A Study of Greedy, Local Search, and Ant Colony Optimization Approaches for Car Sequencing Problems

  • Jens Gottlieb
  • Markus Puchta
  • Christine Solnon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)


This paper describes and compares several heuristic approaches for the car sequencing problem. We first study greedy heuristics, and show that dynamic ones clearly outperform their static counterparts. We then describe local search and ant colony optimization (ACO) approaches, that both integrate greedy heuristics, and experimentally compare them on benchmark instances. ACO yields the best solution quality for smaller time limits, and it is comparable to local search for larger limits. Our best algorithms proved one instance being feasible, for which it was formerly unknown whether it is satisfiable or not.


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  1. 1.
    A. Davenport and E.P. K. Tsang. Solving constraint satisfaction sequencing problems by iterative repair. In Proceedings of the First International Conference on the Practical Applications of Constraint Technologies and Logic Programming, 345–357, 1999Google Scholar
  2. 2.
    A. Davenport, E. Tsang, K. Zhu and C. Wang. GENET: a connectionist architecture for solving constraint satisfaction problems by iterative improvement. In Proceedings of AAAI’94, 325–330, 1994Google Scholar
  3. 3.
    M. Dincbas, H. Simonis and P. van Hentenryck. Solving the car-sequencing problem in constraint logic programming. In Proceedings of ECAI-88, 290–295, 1988Google Scholar
  4. 4.
    M. Dorigo and G. Di Caro. The Ant Colony Optimization Meta-Heuristic. In D. Corne, M. Dorigo, and F. Glover (eds.), New Ideas in Optimization, 11–32, McGraw Hill, UK, 1999Google Scholar
  5. 5.
    I.P. Gent. Two Results on Car-sequencing Problems. Technical report APES. 1998Google Scholar
  6. 6.
    I.P. Gent and T. Walsh. CSPLib: a benchmark library for constraints. Technical report APES-09-1999, available from, a shorter version appeared in CP99, 1999
  7. 7.
    J.H.M. Lee, H.F. Leung and H.W. Won. Performance of a Comprehensive and Efficient Constraint Library using Local Search. In Proceedings of 11th Australian Joint Conference on Artificial Intelligence, 191–202, LNAI 1502, Springer, 1998Google Scholar
  8. 8.
    M. Puchta and J. Gottlieb. Solving Car Sequencing Problems by Local Optimization. In Applications of Evolutionary Computing, 132–142, LNCS 2279, Springer, 2002CrossRefGoogle Scholar
  9. 9.
    J.-C. Regin and J.-F. Puget. A Filtering Algorithm for Global Sequencing Constraints. In Principles and Practice of Constraint Programming, 32–46, LNCS 1330, Springer, 1997CrossRefGoogle Scholar
  10. 10.
    B. Smith. Succeed-first or fail-first: A case study in variable and value ordering heuristics. In Third Conference on the Practical Applications of Constraint Technology, 321–330, 1996Google Scholar
  11. 11.
    C. Solnon. Solving Permutation Constraint Satisfaction Problems with Artificial Ants. In Proceedings of ECAI-2000, 118–122, IOS Press, 2000Google Scholar
  12. 12.
    T. Stützle and H.H. Hoos. MAX-MIN Ant System. Journal of Future Generation Computer Systems, Volume 16, 889–914, 2000CrossRefGoogle Scholar
  13. 13.
    E. Tsang. Foundations of Constraint Satisfaction. Academic Press, 1993Google Scholar
  14. 14.
    T. Warwick and E. Tsang. Tackling car sequencing problems using a genetic algorithm. Evolutionary Computation, Volume 3, Number 3, 267–298, 1995CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jens Gottlieb
    • 1
  • Markus Puchta
    • 1
  • Christine Solnon
    • 2
  1. 1.SAP AGWalldorfGermany
  2. 2.LIRIS, NautibusUniversity Lyon IVilleurbanne cedexFrance

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