEXCLP with start-up and relocation cost

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  • van den Berg, Pieter L.
  • Aardal, Karen

Abstract

In this paper we introduce a time-dependent probabilistic location model for Emergency Medical Service (EMS) vehicles. The goal is to maximize the expected coverage throughout the day and at the same time minimize the number of opened facilities and the number of relocations. We apply our model to both a randomly generated test instance and to data from the city of Amsterdam, the Netherlands. We see that time-dependent models can result in better solutions than time-independent models. Furthermore, we see that the current set of base locations in Amsterdam is not optimal. We can obtain higher coverage with even less base locations.

Suggested Citation

  • van den Berg, Pieter L. & Aardal, Karen, 2015. "Time-dependent MEXCLP with start-up and relocation cost," European Journal of Operational Research, Elsevier, vol. 242(2), pages 383-389.
    • Handle: RePEc:eee:ejores:v:242:y:2015:i:2:p:383-389
    DOI: 10.1016/j.ejor.2014.10.013
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    References listed on IDEAS

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    Cited by:

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    2. KC, Kiran & Corcoran, Jonathan & Chhetri, Prem, 2020. "Measuring the spatial accessibility to fire stations using enhanced floating catchment method," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    3. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    4. Caio Vitor Beojone & Regiane Máximo de Souza & Ana Paula Iannoni, 2021. "An Efficient Exact Hypercube Model with Fully Dedicated Servers," Transportation Science, INFORMS, vol. 55(1), pages 222-237, 1-2.
    5. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    6. Enayati, Shakiba & Mayorga, Maria E. & Rajagopalan, Hari K. & Saydam, Cem, 2018. "Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers," Omega, Elsevier, vol. 79(C), pages 67-80.
    7. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    8. Pieter L van den Berg & Peter Fiskerstrand & Karen Aardal & Jørgen Einerkjær & Trond Thoresen & Jo Røislien, 2019. "Improving ambulance coverage in a mixed urban-rural region in Norway using mathematical modeling," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-14, April.
    9. Wajid, Shayesta & Nezamuddin, N., 2023. "Capturing delays in response of emergency services in Delhi," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    10. Suriyaphong Nilsang & Chumpol Yuangyai & Chen-Yang Cheng & Udom Janjarassuk, 2019. "Locating an ambulance base by using social media: a case study in Bangkok," Annals of Operations Research, Springer, vol. 283(1), pages 497-516, December.
    11. Thije van Barneveld, 2016. "The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 370-384, May.
    12. Wang, Wei & Wu, Shining & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2021. "Emergency facility location problems in logistics: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    13. Ranon Jientrakul & Chumpol Yuangyai & Klongkwan Boonkul & Pakinai Chaicharoenwut & Suriyaphong Nilsang & Sittiporn Pimsakul, 2022. "Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok," Sustainability, MDPI, vol. 14(16), pages 1-15, August.

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