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Risk-averse flexible policy on ambulance allocation in humanitarian operations under uncertainty

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  • Guodong Yu
  • Aijun Liu
  • Huiping Sun

Abstract

Proactive ambulance management is constructive to improve the response efficiency for emergency medical service (EMS) systems under uncertainty. In this paper, we present a dynamic optimisation model concerning the ambulance dispatching and relocation. We develop a flexible operation policy driven by the interval rolling to match vehicles with calls in batch. We formulate the problem in Markov Decision Process and incorporate $M/G/c $M/G/c queues to minimise the average response and delay time. Considering the curse-of-dimensionality, we provide a simulation-based empirical dynamic programming with the state aggregation and post-decision state to solve the model. To further accelerate the computational efficiency, a greedy heuristic method is introduced to improve the quality of sampling operations. Then, a risk-averse model is developed based on the stochastic dominance strategy to improve operational reliability. We develop an equivalent linear programming to evaluate concave dominating functions. We test the performance by a numerical case and extract managerial insights for practitioners. Our results show that the proposed flexible and risk-averse solution outperforms the classic model on reducing the delay under uncertain calls. And the improvement is more active during peak hours, when real-time needs exceed available ambulances.

Suggested Citation

  • Guodong Yu & Aijun Liu & Huiping Sun, 2021. "Risk-averse flexible policy on ambulance allocation in humanitarian operations under uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 59(9), pages 2588-2610, May.
  • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:9:p:2588-2610
    DOI: 10.1080/00207543.2020.1735663
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    Cited by:

    1. Moiz Ahmad & Muhammad Babar Ramzan & Muhammad Omair & Muhammad Salman Habib, 2024. "Integrating Risk-Averse and Constrained Reinforcement Learning for Robust Decision-Making in High-Stakes Scenarios," Mathematics, MDPI, vol. 12(13), pages 1-32, June.

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