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A Genetic Algorithm for Efficient Delivery Vehicle Operation Planning Considering Traffic Conditions

  • Yoong-Seok Yoo
  • Jae-Yearn Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6017)

Abstract

To ensure customer satisfaction, companies must deliver their product safely and within a fixed time. However, it is difficult to determine an inexpensive delivery route when given a number of options. Therefore, an efficient vehicle delivery plan is necessary. Until now, studies of vehicle routes have generally focused on determining the shortest distance. However, vehicle capacity and traffic conditions are also important constraints. We propose using a modified genetic algorithm by considering traffic conditions as the most important constraint to establish an efficient delivery policy for companies. Our algorithm was tested for fourteen problems, and it showed efficient results.

Keywords

Genetic Algorithm Delivery Vehicle Operation Planning 

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References

  1. 1.
    Baker, B.M., Ayechew, M.A.: A Genetic Algorithm for the Vehicle Routing Problem. Computers & Operations Research 30, 787–800 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Cheng, R., Gen, M.: Genetic Algorithm and Engineering Design. John Wiley & Sons, New York (1996)Google Scholar
  3. 3.
    Prins, C.: A Simple and Effective Evolutionary Algorithm for the Vehicle Routing Problem. Computers & Operations Research 31, 1985–2002 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Christofides, N., Eilon, S.: An Algorithm for the Vehicle Dispatching Problem. Operational Research Quarterly 20(3), 309–318 (1969)CrossRefGoogle Scholar
  5. 5.
    Clarke, G., Wright, J.: Scheduling of Vehicles from a Central Depot to a Number of Delivery Points. Operations Research 11(4), 568–581 (1963)Google Scholar
  6. 6.
    Dantzig, G.B., Ramser, J.H.: The Truck Dispatching Problem. Management Science 6(1), 80–91 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Dolce, J.: Fleet Management. McGraw-Hill, New York (1984)Google Scholar
  8. 8.
    Gaskell, T.J.: Bases for Vehicle Fleet Scheduling. Operational Research Quarterly 18(3), 281–295 (1967)CrossRefGoogle Scholar
  9. 9.
    Gendreau, M., Hertz, A., Laporte, G.: A Tabu Search Heuristic for the Vehicle Routing Problem. Management Science 40(10), 1276–1290 (1994)zbMATHCrossRefGoogle Scholar
  10. 10.
    Hayes, R.L.: The Delivery Problem. Carnegie Institute of Technology, Graduate School of Industrial Administration, Pittsburgh, Report No. MSR 106 (1967)Google Scholar
  11. 11.
    Lee, C., Kim, S.: Parallel Genetic Algorithm for the Tardiness Job Scheduling Problem with General Penalty Weights. International Journal of Computers and Industrial Engineering 28, 231–243 (1995)CrossRefGoogle Scholar
  12. 12.
    Toth, P., Vigo, D.: The Vehicle Routing Problem. Society for Industrial and Applied Mathematics, Philadelphia (2002)zbMATHGoogle Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithm + Data Structure = Evolution Programs. Springer, Heidelberg (1996)Google Scholar
  14. 14.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yoong-Seok Yoo
    • 1
  • Jae-Yearn Kim
    • 1
  1. 1.Department of Industrial EngineeringHanyang University, Sungdong-guSeoulKorea

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