A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
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
Computers and Operations Research
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
Drive: Dynamic Routing of Independent Vehicles
Operations Research
The Shortest Path Problem with Time Windows and Linear Waiting Costs
Transportation Science
Solving a Practical Pickup and Delivery Problem
Transportation Science
A Branch-and-Cut Algorithm for the Dial-a-Ride Problem
Operations Research
Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows
Transportation Science
Variable neighborhood search for the dial-a-ride problem
Computers and Operations Research
A heuristic two-phase solution approach for the multi-objective dial-a-ride problem
Networks - Route 2007
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This paper introduces models and algorithms for a static dial-a-ride problem arising in the transportation of patients by non-profit organizations such as the Austrian Red Cross. This problem is characterized by the presence of heterogeneous vehicles and patients. In our problem, two types of vehicles are used, each providing a different capacity for four different modes of transportation. Patients may request to be transported either seated, on a stretcher or in a wheelchair. In addition, some may require accompanying persons. The problem is to construct a minimum-cost routing plan satisfying service-related criteria, expressed in terms of time windows, as well as driver-related constraints expressed in terms of maximum route duration limits and mandatory lunch breaks. We introduce both a three-index and a set-partitioning formulation of the problem. The linear programming relaxation of the latter is solved by a column generation algorithm. We also propose a variable neighborhood search heuristic. Finally, we integrate the heuristic and the column generation approach into a collaborative framework. The column generation algorithm and the collaborative framework provide tight lower bounds on the optimal solution values for small-to-medium-sized instances. The variable neighborhood search algorithm yields high-quality solutions for realistic test instances.