Dispatching Fire Trucks under Stochastic Driving Times
Dmitrii Usanov, Peter van de Ven, Rob van der Mei
TL;DR
This paper develops an optimal dispatching model for fire trucks considering stochastic driving times, demonstrating significant improvements over current practices and proposing a practical heuristic for real-world application.
Contribution
It formulates the dispatching problem as a Markov Decision Process and introduces a queueing-based heuristic for large-scale, realistic scenarios.
Findings
Dispatching based on the optimal policy reduces late arrivals by about 20%.
Ignoring driving-time correlation can decrease performance by over 20%.
The proposed heuristic performs close to the optimal policy with less computation.
Abstract
In this paper we discuss optimal dispatching of fire trucks, based on a particular dispatching problem that arises at the Amsterdam Fire Department, where two fire trucks are send to the same incident location for a quick response. We formulate the dispatching problem as a Markov Decision Process, and numerically obtain the optimal dispatching decisions using policy iteration. We show that the fraction of late arrivals can be significantly reduced by deviating from current practice of dispatching the closest available trucks, with a relative improvement of on average about , and over for certain instances. We also show that driving-time correlation has a non-negligible impact on decision making, and if ignored may lead to performance decrease of over in certain cases. As the optimal policy cannot be computed for problems of realistic size due to the computational…
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