Abstract
A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are distributed over the entire geographic area during the initialization phase, while the genetic algorithm finds good parameter settings in the route construction phase that follows. Computational results on a standard set of problems are also reported.
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S.C. Ahalt, A.K. Krishnamurthy, P. Chen, and D.E. Melton, “Competitive learning algorithms for vector quantization,” Neural Networks, vol. 3, pp. 277–290, 1990.
J.E. Baker, “Reducing bias and inefficiency in the selection algorithm,” in Proceedings of the Second Int. Conf. on Genetic Algorithms, Cambridge, MA, 1987, pp. 14–21.
J.L. Blanton and R.L. Wainwright, “Multiple vehicle routing with time and capacity constraints using genetic algorithms,” in Proceedings of the Fifth International Conference on Genetic Algorithms, Champaign, IL, 1993, pp. 452–459.
G. Clarke and J. Wright, “Scheduling of vehicles from a central depot to a number of delivery points,” Operations Research, vol. 12, pp. 568–581, 1964.
L.J. Fogel, A.J. Owens, and M.J. Walsh, Artificial Intelligence through Simulated Evolution, Wiley: New York, 1966.
S. Grossberg, “Adaptive pattern classification and universal recoding I. Parallel development and coding of neural feature detectors,” Biological Cybernetics, vol. 23, pp. 121–134, 1976.
S. Grossberg, “Adaptive pattern classification and universal recoding II. Feedback, expectation, olfaction, illusions,” Biological Cybernetics vol. 23, pp. 187–202, 1976.
S. Grossberg, “Competitive learning: From interactive activation to adaptive resonance,” Cognitive Science, vol. 11, pp. 23–63, 1987.
J.H. Holland (1975), Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor, Reprinted by MIT Press, 1992.
T. Kohonen, Self Organization and Associative Memory, Second Edition, Springer: Berlin, 1988.
S. Martello and P. Toth, “An algorithm for the generalized assignment problem,” in Operational Research'81: Proceedings of the Ninth IFORS Int. Conf. on Operational Research, edited by J.P. Brans, North Holland, 1981, pp. 589–603.
J.Y. Potvin and J.M. Rousseau, “A parallel route building algorithm for the vehicle routing and scheduling problem with time windows,” European Journal of Operational Research, vol. 66, pp. 331–340, 1993.
D.E. Rumelhart and D. Zipser, “Feature discovery by competitive learning,” Cognitive Science, vol. 9, pp. 75–112, 1985.
M.W.P. Savelsbergh, “An efficient implementation of local search algorithms for constrained routing problems,” European Journal of Operational Research, vol. 47, pp. 75–85, 1990.
M.M. Solomon, “Algorithms for the vehicle routing and scheduling problem with time window constraints,” Operations Research, vol. 35, pp. 254–265, 1987.
M.M. Solomon and J. Desrosiers, “Time window constrained routing and scheduling problems,” Transportation Science, vol. 22, pp. 1–13, 1988.
M.M. Solomon, E.K. Baker, and J.R. Schaffer, “Vehicle routing and scheduling problems with time window constraints: Efficient implementations of solution improvement procedures,” in Vehicle Routing: Methods and Studies, edited by B.L. Golden and A.A. Assad, North Holland: Amsterdam, pp. 85–105, 1988.
P. Thompson and H. Psaraftis, “Cyclic transfer algorithms for multivehicle routing and scheduling problems,” Operations Research, vol. 41, pp. 935–946, 1993.
F. Tillman and T. Cain, “An upper bounding algorithm for the single and multiple terminal delivery problem,” Management Science, vol. 18, pp. 664–682, 1972.
D. Whitley, “The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best,” in Proceedings of the Third Int. Conf. on Genetic Algorithms, Fairfax, VA, 1989, pp. 116–121.
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Potvin, JY., Dubé, D. & Robillard, C. A hybrid approach to vehicle routing using neural networks and genetic algorithms. Appl Intell 6, 241–252 (1996). https://doi.org/10.1007/BF00126629
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DOI: https://doi.org/10.1007/BF00126629