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Advanced Network Models

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Network Models and Optimization

Part of the book series: Decision Engineering ((DECENGIN))

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Abstract

Everywhere we look in our daily lives, networks are apparent. National highway systems, rail networks, and airline service networks provide us with the means to cross great geographical distances to accomplish our work, to see our loved ones, and to visit new places and enjoy new experiences. Manufacturing and logistics networks give us access to life’s essential foodstock and to consumer products. And computer networks, such as airline reservation systems, have changed the way we share information and conduct our business and personal lives. In all of these problem domains, and in many more, we wish to move some entity (electricity, a product, a person or a vehicle, a message) from one point to another in an underlying network, and to do so as efficiently as possible, both to provide good service to the users of the network and to use the underlying (and typically expensive) transmission facilities effectively. In the most general sense, we want to learn how to model application settings as mathematical objects known as network design models and to study various ways (algorithms) to solve the resulting models [1]. In this chapter, the following advanced network models are introduced as shown in Fig. 9.1.

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References

  1. Ahuj, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network Flows, New Jersey, Prentice Hall.

    Google Scholar 

  2. Abara, J. (1989). Applying integer linear programming to the fleet assignment problem, Interfaces, 19, 20–28.

    Google Scholar 

  3. Subramanian, R., Scheff, R. P., Quillinan, J. D., Wiper, D. S., & Marsten, R. E. (1994). Coldstart: Fleet assignment at Delta Air Lines, Interfaces, 24, 104–120.

    Google Scholar 

  4. Hane, C., Barnhart, C., Johnson, E. L., Marsten, R. E., Nemhauser, G. L., & Sigismondi, G. (1995). The fleet assignment problem: Solving a large-scale integer program, Mathematical Programming, 70, 211–232.

    MathSciNet  Google Scholar 

  5. Rushmeier, R. A., & Kontogiorgis, S. A. (1997). Advances in the optimization of airline fleet assignment, Transportation Science, 31, 159–169.

    MATH  Google Scholar 

  6. Barnhart, C., Johnson, E. L., Nemhauser, G. L., Savelsbergh, M. W. P., & Vance, P. H. (1998). Branch-and-Price: column generation for solving huge integer programs, Operations Research, 46, 316–329.

    MathSciNet  MATH  Google Scholar 

  7. Yan, S. & Tseng, C. H. (2002). A passenger demand model for airline flight scheduling & fleet routing, Computer & Operations Research, 29, 1559–1581.

    Article  MATH  Google Scholar 

  8. Lohatepanont, M. & Barnhart, C. (2004). Airline schedule planning: Integrated models & algorithms for schedule design & fleet assignment, Transportation Science, 38, 19–32.

    Article  Google Scholar 

  9. Desaulniers, G., Desrosiers, J., Dumas, Y., Solomon, M. M., & Soumis, F. (1997). Daily aircraft routing & scheduling, Management Science, 43, 841–855.

    MATH  Google Scholar 

  10. Rexing, B., Barnhartm, C., Kniker, T., Jarrah, A., & Krishnamurthy, N. (2000). Airline fleet assignment with time windows, Transportation Science, 34, 1–20.

    Article  MATH  Google Scholar 

  11. Bandet, P. O. (1994). Armada-Hub: An adaption of AF fleet assignment model to take into account the hub structure in CDG, Proceedings of AGIFORS 34th Annual Symposium.

    Google Scholar 

  12. Desaulniers, G., Deserosiers, J., Solomon, M. M., & Soumis, F. (1994). Daily aircraft routing & scheduling, GERAD technical report, Montreal, Quebec, Canada.

    Google Scholar 

  13. Yan, S. & Young, H. F. (1996). A decision support framework for multi-fleet routing & multishop flight scheduling, Transportation Research, 30, 379–398.

    Google Scholar 

  14. Desrochers, M. & Soumis, F. (1988). A generalized permanent labeling algorithm for the shortest path problem with time windows, Infor, 26, 191–212.

    MATH  Google Scholar 

  15. Ioachim, I., Gelinas, S., Desrosiers, J., & Soumis, F. (1998). A dynamic programming algorithm for the shortest path problem with time windows & linear node costs, Networks, 31, 193–204.

    Article  MathSciNet  MATH  Google Scholar 

  16. Barnhart, C., Kniker, T. S., & Lohatepanont, M. (2002). Itinerary-based airline fleet assignment, Transportation Science, 36, 199–217.

    Article  MATH  Google Scholar 

  17. Lee, L. H., Lee, C. U., & Tan, Y. P. (2007). A multi-objective genetic algorithm for robust flight scheduling using simulation, European Journal of Operational Research, 177, 1948– 1968.

    Article  MathSciNet  MATH  Google Scholar 

  18. Yan, S, Tang, C. H., & Lee, M. C. (2007). A flight scheduling model for Taiwan airlines under market competitions, Omega, 35, 61–74.

    Article  Google Scholar 

  19. Gu, Z., Johnson, L., Nemhauser, G.L. & Wang, Y. (1994). Some properties of the fleet assignment problem, Operations Research Letters, 15(2), 59-71.

    Article  MathSciNet  MATH  Google Scholar 

  20. Sherali, H.D., Bish. E.K., & Zhu. X. (2006). Airline fleet assignment concepts, models, and algorithms, European Journal of Operational Research, 172(1), 1-30.

    Article  MATH  Google Scholar 

  21. Stojkovic, M., & Soumis, F. (2001). An Optimization Model for the Simultaneous Operational Flight and Pilot Scheduling Problem, Management Science, 47(9), 1290-1305.

    Article  Google Scholar 

  22. B´elanger, N., Desaulniers, G., Soumis, F., Desrosiers, J. & Lavigne, J. (2006). Weekly airline fleet assignment with homogeneity, Transportation Research Part B: Methodological, 40(4), 306-318.

    Article  Google Scholar 

  23. B´elanger, N., Desaulniers, G., Soumis, F. & Desrosiers, J. (2006). Periodic airline fleet assignment with time windows, spacing constraints, and time dependent revenues, European Journal of Operational Research, 175(3), 1754-1766.

    Article  MathSciNet  Google Scholar 

  24. Kim, K. H. & Kim, H. (1998). The optimal determination of the space requirement & the number of transfer cranes for import containers, Computers & Industrial Engineering, 35, 427–430.

    Article  Google Scholar 

  25. Bose, J., Reiners, T., Steenken, D., & Voss, S. (2000). Vehicle dispatching at seaport container terminals using evolutionary algorithms, Proceedings of Annual Hawaii International Conference on System Sciences, 2, 1–10.

    Google Scholar 

  26. Zhang, C. Q., Wan, Y. W., Liu, J. Y., & Linn, R. J. (2002). Dynamic crane deployment in container storage yards, Transporation Research, Part B, 36, 537-555.

    Article  Google Scholar 

  27. Chung, R. K., Li, C., & Lin, W. (2002). Interblock Crane Deployment in Container Terminals, Transportation Science, 36, 79–93,

    Article  MATH  Google Scholar 

  28. Linn, R., Liu, J., Wan, Y., Zhang, C., & Murty, K. (2003). Rubber tired gantry crane deployment for container yard operation, Computers & Industrial Engineering, 45, 429–442.

    Article  Google Scholar 

  29. Kim, K. Y. & Kim, K. H. (2003). Heuristic algorithms for routing yard-side equipment for minimizing loading times in container terminals, Naval Research Logistics, 50, 498–514.

    Article  MathSciNet  MATH  Google Scholar 

  30. Ng, W. C. & Mak, K. L. (2005). Yard crane scheduling in port container terminals, Applied Mathematical Modeling, 29, 263–276.

    Article  MATH  Google Scholar 

  31. Ng, W. C. (2005). Crane scheduling in container yards with inter-crane interference, European Journal of Operational Research, 164, 64–78.

    Article  MathSciNet  MATH  Google Scholar 

  32. Kim, K. H. & Kim, K. Y. (1999). An optimal routing algorithm for a transfer crane in port container terminals, Transportation Science, 33, 17–33.

    Article  MATH  Google Scholar 

  33. Kozan, E. & Preston, P. (1999). Genetic algorithms to schedule container transfers at multimodal terminals, International Transactions in Operational Research, 6, 311–329.

    Article  Google Scholar 

  34. Preston, P. & Kozan, E. (2001). An approach to determine storage locations of containers at seaport terminals, Computers & Operations Research, 28, 983–995.

    Article  MATH  Google Scholar 

  35. Kozan, E. & Preston, P. (2006). Mathematical modelling of container transfers & storage locations at seaport terminals, OR Spectrum, 28, 519–537.

    Article  MATH  Google Scholar 

  36. Imai, A., Sasaki, K., Nishimura, E., & Papadimitriou, S. (2006). Multi-objective simultaneous stowage & load planning for a container ship with container rehandle in yard stacks, European Journal of Operational Research, 171, 373–389.

    Article  MATH  Google Scholar 

  37. Kim, K. H. & Park, Y. (2004). A crane scheduling method for port container terminals, European Journal of Operational Research, 156, 752–768.

    Article  MATH  Google Scholar 

  38. Han, M., Li, P., & Sun, J. (2006). The algorithm for berth scheduling problem by the hybrid optimization strategy GASA, Proceedings of 9th International Conference on Control, Automation, Robotics & Vision, 1–4.

    Google Scholar 

  39. Jung, S. H. & Kim, K. H. (2006). Load scheduling for multiple quay cranes in port container terminals, Jorunal of Intellignet & Manufacturing, 17, 479–492.

    Article  Google Scholar 

  40. Zhou, P. F., Kang, H. G., & Lin, L. (2006). A dynamic berth allocation model based on stochastic consideration, Proceedings of the Sixth World Congress on Intelligent Control & Automation, 7297–7301.

    Google Scholar 

  41. Imai, A., Nishimura, E., Hattori, M., & Papadimitriou, S. (2007). Berth allocation at indented berths for mega-containerships, European Journal of Operational Research, 179, 579–593.

    Article  MATH  Google Scholar 

  42. Hansen, P., Oguz, C., & Mladenovic, N. (2003). Variable neighborhood search for minimum cost berth allocation, European Journal of Operational Research, In Press.

    Google Scholar 

  43. Imai, A., Chen, H., Nishimura, E., & Papadimitriou, S. The simultaneous berth & quay crane allocation problem, Transportation Research, PartE, Logistics & Transportation Review, In Press.

    Google Scholar 

  44. Lee, D., Wang, H., & Miao, L. (2008). Quay crane scheduling with non-interference constraints in port container terminals, Transportation Research Part E: Logistics & Transportation Review, 44, 124–135.

    Article  Google Scholar 

  45. Wen, S. Q. & Zhou, P. F. (2007). A container vehicle routing model with variable traveling time, Proceedings of 2007 IEEE International Conference on Automation & Logistics, 2243– 2247.

    Google Scholar 

  46. Zeng, Q. C. & Yang, Z. Z. (2006). A bi-level programming model & its algorithm for scheduling at a container terminal, Proceedings of International Conference on Management Science & Engineering, 402–406.

    Google Scholar 

  47. Zhou, P. F., Kang, H. G., & Lin, L. (2006). A fuzzy model for scheduling equipments handling outbound container in terminal, Proceedings of the Sixth World Congress on Intelligent Control & Automation, 7267–7271.

    Google Scholar 

  48. Lau, H. Y. K. & Zhao, Y. Integrated scheduling of handling equipment at automated container terminals, International Journal of Production Economics, In Press.

    Google Scholar 

  49. Gen, M. & Cheng, R. (1997). Genetic Algorithms & Engineering Design, New York: John Wiley & Sons.

    Google Scholar 

  50. Gen, M. & Cheng, R. (2000). Genetic Algorithms & Engineering Optimization, New York: John Wiley & Sons.

    Google Scholar 

  51. Holland, J. (1992). Adaptation in Natural & Artificial System, University of Michigan Press, Ann Arbor, MI, 1975; MIT Press, Cambridge, MA.

    Google Scholar 

  52. Hwang, H., Moon, S., & Gen, M. (2002). An integrated model for the design of end-of-aisle order picking system & the determination of unit load sizes of AGVs, Computer & Industrial Engineering, 42, 294–258.

    Google Scholar 

  53. Kim, D. B. & Hwang, H. (2001). A dispatching algorithm for multiple-load AGVs using a fuzzy decision-making method in a job shop environment, Engineering Optimization, 33, 523–547.

    Article  Google Scholar 

  54. Kim, S. H. & Hwang, H. (1999). An adaptive dispatching algorithm for automated guided vehicles based on an evolutionary process, Internatioanl Journal of Production Economics, 60–61, 465–472.

    Google Scholar 

  55. Kim, K., Yamazaki, G., Lin, L., & Gen, M. (2004). Network-based Hybrid Genetic Algorithm to the Scheduling in FMS environments, Journal of Artificial Life & Robotics, 8(1), 67–76.

    Article  Google Scholar 

  56. Le-Anh, T. & Koster, D. (2006). A review of design & control of automated guided vehicle systems, European Journal of Operational Research, 171(11), 1–23.

    Article  MathSciNet  MATH  Google Scholar 

  57. Lin, J. K. (2004). Study on Guide Path Design & Path Planning in Automated Guided Vehicle System, PhD Thesis, Waseda University.

    Google Scholar 

  58. Moon, S. W. & Hwang, H. (1999). Determination of unit load sizes of AGV in multiproduct multi-line assembly production systems, International Journal of Production Research, 37(15), 3565–2581.

    Article  MATH  Google Scholar 

  59. Naso, D. & Turchiano, B. (2005). Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence, IEEE Transactions on System Man & Cybernetics, part B, 35(2), 208–226.

    Article  Google Scholar 

  60. Proth, J., Sauer, N., & Xie, X. (1997). Optimization of the number of transportation devices in a flexible manufacturing system using event graphs, IEEE Transactions On Industrial Electronics, 44(3), 298–306.

    Article  Google Scholar 

  61. Qiu, L., Hsu, W., Huang, S., & Wang, H. (2002). Scheduling & routing algorithms for AGVs: a survey, Internatioanl Journal of Production Research, 40(3), 745–760.

    Article  MATH  Google Scholar 

  62. Vis, Iris F. A. (2004). Survey of research in the design & control of automated guided vehicle systems, European Journal of Operational Research, 170(3), 677–709.

    Article  Google Scholar 

  63. Yang, J. B. (2001). GA-Based Discrete Dynamic Programming Approach for Scheduling in FMS Environment, IEEE Transactions on System Man & Cybernetics, part B, 31(5), 824– 835.

    Article  Google Scholar 

  64. Gen, M., Cheng, R., & Oren, S. S. (2001). Network Design Techniques using Adapted Genetic Algorithms, Advances in Engineering Software, 32(9), 731–744.

    Article  MATH  Google Scholar 

  65. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs, Numerische Mathematik, 1(2), 269–271.

    Article  MathSciNet  MATH  Google Scholar 

  66. Hart, E. P., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systtem Science Cybernetics, SSC-4(2), 100– 107.

    Article  Google Scholar 

  67. Chakrabory, B., Maeda, T., & Charkrabory, G. (2005). Multiobjective Route Selection for Car Navigation System using Genetic Algorithm, Proceedings of IEEE Workshop on Soft Computing, 190–195.

    Google Scholar 

  68. Kanoh, H. (2007). Dynamic route planning for car navigation systems using virus genetic algorithms, International Journal of Knowledge-Based & Intelligent Engineering Systems, 11(1), 65–78.

    Google Scholar 

  69. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast & Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182– 197.

    Article  Google Scholar 

  70. Gen, M., Wen, F. & Ataka, S. (2007). Intelligent Approach to Car Navigation System for ITS in Japan, Proceedings of International Conference on Computers, Communication & Systems, 19–26.

    Google Scholar 

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(2008). Advanced Network Models. In: Network Models and Optimization. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84800-181-7_9

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  • DOI: https://doi.org/10.1007/978-1-84800-181-7_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-180-0

  • Online ISBN: 978-1-84800-181-7

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