Here you find instance definitions and the best known solutions (to our knowledge) for the 200 customer instances of Homberger's extended VRPTW benchmark. The version reported here has a hierarchical objective: 1) Minimize number of vehicles 2) Minimize total distance. Distance and time should be calculated with double precision, total distance results are rounded to two decimals. Exact methods typically use a total distance objective and use integral or low precision distance and time calculations. Hence, results are not directly comparable.
Here is a zip file with the 200 customer instances.
The C1_2_8 and R1_2_1 entries have been changed to inferior values. The previous entries were based on reports without detailed solutions. Later, it appeared that the solutions are infeasible, see:
Yuichi Nagata, Olli Bräysy, and Wout Dullaert. 2010. A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Comput. Oper. Res. 37, 4 (April 2010), 724-737. DOI=10.1016/j.cor.2009.06.022 http://dx.doi.org/10.1016/j.cor.2009.06.022
Similarly, the original C1_2_9 result of Mester & Bräysy 18/2642.82 turned out to represent an infeasible solution, given the double precision requirement.
The updated entries are based on detailed solutions that have been checked by our solution checker.
B - O. Bräysy, "A Reactive Variable Neighborhood Search Algorithm for the Vehicle Routing Problem with Time Windows," Working Paper, University of Vaasa, Finland, 2001.
BC4, Mirosław BŁOCHO, Zbigniew J. CZECH, "A parallel memetic algorithm for the vehicle routing problem with time windows". 3PGCIC 2013, 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.
BSJ2 - Bjørn Sigurd Johansen, Beskyttet adresse, DSolver version2 05-2005.BVH - R. Bent and P. Van Hentenryck, "A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows," Technical Report CS-01-06, Department of Computer Science, Brown University, 2001.G - Tomasz Gandor: "Approximate Solutions for the Vehicle Routing Problem with Time Windows for a Large Number of Customers". "Systemy Wspomagania Decyzji" (Decision Support Systems), 2008, Zakopane, Poland.GH - H. Gehring and J. Homberger, "A Parallel Two-phase Metaheuristic for Routing Problems with Time Windows," Asia-Pacific Journal of Operational Research, 18, 35-47, (2001).
MB - Mester, D. and O. Bräysy (2005), “Active Guided Evolution Strategies for Large Scale Vehicle Routing Problems with Time Windows”. Computers & Operations Research 32, 1593-1614.
MB2 - Mester, D & O. Bräysy (2012). "A new powerful metaheuristic for the VRPTW”, working paper, University of Haifa, Israel.
PGDR - Eric Prescott-Gagnon, Guy Desaulniers and Louis-Martin Rousseau. A Branch-and-Price-Based Large Neighborhood Search Algorithm for the Vehicle Routing Problem with Time Windows. (2007)
RP S. Ropke & D.Pisinger. "A general heuristic for vehicle routing problems", technical report, Department of Computer Science, University of Copenhagen.
VCGP - T. Vidal, T. G. Crainic, M. Gendreau, C. Prins. "A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows", Computers & Operations Research, Vol. 40, No. 1. (January 2013), pp. 475-489.
Published November 20, 2013