TY - JOUR
T1 - Simulation–optimization configurations for real-time decision-making in fugitive interception
AU - van Droffelaar, Irene S.
AU - Kwakkel, Jan H.
AU - Mense, Jelte P.
AU - Verbraeck, Alexander
PY - 2024
Y1 - 2024
N2 - Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception. The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time. We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.
AB - Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception. The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time. We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.
KW - Emergency service optimization
KW - Fugitive interception
KW - Real-time optimization
KW - Search problem
KW - Simulation–optimization
UR - http://www.scopus.com/inward/record.url?scp=85188664191&partnerID=8YFLogxK
U2 - 10.1016/j.simpat.2024.102923
DO - 10.1016/j.simpat.2024.102923
M3 - Article
AN - SCOPUS:85188664191
SN - 1569-190X
VL - 133
JO - Simulation Modelling Practice and Theory
JF - Simulation Modelling Practice and Theory
M1 - 102923
ER -