Abstract
In this paper, we present the freight transportation planning component of the INWEST project. This system utilizes an evolutionary algorithm with intelligent search operations in order to achieve a high utilization of resources and a minimization of the distance travelled by freight carriers in real-world scenarios. We test our planner rigorously with real-world data and obtain substantial improvements when compared to the original freight plans. Additionally, different settings for the evolutionary algorithm are studied with further experiments and their utility is verified with statistical tests.
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Bundesministerium für Verkehr, Bau- und Stadtentwicklung: Verkehr in Zahlen 2006/2007. Deutscher Verkehrs-Verlag GmbH, Hamburg, Germany (2006)
Ceollo Coello, C.A., Lamont, G.B., van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, Heidelberg (2007)
CEN/TC 119: Swap bodies – non-stackable swap bodies of class C – dimensions and general requirements. EN 284, CEN-CEN ELEC, Brussels, Belgium (2006)
Glover, F.: Future paths for integer programming and links to artificial intelligence. Computers & Operations Research 13(5), 533–549 (1986)
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)
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)
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)
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)
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)
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)
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)
Jih, W., Hsu, J.: Dynamic vehicle routing using hybrid genetic algorithms. In: IEEE International Conference on Robotics and Automation, pp. 453–458 (1999)
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)
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)
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)
Ralphs, T.: Vehicle routing data sets, Data sets (2003) (accessed 2008-10-27), http://www.coin-or.org/SYMPHONY/branchandcut/VRP/data/
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
Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Nature-Inspired Algorithms for Optimisation. Springer, Heidelberg (to appear, 2009)
Radcliffe, N.J.: The algebra of genetic algorithms. Annals of Mathematics and Artificial Intelligence 10(4), 339–384 (1994)
Weise, T.: Global Optimization Algorithms – Theory and Application, 2nd edn. (2009) (accessed 2009-02-10), http://www.it-weise.de/
Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Podlich, A.: Intelligente Planung und Optimierung des Güterverkehrs auf Straße und Schiene mit evolutionären Algorithmen. Master’s thesis, Univ. of Kassel (2009)
Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery. John Wiley & Sons, Chichester (2005)
Yates, F.: The Design and Analysis of Factorial Experiments. Imperial Bureau of Soil Science, Commonwealth Agricultural Bureaux, Tech. Comm. No. 35 (1937)
Siegel, S., Castellan Jr., N.J.: Nonparametric Statistics for The Behavioral Sciences. Humanities/Social Sciences/Languages. McGraw-Hill, New York (1988)
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Weise, T., Podlich, A., Reinhard, K., Gorldt, C., Geihs, K. (2009). Evolutionary Freight Transportation Planning. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_87
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DOI: https://doi.org/10.1007/978-3-642-01129-0_87
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