Evolutionary freight transportation planning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5484 LNCS, Issue , 2009, Pages 768-777

  Weise, Thomas ^a   Podlich, Alexander ^b   Reinhard, Kai ^b   Gorldt, Christian ^c   Geihs, Kurt ^a  

^a UNIVERSITY OF KASSEL   (Germany)

^b Micromata GmbH Kassel   (Germany)

^c Logistik GmbH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FREIGHT CARRIER; INTELLIGENT SEARCH; REAL WORLD DATA; REAL-WORLD SCENARIO; TRANSPORTATION PLANNING;

COMPUTER SCIENCE; EVOLUTIONARY ALGORITHMS; PLANNING; STATISTICAL TESTS;

FREIGHT TRANSPORTATION;

EID: 67650665289     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-01129-0_87     Document Type: Conference Paper

References (26)

1. Bundesministerium für Verkehr, Bau-und Stadtentwicklung: Verkehr in Zahlen 2006/2007. Deutscher Verkehrs-Verlag GmbH, Hamburg, Germany (2006)
2. Ceollo Coello, C.A., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)
3. CEN/TC 119: Swap bodies-non-stackable swap bodies of class C-dimensions and general requirements. EN 284, CEN-CEN ELEC, Brussels, Belgium (2006)
4. Glover, F.: Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13(5), 533-549 (1986) (Pubitemid 17480188)
5. Amberg, A., Domschke, W., Voß, S.: Multiple center capacitated arc routing problems: A tabu search algorithm using capacitated trees. European Journal of Operational Research (EJOR) 124(2), 360-376 (2000) (Pubitemid 30864235)
6. Badeau, P., Gendreau, M., Guertin, F., Potvin, J.Y., Taillard, É.D.: A parallel tabu search heuristic for the vehicle routing problem with time windows. Transportation Research Part C: Emerging Technologies 5(2), 109-122 (1997) (Pubitemid 127427068)
7. Bräysy, O., Gendreau, M.: Tabu search heuristics for the vehicle routing problem with time windows. TOP: An Official Journal of the Spanish Society of Statistics and Operations Research 10(2), 211-237 (2002)
8. Breedam, A.V.: An analysis of the behavior of heuristics for the vehicle routing problem for a selection of problems with vehicle-related, customer-related, and time-related constraints. PhD thesis, University of Antwerp, Belgium (1994)
9. Czech, Z.J., Czarnas, P.: Parallel simulated annealing for the vehicle routing problem with time windows. In: 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing (PDP 2002), pp. 376-383. IEEE Computer Society, Los Alamitos (2002)
10. Bullnheimer, B., Hartl, R.F., Strauss, C.: An improved ant system algorithm for the vehicle routing problem. Annals of Operations Research 89, 319-328 (1999)
11. Doerner, K., Gronalt, M., Hartl, R.F., Reimann, M., Strauss, C., Stummer, M.: Savings Ants for the vehicle routing problem. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol.2279, pp. 11-20. Springer, Heidelberg (2002)
12. Jih, W., Hsu, J.: Dynamic vehicle routing using hybrid genetic algorithms. In: IEEE International Conference on Robotics and Automation, pp. 453-458 (1999)
13. Thangiah, S.R.: Vehicle routing with time windows using genetic algorithms. In: Practical Handbook of Genetic Algorithms: New Frontiers, pp. 253-277. CRC, Boca Raton (1995)
14. Zhu, K.Q.: A diversity-controlling adaptive genetic algorithm for the vehicle routing problem with time windows. In: 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 176-183. IEEE Computer Society, Los Alamitos (2003)
15. Alba, E., Dorronsoro, B.: Solving the vehicle routing problem by using cellular genetic algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol.3004, pp. 11-20. Springer, Heidelberg (2004) (Pubitemid 39737004)
16. Ralphs, T.: Vehicle routing data sets, Data sets (2003) (accessed 2008-10-27), http://www.coin-or.org/SYMPHONY/branchandcut/VRP/data/
17. Pankratz, G., Krypczyk, V.: Benchmark data sets for dynamic vehicle routing problems (2007) (2008-10-27), http://www.fernuni-hagen.de/WINF/inhfrm/ benchmark-data.htm
18. Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Nature-Inspired Algorithms for Optimisation. Springer, Heidelberg (to appear, 2009)
19. Radcliffe, N.J.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10(4), 339-384 (1994) (Pubitemid 24808904)
20. Weise, T.: Global Optimization Algorithms-Theory and Application, 2nd edn. (2009) (accessed 2009-02-10), http://www.it-weise.de/
21. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)
22. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
23. Podlich, A.: Intelligente Planung und Optimierung des Güterverkehrs auf Straße und Schiene mit evolutionären Algorithmen. Master's thesis, Univ. of Kassel (2009)
24. Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. John Wiley & Sons, Chichester (2005)
25. Yates, F.: The Design and Analysis of Factorial Experiments. Imperial Bureau of Soil Science, Commonwealth Agricultural Bureaux, Tech. Comm. No. 35 (1937)
26. Siegel, S., Castellan Jr., N.J.: Nonparametric Statistics for The Behavioral Sciences. Humanities/Social Sciences/Languages. McGraw-Hill, New York (1988)