Large Neighborhood Search for Dial-a-Ride Problems

摘要

Dial-a-Ride problems (DARPs) arise in many urban transportation applications. The core of a DARP is a pick and delivery routing with multiple vehicles in which customers have ride-time constraints and routes have a maximum duration. This paper considers DARPs for which the objective is to minimize the routing cost, a complex optimization problem which has been studied extensively in the past. State-of-the-art approaches include sophisticated tabu search and variable neighborhood search. This paper presented a simple constraint-based large neighborhood search, which uses constraint programming repeatedly to find good reinsertions for randomly selected sets of customers. Experimental evidence shows that the approach is competitive in finding best-known solutions and reaches high-quality solutions significantly faster than the state of the art.