Skip to main content
SpringerLink
Log in

Expert Systems for Vehicle Scheduling

  • Chapter
Artificial Intelligence in Operational Research

Abstract

Vehicle scheduling is one of the most commonly occurring problems of transport management. Traditionally, human schedulers tackle these problems, but over the past 30 years a considerable effort has been put into developing computer systems to replace or assist them. Despite this effort, very few organizations routinely use computerized vehicle scheduling. There are several reasons for this, including the difficulty in dealing with real complexity and uncertainty in algorithmic solutions. In recent years, interactive systems have tried to overcome these difficulties by using humans to play a part in the scheduling. A next step would be to include the schedulers’ skills in an expert system. Unfortunately, there is considerable diversity in scheduling problems and little agreement about the facts or rules needed for a knowledge base. At present, some characteristics of a general expert system for vehicle scheduling can be suggested, but several practical difficulties must still be overcome.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. P. Drucker (1962) The economy’s dark continent. Fortune, April 1962.

    Google Scholar 

  2. A. T. Kearney Ltd (1980) Improving Productivity in Physical Distribution. Centre for Physical Distribution Management, London.

    Google Scholar 

  3. B. N. Mckibbin (1984) Distribution’s new network. Manaaement Today. December 1984. 87–96.

    Google Scholar 

  4. J. O’brien (1987) Distribution cost survey. FOCUS on Phys. Distrib. Mamt 6, 3–6.

    MathSciNet  Google Scholar 

  5. R. Ball (1979) The profits of distribution. Management Today, May 1979, 105–120.

    Google Scholar 

  6. D. Firth, F. R. Denham, K. R. Griffin et al. (1980) Distribution Management Handbook. McGraw-Hill, London.

    Google Scholar 

  7. B. Warner (1985) Future trends in the costs of physical distribution. FOCUS on Phys. Distrib. Mpmt 4, 3–9.

    Google Scholar 

  8. B. Warner (1986) The future impact of economic trends on physical distribution. FOCUS on Phys. Distrib. Mgmt 5, 30–40.

    Google Scholar 

  9. G. B. Dantzig and D. R. Fulkerson (1954) Minimising the number of tankers to meet a fixed schedule. Nav. Res. Logist. Q. 1, 217–222.

    Article  Google Scholar 

  10. W. W. Garvin, H. W. Crandall, J. B. John and R. A. Spellman (1957) Applications of linear programming in the oil industry. Mgmt Sci. 3, 407–430.

    Article  MathSciNet  MATH  Google Scholar 

  11. G. B. Dantzig and J. H. Ramser (1959) The truck dispatching problem. Mgmt Sci. 6, 80–91.

    Article  MathSciNet  MATH  Google Scholar 

  12. N. Christofides A. Mingozzt and P. Toth (1981) Exact algorithms for the vehicle routing problem. Math. Prog. 20, 255–282.

    Article  MATH  Google Scholar 

  13. E. J. Marien (1973) The application of linear programming to a distribution system oriented towards service. Int. J. Phys. Distrib. Mat. Mgmt 3, 191–204.

    Google Scholar 

  14. C. D. J. Waters and G. R. Brodie (1988) Realistic sizes for vehicle routing problems. J. Opl Res. Soc. 38, 565–566.

    Article  Google Scholar 

  15. C. D. J. Waters (1988) Expanding the scope of linear programmes to larger vehicle scheduling problems. OMEGA 6, 577–583.

    Article  Google Scholar 

  16. S. A. Cook (1971) The complexity of theorem proving procedures. In Proceedings of the 3rd ACM Symposium on Theory of Computing, 151–158.

    Google Scholar 

  17. R. M. Karp (1975) On the computational complexity of combinatorial problems. Networks 5, 45–68.

    MathSciNet  MATH  Google Scholar 

  18. J. K. Lenstra and A. H. G. Rinnooy Kan (1976) On general routing problems. Networks 6, 273–280.

    Article  MathSciNet  MATH  Google Scholar 

  19. J. K. Lenstra and A. H. G. Rinnooy Kan (1981) Complexity of vehicle scheduling and routing problems. Networks 11, 221–227.

    Article  Google Scholar 

  20. G. N. Frederickson, M. S. Hecht and C. E. King (1978) Approximation algorithms for some routing problems. SIAM J. Comp. 7, 178–193.

    Article  Google Scholar 

  21. C. H. Papadimitriou (1976) On the complexity of edge traversing. J. Assoc. Comp. Mach. 23, 544–554.

    Article  MathSciNet  MATH  Google Scholar 

  22. G. Clarke and J. W. Wright (1964) Scheduling of vehicles from a central depot to a number of delivery points. Opns Res. 12, 568–581.

    Article  Google Scholar 

  23. L. Bodin, B. Golden, A. Assad and M. Ball (1983) Routing and scheduling of vehicles and crews. Comp Opns Res. 10, 63–211.

    Article  MathSciNet  Google Scholar 

  24. M. L. Fisher and R. Jaikumar (1981) A generalised assignment heuristic for vehicle routing. Networks 11, 109–124.

    Article  MathSciNet  Google Scholar 

  25. T. J. Gaskell (1967) Bases for vehicle fleet scheduling. Operat. Res. Quart. 18, 281–295.

    Article  Google Scholar 

  26. P. C. Yellow (1970) A computational modification to the savings method of vehicle scheduling. Opl Res. Q. 21, 281–283.

    Article  Google Scholar 

  27. M. H. J. Webb (1972) Relative performance of some sequential methods of planning multiple delivery journeys. Opl Res. Q. 23, 361–372.

    Article  Google Scholar 

  28. R. H. Mole and S. R. Jameson (1976) A sequential route building employing a generalised savings criterion. Opl Res. Q. 27, 502–511.

    Article  Google Scholar 

  29. G. M. Buxey (1979) The vehicle scheduling problem and Monte Carlo simulation J. Opl Res. Soc. 30, 563–573.

    Article  MATH  Google Scholar 

  30. F. A. Tillman and R. W. Hering (1971) A study of a look-ahead procedure for solving the multiterminal delivery problem. Trans. Res. 5, 225–229.

    Article  Google Scholar 

  31. A. Wren and A. Holliday (1972) Computer scheduling of vehicles from one or more depots to a number of delivery points. Opl Res. Q. 23, 333–344.

    Article  Google Scholar 

  32. S. Eilon (1977) Management perspectives in physical distribution. OMEGA 5, 437–462.

    Article  Google Scholar 

  33. B. E. Gillett and L. R. Miller (1974) A heuristic algorithm for the vehicle-dispatch problem. Opns Res. 22, 340–349.

    Article  MATH  Google Scholar 

  34. B. A. Foster and D. M. Ryan (1976) An integer programming approach to the vehicle scheduling problem. Opl Res. Q. 27, 367–384.

    Article  MathSciNet  MATH  Google Scholar 

  35. I. M. Chehire A. M. Malleson and P. F. Naccache (1982) A dual heuristic for vehicle scheduling. J. Opl Res. Soc. 33, 51–61.

    Article  Google Scholar 

  36. S. Lin (1965) Computer solutions of the travelling salesman problem. Bell Syst. Tech. J. 44, 2245–2269.

    Article  MATH  Google Scholar 

  37. N. Christofides and S. Eilon (1969) An algorithm for the vehicle dispatching problem. Opl Res. Q. 20, 309–318.

    Article  Google Scholar 

  38. S. Lin and B. W. Kernighan (1973) An effective heuristic algorithm for the travelling salesman problem. Opns Res. 21, 498–516.

    Article  MathSciNet  MATH  Google Scholar 

  39. J. E. Beasley (1983) Route first-cluster second methods for vehicle routing. OMEGA 11, 403–408.

    Article  Google Scholar 

  40. C. D. J. Waters (1987) A solution procedure for the vehicle scheduling problem based on iterative route improvement. J. Opl Res. Soc. 38, 833–839.

    Article  MATH  Google Scholar 

  41. C. D. T. Watson-gandy and L. R. Foulds (1981) The vehicle scheduling problem: a survey. New Zealand J. Opns Res. 9, 73–92.

    MathSciNet  Google Scholar 

  42. S. Jones (1970) Computer Load Planning in Physical Distribution Management (F. R. L. Wentworth, Ed.). Gower Press, London.

    Google Scholar 

  43. J. Sussams (1984) Computerised vehicle load planning. J. Opl Res. Soc. 35, 963–966.

    Article  Google Scholar 

  44. C. D. J. Waters (1984) Interactive vehicle routing. J. Opl Res. Soc. 35, 821–826.

    Article  Google Scholar 

  45. F. Cullen, J. Jarvis and D. H. Ratcliff (1981) Set partitioning based heuristics for interactive routing. Networks 11, 125–143.

    Article  MathSciNet  Google Scholar 

  46. G. Mihalcheon, C. Nlcks, J. Pink and C. D. J. Waters (1989) Software Requirements of the Alberta Trucking Industry. Institute for Transportation Studies, University of Calgary, Calgary.

    Google Scholar 

  47. C. D. J. Waters (1989) Developments in the routing and scheduling of road transport vehicles. Presented at the Road and Transport Association of Canada, Annual Conference, Calgary, Alberta September 1989.

    Google Scholar 

  48. R. Greenway (1974) Survey of Vehicle Scheduling Packages. CIDP Publications, London.

    Google Scholar 

  49. E. Rich (1983) Artificial Intelligence. McGraw-Hill, New York.

    Google Scholar 

  50. E. A. Feigenbaum and P. McCorduk (1983) The Fifth Generation — Artificial Intelligence and Japan’s Computer Challenge to the World. Addison-Wesley, New York.

    Google Scholar 

  51. M. Stefik, J. Aikins R. Balzer et al. (1982) The organisation of expert systems: a tutorial. Artificial Intelligence 18, 135–173.

    Article  Google Scholar 

  52. J. F. Sowa (1984) Conceptual Structures: Information Processing in Minds and Machines. Addison-Wesley, Reading, Massachusetts.

    Google Scholar 

  53. P. Ducheessi, S. Belardo and J. P. Seagle (1988) Artificial intelligence and the management science practitioner: knowledge enhancements to a decision support system for vehicle routing. Interfaces 18, 85–93.

    Article  Google Scholar 

  54. S. Belardo, P. Duchessi and J. P. Seagle (1985) Microcomputer graphics in support of vehicle fleet routing. Interfaces 15, 84–92.

    Article  Google Scholar 

  55. J. Mingers and J. Adlam (1989) Where are the ‘real’ expert systems? OR Insight 2, 6–9.

    Article  Google Scholar 

  56. A. A. Assad and B. L. Golden (1986) Expert systems, microcomputers and operations research. Comp. Opns Res. 13, 301–321.

    Article  Google Scholar 

  57. N. Bryant (1988) Managing Expert Systems. Wilev, New York.

    Google Scholar 

  58. Office of Industrial Innovation (1985) Expert Systems. Government of Canada, Ottawa.

    Google Scholar 

  59. A. d’Agapeyeff and C. J. B. Hawkins (1987) The Second Short Survey of Expert Systems in UK Business. Expertech, Slough.

    Google Scholar 

  60. D. Michie (1987) Current developments in expert systems. In Applications of Expert Systems (J. R. Quinlan, Ed.) Turing Institute Press, Glasgow.

    Google Scholar 

  61. M. King (1989) Experiments with experts developing simple expert systems. OMEGA 17, 123–134.

    Article  Google Scholar 

  62. E. Turban and R. Trippi (1989) The utilisation of expert systems in OR/MS: an assessment. OMEGA 17, 311–322.

    Article  Google Scholar 

  63. A. Barr and E. A. Feigenbaum (Eds) (1982) The Handbook of Artificial Intelligence (Vol. 2). Pitman, London.

    Google Scholar 

  64. R. M. O’Keefe (1985) Expert systems and operational research—mutual benefits. J. Opl Res. Soc. 36, 125–129.

    Article  Google Scholar 

  65. B. R. Gaines (1988) Knowledge acquisition for rapid prototyping of expert systems. INFOR 26, 256–285.

    Google Scholar 

  66. J. H. Boose (1985) A knowledge acquisition system for expert systems based on personal construct psychology. Int. J. Man Mach. Studies 20, 21–43.

    Google Scholar 

  67. J. H. Boose (1986) Rapid acquisition and combination of knowledge from multiple experts in the same domain. Future Comp. Syst. 1, 191–216.

    Google Scholar 

  68. M. L. G. Shaw and B. R. Gaines (1986) Interactive elicitation of knowledge from experts. Future Comp. Syst. 1, 151–190.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Management, University of Calgary, Calgary, Alberta, Canada

    C. D. J. Waters

Authors
  1. C. D. J. Waters
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Athens University of Economics and Business, Greece

    Georgios I. Doukidis

  2. London School of Economics and Political Science, UK

    Ray J. Paul

Copyright information

© 1992 Operational Research Society Ltd

About this chapter

Cite this chapter

Waters, C.D.J. (1992). Expert Systems for Vehicle Scheduling. In: Doukidis, G.I., Paul, R.J. (eds) Artificial Intelligence in Operational Research. Palgrave, London. https://doi.org/10.1007/978-1-349-12362-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-349-12362-9_21

  • Publisher Name: Palgrave, London

  • Print ISBN: 978-1-349-12364-3

  • Online ISBN: 978-1-349-12362-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation