A Local Search Q-learning Based Approach for Multi-period Vehicle Redeployment

Abstract

The problem of vehicle redeployment in multi-period systems is studied in this paper. It is a sequential decision making problem that involves optimizing an objective over some planning horizon composed of multiple periods. In this work, the problem is handled with a hybrid local search and Q-learning algorithm. It integrates local search into the Dyna-Q algorithm and combines the system’s tactical and operational decision levels to find a multi-period policy for sharing vehicles between stations while optimizing the objective. Tests were carried out on synthetic and real instances, and the obtained results show how local search integration can improve the basic Dyna-Q algorithm.