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Probabilistic Envelope Constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme

Chun Peng (chun.peng***at***hec.ca)
Erick Delage (erick.delage***at***hec.ca)
Jinlin Li (jinlinli***at***bit.edu.cn)

Abstract: This paper considers a multiperiod Emergency Medical Services (EMS) location problem and introduces two two-stage stochastic programming formulations that account for uncertainty about emergency demand. While the first model considers both a constraint on the probability of covering the realized emergency demand and minimizing the expected cost of doing so, the second one employs probabilistic envelope con- straints which allow us to control the degradation of coverage under the more severe scenarios. These models give rise to large mixed-integer programs, which can be tackled directly or using a conservative approximation scheme. For the former, we implement the Branch-and-Benders-Cut method, which improves significantly the solution time when compared to a state-of-the art Branch-and-Bound algorithm proposed in the recent literature and to using the CPLEX solver. Finally, a practical study is conducted using historical data from Northern Ireland Ambulance Service and sheds some light on optimal EMS location configuration for this region and necessary trade-offs that must be made between emergency demand coverage and expected cost. These insights are confirmed through an out-of-sample performance analysis.

Keywords: Two-stage chance-constrained stochastic programming, probabilistic envelope constraint, Emergency Medical Services, Branch-and-Benders-Cut, dynamic ambulance location, time-dependent uncertainty

Category 1: Applications -- OR and Management Sciences (Transportation )

Category 2: Applications -- OR and Management Sciences (Production and Logistics )

Category 3: Stochastic Programming

Citation: C. Peng, E. Delage, J. Li, Probabilistic Envelope Constrained Multiperiod Stochastic Emergency Medical Services Location Model and Decomposition Scheme, working paper, 2019

Download: [PDF]

Entry Submitted: 07/27/2018
Entry Accepted: 07/28/2018
Entry Last Modified: 07/31/2019

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