Risk-averse anticipation for dynamic vehicle routing

Abstract

In the field of dynamic vehicle routing, the importance to integrate stochastic information about possible future events in current decision making increases. Integration is achieved by anticipatory solution approaches, often based on approximate dynamic programming (ADP). ADP methods estimate the expected mean values of future outcomes. In many cases, decision makers are risk-averse, meaning that they avoid “risky” decisions with highly volatile outcomes. Current ADP methods in the field of dynamic vehicle routing are not able to integrate risk-aversion. In this paper, we adapt a recently proposed ADP method explicitly considering risk-aversion to a dynamic vehicle routing problem with stochastic requests. We analyze how risk-aversion impacts solutions’ quality and variance. We show that a mild risk-aversion may even improve the risk-neutral objective.