Abstract
This paper considers a container rail service planning problem, in which customer demands are known in advance. The existing rail freight optimisation models are complex and not demand responsive. This paper focuses on constructing profitable schedules, in which service supply matches customer demands and optimises on booking preferences whilst satisfying regulatory constraints. A constraint satisfaction approach is used, in which optimisation criteria and operational requirements are formulated as soft and hard constraints respectively. We present a constraint-based search algorithm capable of handling problems of realistic size. It employs a randomised strategy for the selection of constraints and variables to explore, and uses a predictive choice model to guide and intensify the search within more promising regions of the space. Experimental results, based on real data from the Royal State Railway of Thailand, have shown good computational performance of the approach and suggest significant benefits can be achieved for both the rail company and its customers.
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References
Aardal, K. (1998) Capacitated facility location: separation algorithm and computational experience, Mathematical Programming, 81:149–175.
Abramson, D. and Randall, M. (1999) A simulated annealing code for general integer linear programs, Annals of Operation Research, 86:3–24.
Anderson, P. S., Palma, A. and Thisse, J. (1992) Discrete Choice Theory of Product Differentiation, MIT Press, Princeton, NJ.
Arshad, F., Rhalibi, A. and Kelleher, G. (2000) Information Management within Intermodal Transport Chain Scheduling, European Project PISCES Report, Liverpool John Moores University.
Beasley, J. E. (1988) An algorithm for solving large capacitated warehouse location problems, European Journal of Operational Research, 33:314–325.
Ben-Akiva, M. and Lerman, S. R. (1985) Discrete Choice Analysis: Theory and Application to Predict Travel Demand, MIT Press, Princeton, NJ.
Boffey, T. B. (1989) Location problems arising in computer networks, Journal of the Operational Research Society, 40:347–354.
Connolly, D. (1992) General purpose simulated annealing, Journal of the Operational Research Society, 43:495–505.
Cordeau, J., Toth, P. and Vigo, D. (1998) A survey of optimisation models for train routing and scheduling, Transportation Science, 32:380–404.
Gomes, C. P., Selman, B. and Kautz, H. (1998) Boosting combinatorial search through randomisation. In Proceeding of AAAI-98, AAAI Press, Menlo Park, CA.
Gorman, M. F. (1998) An application of genetic and tabu searches to the freight railroad operating plan problem, Annals of Operations Research, 78:51–69.
Henz, M., Lim, Y. F., Lua, S.C., Shi, X. P., Walser, J. P. and Yap, R. (2000) Solving hierarchical constraints over finite domains. In Proceedings of the 6th International Symposium on Artificial Intelligence and Mathematics (Fort Lauderdale, FL).
Hansen, E.R. (1992) Global Optimisation Using Interval Analysis, Dekker, New York.
Horvitz, E., Ruan, Y., Gomes, C., Kautz, H., Selman, B. and Chickering, M. (2001) A Bayesian approach to tackling hard computational problems. In Proceedings of UAI01, pp. 235–244.
Huntley, C. L., Brown, D. E., Sappington, D. E. and Markowicz, B. P. (1995) Freight routing and scheduling at CSX transportation, Interfaces, 25:58–71.
ILOG (2000) ILOG OPL Studio Version 3.5.1 Reference Manual, ILOG Inc.
Indra-Payoong, N., Srisurapanon, V. and Laosirihongthong, T. (1998) Factors influencing modal choice for freight transportation. In Proceedings of the Civil and Environmental Engineering Conference, New Frontiers and Challenges (Bangkok, Thailand), 419–26.
Johnson, R. and Wichern, D. (1996) Applied Multivariate Statistical Analysis, Cambridge University Press, Cambridge.
Kallenberg, 0. (1997) Foundations of Modern Probability, Springer, New York.
Kearfott, R. B. (1998) On proving existence of feasible points in equality constrained optimisation problems, Mathematical Programming, 83:89–100.
Kochmann, G. A. and McCallum, C. J. (1981) Facility location models for planning a transatlantic communications network, European Journal of Operational Research, 6:205–211.
Kraft, E. R. (2002) Scheduling railway freight delivery appointments using a bid price approach, Transportation Research, 36A:145–165.
Larraaga, P. and Lozano, J. A. (2002) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer, Dordrecht.
Lau H. C., Lim, Y. F. and Liu, Q. (2001) Diversification of neighbourhood via constraint-based local search and its application to VRPTW. In Proceedings of CP-AI-OR, pp. 361–374.
Marder, E. (1997) The Laws of Choice: Predicting Customer Behavior, The Free Press, Simon and Schuster, New York.
Marin, A. and Salmeron, J.(1996) Tactical design of rail freight networks. Part II: local search methods with statistical analysis, European Journal of Operational Research, 94:43–53.
McAllester, D., Selman, B. and Kautz, H. (1997) Evidence for invariants in local search. In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI-97), pp. 459–465.
Ministry of Commerce (2002) Thai Commodity Market Price, Department of Business Economics, Ministry of Commerce, Thailand.
Newman, A. M. and Yano, C. A. (2000) Scheduling direct and indirect trains and containers in an intermodal setting, Transportation Science, 34:256–270.
Nonobe, K. and Ibaraki, T. (1998) A tabu search approach to constraint satisfaction problem as a general problem solver, European Journal of Operational Research, 106:599–623.
Parkes, A. and Walser, J. (1996) Tuning local search for satisfiability testing. In Proceedings of AAAI-96, pp. 356–362.
Patrick, R. (1982) Algorithm AS 181: The W test for normality, Applied Statistics, 31:176–180.
Proll, L. and Smith, B. (1998) ILP and constraint programming approaches to a template design problem, INFORMS Journal of Computing, 10:265–277.
Resende, G. C. and Ribero, C. C. (2001) Greedy Randomized Adaptive Search Procedures, State-of-the Art Handbook in Metaheuristics, F. Glover and G. Kochenberger (Eds.), Kluwer, Dordrecht.
Selman, B., Levesque, H. and Mitchell D. (1992) A new method for solving hard satisfiability problems. In Proceedings of AAAI-92, pp. 440–446.
Selman, B., Kautz, H. and Cohen, B. (1994) Noise strategies for improving local search. In Proceedings of AAAI-94, pp. 337–343.
Shapiro, S. S. and Wilk, M. B. (1965) An analysis of variance test for normality (complete samples), Biometrika, 52:591–611.
Smith, B., Stergiou, K. and Walsh, T. (2000) Using auxiliary variables and implied constraints to model non-binary problems. In Proceedings of AAAI-2000, Austin, TX.
Trotter, H. F. (1959) An elementary proof of the central limit theorem, Archives of Mathematics, 10:226–234.
Walser, J. P. (1999) Integer Optimisation by Local Search: A Domain-Independent Approach, Lecture Notes in Artificial Intelligence, Vol. 1636, Springer, Berlin.
Yano, C. A. and Newman, A. M. (2001) Scheduling trains and containers with due dates and dynamic arrivals, Transportation Science, 35:181–191.
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Indra-Payoong, N., Kwan, R.S.K., Proll, L. (2005). Rail Container Service Planning: A Constraint-Based Approach. In: Kendall, G., Burke, E.K., Petrovic, S., Gendreau, M. (eds) Multidisciplinary Scheduling: Theory and Applications. Springer, Boston, MA. https://doi.org/10.1007/0-387-27744-7_17
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DOI: https://doi.org/10.1007/0-387-27744-7_17
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