Abstract
This paper presents a rescheduling model of a vehicle routing problem when a disruption occurs at a particular time and lasts for a period of time after a subset of the customers has been visited. In such cases, continuing with the original schedule is likely to be infeasible. The rescheduling model taken here is significantly different from the original one due to the fact that the objective is to find a new schedule that minimizes total distance and deviations from the original plan, and that the different neighborhood size and several new constraints must be considered during the recovery procedure. A hybrid algorithm, which is to hybridize ant colony optimization (ACO) with scatter search, is adopted to determine good approximate solutions. Computational experiments were also tested to determine the effects of factors affect the recovery procedure, and our studies will be helpful to disruption management for the vehicle routing problem.
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References
Yu, G., Yang, J.: Optimization Applications in the Airline Industry. In: Du, D.-Z., Pardalos, P.M. (eds.) Handbook of Combinatorial Optimization (Chapter 14), vol. 2, pp. 635–726. Kluwer Academic Publishers, Norwell, MA (1998)
Yang, J., Qi, X.T., Yu, G.: Disruption Management in Production Planning. Naval Research Logistics 52, 420–442 (2005)
Zhu, G., Bard, J.F, Yu, G.: Disruption Management for Resource-constrained Project Scheduling. Operational Research Society 56, 365–381 (2005)
Lee, C.Y., Yu, G.: Single Machine Scheduling under Potential Disruption. Operations Research Letters (2006)
Lee, C.Y., Yu, G.: Parallel-machine Scheduling under Potential Disruption. Optimization Letters (2006)
Swihart, M.R., Papastavrou, J.D.: A Stochastic and Dynamic Model for the Single-vehicle Pick-up and Delivery Problem. European Journal of Operational Research 114, 447–464 (1999)
Genreau, M., Laporte, G., Sequin, R.: A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers. Operations Research 44(3), 469–477 (1996)
Dorigo, M., Gambardella, L.M.: Ant Colonies for the Traveling Salesman Problem. BioSystems 43, 73–81 (1997)
Marti, M., Laguna, M., Glover, F.: Principles of Scatter Search. European Journal of Operational Research 169, 359–372 (2006)
Russell, R.A., Chiang, W.C.: Scatter Search for the Vehicle Routing Problem with Time Windows. European Journal of Operational Research 169, 606–622 (2006)
Greistorfer, P.: A Tabu Scatter Search Metaheuristic for the Arc Routing Problem. Computers and Industrial Engineering 44(2), 249–266 (2003)
Bell, J.E., McMullen, P.R.: Ant Colony Optimization Techniques for the Vehicle Routing Problem. Advanced Engineering Informatics 18, 41–48 (2004)
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© 2007 Springer-Verlag Berlin Heidelberg
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Zhang, X., Tang, L. (2007). Disruption Management for the Vehicle Routing Problem with Time Windows. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_26
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DOI: https://doi.org/10.1007/978-3-540-74282-1_26
Publisher Name: Springer, Berlin, Heidelberg
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