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Locating emergency services with priority rules: The priority queuing covering location problem

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Abstract

One of the assumptions of the Capacitated Facility Location Problem (CFLP) is that demand is known and fixed. Most often, this is not the case when managers take some strategic decisions such as locating facilities and assigning demand points to those facilities. In this paper we consider demand as stochastic and we model each of the facilities as an independent queue. Stochastic models of manufacturing systems and deterministic location models are put together in order to obtain a formula for the backlogging probability at a potential facility location. Several solution techniques have been proposed to solve the CFLP. One of the most recently proposed heuristics, a Reactive Greedy Adaptive Search Procedure, is implemented in order to solve the model formulated. We present some computational experiments in order to evaluate the heuristics’ performance and to illustrate the use of this new formulation for the CFLP. The paper finishes with a simple simulation exercise.

Suggested Citation

  • Daniel Serra & Francisco Silva, 2002. "Locating emergency services with priority rules: The priority queuing covering location problem," Economics Working Papers 642, Department of Economics and Business, Universitat Pompeu Fabra, revised May 2008.
    • Handle: RePEc:upf:upfgen:642
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    Cited by:

    1. Vatsa, Amit Kumar & Jayaswal, Sachin, 2016. "A new formulation and Benders decomposition for the multi-period maximal covering facility location problem with server uncertainty," European Journal of Operational Research, Elsevier, vol. 251(2), pages 404-418.
    2. Sachin Jayaswal & Navneet Vidyarthi, 2017. "Facility location under service level constraints for heterogeneous customers," Annals of Operations Research, Springer, vol. 253(1), pages 275-305, June.
    3. repec:iim:iimawp:13011 is not listed on IDEAS
    4. Sondes Hammami & Aida Jebali, 2021. "Designing modular capacitated emergency medical service using information on ambulance trip," Operational Research, Springer, vol. 21(3), pages 1723-1742, September.
    5. Knight, V.A. & Harper, P.R. & Smith, L., 2012. "Ambulance allocation for maximal survival with heterogeneous outcome measures," Omega, Elsevier, vol. 40(6), pages 918-926.
    6. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    7. Areej Alhothali & Budoor Alwated & Kamil Faisal & Sultanah Alshammari & Reem Alotaibi & Nusaybah Alghanmi & Omaimah Bamasag & Manal Bin Yamin, 2022. "Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia," IJERPH, MDPI, vol. 19(14), pages 1-21, July.
    8. Jayaswal, Sachin, 2014. "Emergency Medical Service System Design under Service Level Constraints for Heterogeneous Patients," IIMA Working Papers WP2014-11-04, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Vidyarthi, Navneet & Jayaswal, Sachin, 2013. "Efficient Solution of a Class of Location-Allocation Problems with Stochastic Demand and Congestion," IIMA Working Papers WP2013-11-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    10. Ahmadi-Javid, Amir & Hoseinpour, Pooya, 2019. "Service system design for managing interruption risks: A backup-service risk-mitigation strategy," European Journal of Operational Research, Elsevier, vol. 274(2), pages 417-431.
    11. Hoseinpour, Pooya & Ahmadi-Javid, Amir, 2016. "A profit-maximization location-capacity model for designing a service system with risk of service interruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 113-134.

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    More about this item

    Keywords

    Location; queuing; greedy heuristics; simulation;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L80 - Industrial Organization - - Industry Studies: Services - - - General

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