Table 3

Overview of simulation models in patient flow logistics

ReferenceFocusCriteriaMethodNo. ScenariosProblem
Carr et al. (2016)Effectiveness + efficiencyMortality rate, no. trauma centersMass balance model100 (4 disaster impacts, 25 cities)Allocation
Mills et al. (2018)EfficiencyExpected discounted throughput (+ reward function)Markov decision process300Routing
Mas et al. (2022)EfficiencyThroughput (no. patients transported)Agent-based model3 disaster scenariosAllocation
Su and Jin (2008)EfficiencyTotal transportation timeDiscrete event + system dynamics6 (2 disaster types, 3 inter-arrival times)Facility location
Christie and Levary (1998)EfficiencyTotal transportation timeDiscrete event15 (3 disaster scenarios, 5 inter-arrival times)Resource allocation
Çağlayan and Satoglu (2022)Effectiveness + efficiencyCasualties (min), total time to hospital (min)Discrete event16 scenariosAllocation (victims to hospitals + ambulances)
Shin and Lee (2020)EffectivenessExpected number of survivors (max)Markov decision process2×2 = 4 scenarios + historical caseResource allocation
Hager et al. (2024)EfficiencyWaiting time (min), makespan (min); number of casualties (min)Discrete event12 scenariosAllocation (victims - hospitals)
Fidanova et al. (2024)Effectiveness + efficiencyUnserved patientsNot defined3 hazard scenariosAllocation
Source(s): Authors’ own creation

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