A simulation model to enable the optimization of ambulance fleet allocation and base station location for increased patient survival

Abstract

(2015) 'A simulation model to enable the optimization of ambulance eet allocation and base station location for increased patient survival.', European journal of operational research., 247 (1). pp. 294-309. Further information on publisher's website:http://dx.doi.org/10.1016/j.ejor.2015.05.040Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reected in this document. Changes may have been made to this work since it was submitted for publication. A denitive version was subsequently published in European Journal of Operational Research, 247, 1, 2015Research, 247, 1, , 10.1016Research, 247, 1, /j.ejor.2015 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. further improved survival when its location and resourcing were optimized for key periods of service. Also, the removal of a base station from the system was found to have minimal impact on survival probability when the selected station and resourcing were optimized simultaneously.

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

“…In inventory management problems, for instance, we often need to optimize a system modeled through a discrete event simulation to obtain the optimal stocking quantities ( Jalali & Van Nieuwenhuyse, 2015 ). Examples in other fields include supply chain design ( Saif & Elhedhli, 2015 ), pump scheduling ( Naoum-Sawaya, Ghaddar, Arandia, & Eck, 2015 ), and ambulance fleet allocation ( McCormack & Coates, 2015 ).…”

Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

“…In simulation-based optimization, candidates are evaluated on the basis of simulation results, while in pure optimization, these evaluations are done by math expressions. Just recently, simulation-based optimization method has been successfully used to optimize ambulance fleet allocation and base station locations [28], to determine the best design and control factors of a paint shop production line in an automotive company [29], and to decide the best vehicle schedules for housekeeping in a container transshipment terminal [30]. To the extent of our knowledge, this method has not been used in ACT.…”

Chance-Constrained Programming with Decision-Dependent Uncertainty

“…(2) The objective function maximizes the expected number of requests serviced without delay, incentivizing the fastest possible service, and can be viewed as a survival function [23,43]. In the decision-dependent service uncertainty context, it is insufficient to simply maximize the number of requests serviced within the allocated time, since delays can have devastating consequences.…”