Ejection chains, reference structures and alternating path methods for traveling salesman problems
Discrete Applied Mathematics - Special volume: first international colloquium on graphs and optimization (GOI), 1992
Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows
Transportation Science
Variable neighborhood search for the dial-a-ride problem
Computers and Operations Research
A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows
Computers and Operations Research
Checking the Feasibility of Dial-a-Ride Instances Using Constraint Programming
Transportation Science
A Hybrid Tabu Search and Constraint Programming Algorithm for the Dynamic Dial-a-Ride Problem
INFORMS Journal on Computing
Simple temporal problems in route scheduling for the dial---a---ride problem with transfers
CPAIOR'12 Proceedings of the 9th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Hybrid column generation and large neighborhood search for the dial-a-ride problem
Computers and Operations Research
An integrated search heuristic for large-scale flexible job shop scheduling problems
Computers and Operations Research
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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.