Lagrangian dual decomposition for the ambulance relocation and routing considering stochastic demand with the truncated Poisson
Journal
Transportation Research Part B: Methodological
Journal Volume
157
Pages
1-23
Date Issued
2022
Author(s)
Abstract
The pre-hospital Emergency Medical Service (EMS) provides the critical care to the ill or injured patients, and evaluates and manages those patients at scene before their transport to an emergency medical facility. The Time to Arrive at Hospital (TAH) is a useful performance measurement defined as the time interval from the dispatch of an ambulance until the arrival of the patient at the destination facility. By taking into consideration of the short-term demand estimation, there is chance to improve the management of ambulances and reduce the TAH. This study proposes a new stochastic programming model to minimize the TAH within a complete dynamic relocation system. In this system, a truncated Poisson distribution is utilized for forecasting near future EMS requests, and a Lagrangian dual decomposition with branch-and-bound is adapted as the solution methodology. By dynamically generating near future scenarios for the planning of ambulance relocation among bases, we obtain close-to-real-time ambulance relocation decisions. Scenarios collected from New Taipei City, Taiwan have shown that the proposed system has the potential to enhance the performance of the pre-hospital EMS. ? 2022 Elsevier Ltd
Subjects
Ambulance dynamic relocation
Dual decomposition
Emergency Medical Service
Stochastic programming
Ambulances
Branch and bound method
Emergency services
Hospitals
Lagrange multipliers
Poisson distribution
Stochastic models
Stochastic systems
Critical care
Dynamic relocation
Emergency medical services
Lagrangian-dual decompositions
Medical facility
Performance measurements
Routings
Stochastic-demand
SDGs
Type
journal article