Here you find instance definitions and the best known solutions (to our knowledge) for the 600 customer instances of Gehring & 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.
Instance definitions (text)
Best known results for Gehring & Homberger's 600 customer instances
The instance names in blue are hyperlinks to files with corresponding detailed solutions. They have all been checked by our solution checker. Note that many best known solutions do not have a reference to a peer reviewed publication. For these, important details on the solution algorithm, the computing time, and the experimental platform are probably not available. Further, there is no guarantee that the solutions have been produced without using external information, such as detailed solutions published earlier. We may later introduce two categories: 'properly published' and 'freestyle', the latter with no restrictions.
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.
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.
CAINIAO - Zhu He, Longfei Wang, Weibo Lin, Yujie Chen, Haoyuan Hu ( ), Yinghui Xu, & VRP Team (Ying Zhang, Guotao Wu, Kunpeng Han et al.). Unpublished work by CAINIAO AI.
EOE - Eirik Krogen Hagen, EOE Koordinering DA. Exploring infeasible and feasible regions of the VRPTW and PDPTW through penalty based tabu search. Working paper.
JG - Jakub Grzegorek (2017). Improved Hybrid Genetic Search with Advanced Diversity Control, forthcoming MEng thesis, Imperial College London.
MB2 - Mester, D & O. Bräysy (2012). "A new powerful metaheuristic for the VRPTW”, working paper, University of Haifa, Israel.
MK - M. Koch, "An approach combining two methods for the vehicle routing problem with time windows", The solutions were presented at EURO and EURO XX Conference 2004.
NBC - Jakub Nalepa, Miroslaw Blocho, and Zbigniew J. Czech. "Co-operation schemes for the parallel memetic algorithm". In Roman Wyrzykowski, Jack Dongarra, Konrad Karczewski, and Jerzy Waniewski, editors, Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science, pages 191–201. Springer Berlin Heidelberg, 2014. ISBN 978-3-642-55223-6. doi: 10.1007/978-3-642-55224-3 19.
NBD - 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.
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)
Q - Quintiq. http://www.quintiq.com/optimization/vrptw-world-records.html .
RP - S. Ropke & D.Pisinger. "A general heuristic for vehicle routing problems", technical report, Department of Computer Science, University of Copenhagen.
SCR - Piotr Sielski (), Piotr Cybula, Marek Rogalski (), Mariusz Kok, Piotr Beling, Andrzej Jaszkiewicz, Przemysław Pełka. Emapa S.A (http://www.emapa.pl), "New methods of VRP problem optimization", unpublished research funded by The National Centre for Research and Development. project number: POIR.01.01.01.-00-0222/16.
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.