Skip to main content
SpringerLink
Log in

Introduction

  • Editorial Introduction
  • Published:
Constraints Aims and scope Submit manuscript

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Aggoun, A. and Beldiceanu, N. (1993). Extending CHIP in order to solve complex scheduling and placement problems. Mathematical and Computer Modelling, 17: 57-73.

    Google Scholar 

  2. Applegate, D. and Cook, W. (1991). A computational study of the job-shop scheduling instance. ORSA Journal on Computing, 3: 149-156.

    Google Scholar 

  3. Beck, J. C., Davenport, A. J., Davis, E. D., and Fox, M. S. (1998). The ODO project: toward a unified basis for constraint-directed scheduling. Journal of Scheduling, 1(2): 89-125.

    Google Scholar 

  4. Beck, J. C., A. J. Davenport, and M. S. Fox. (1997). Five pitfalls of empirical scheduling research. In Proceedings of Principles and Practice of Constraint Programming, pages 390-404.

  5. Burke, P. and Prosser, P. (1994). The distributed asynchronous scheduler. In M. Zweben and M. Fox (eds.), Intelligent Scheduling. Morgan Kaufmann Publishers, San Francisco, chap. 11, pp. 309-339.

    Google Scholar 

  6. Carlier, J. and Pinson, E. (1989). An algorithm for solving the job-shop problem. Management Science, 35(2): 164-176.

    Google Scholar 

  7. Caseau, Y. and Laburthe, F. (1995). Improving branch and bound for jobshop scheduling with constraint propagation. In Proceedings of the 8th Franco-Japanese Conference CCS'95.

  8. Caseau, Y. and Laburthe, F. (1996). Cumulative scheduling with task intervals. In Proceedings of the Joint International Conference and Symposium on Logic Programming.

  9. Cesta, A. and Oddi, A. (1996). Gaining efficiency and flexibility in the simple temporal problem. In L. Chittaro, S. Goodwin, H. Hamilton, and A. Montanari (eds.), Proceedings of the Third International Workshop on Temporal Representation and Reasoning (TIME-96). Los Alamitos, CA.

  10. Collinot, A. and Le Pape, C. (1987). Controlling constraint propagation. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence.

  11. Dechter, R., Meiri, I., and Pearl, J. (1991). Temporal constraint networks. Artificial Intelligence, 49: 61-95.

    Google Scholar 

  12. Dorndorf, U. and Pesch, E. (1995). Evolution based learning in a job shop scheduling environment. Computers and Operations Research, 22(1): 25-40.

    Google Scholar 

  13. Erschler, J., Roubellat, F., and Vernhes, J. P. (1976). Finding some essential characteristics of the feasible solutions for a scheduling problem. Operations Research, 24: 772-782.

    Google Scholar 

  14. Erschler, J., Roubellat, F., and Vernhes, J. P. (1980). Characterising the set of feasible sequences for n jobs to be carried out on a single machine. European Journal of Operational Research, 4: 189-194.

    Google Scholar 

  15. Ford, Jr., L. R. (1956). Network flow theory. Technical report, Rand Corporation.

  16. Fox, M. S. (1983). Constraint-directed search: a case study of job-shop scheduling. Ph.D. thesis, Carnegie Mellon University, Intelligent Systems Laboratory, The Robotics Institute, Pittsburgh, PA. CMU-RI-TR-85-7.

    Google Scholar 

  17. Fox, M. S. (1990). Constraint-guided scheduling-a short history of research at CMU. Computers in Industry, 14: 79-88.

    Google Scholar 

  18. Garey, M. R., and Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NPCompleteness. W.H. Freeman and Company, New York.

    Google Scholar 

  19. Glover, F. (1989).Tabu search part I. Operations Research Society of America (ORSA) Journal on Computing, 1(3): 109-206.

    Google Scholar 

  20. Glover, F. (1990). Tabu search part II. Operations Research Society of America (ORSA) Journal on Computing, 2(1): 4-32.

    Google Scholar 

  21. Hooker, J. N. (1994). Needed: an empirical science of algorithms. Operations Research, 42: 201-212.

    Google Scholar 

  22. Kowalski, R. (1979). Algorithm D logic C control. Communications of the ACM, 22(7): 424-436.

    Google Scholar 

  23. Laarhoven, P. J. M., Aarts, E. H. L. and Lenstra, J. K. (1992). Job shop scheduling by simulated annealing. Operations Research, 40(1): 113-125.

    Google Scholar 

  24. Latombe, J. (1979). Failure processing in a system for designing complex assemblies. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence.

  25. Le Pape, C. (1988). Des syst`emes d'ordonnancement flexibles et opportunistes. Ph.D. thesis, University Paris XI.

  26. Le Pape, C. (1994a). Constraint-based programming for scheduling: An historical perspective. In Working Papers of the Operations Research Seminar on Constraint Handling Techniques.

  27. Le Pape, C. (1994b). Using a constraint-based scheduling library to solve a specific scheduling problem. In Proceedings of the AAAI-SIGMAN Workshop on Artificial Intelligence Approaches to Modelling and Scheduling Manufacturing Processes.

  28. Le Pape, C. and Baptiste, P. (1996). Constraint propagation techniques for disjunctive scheduling: the preemptive case. In Proceedings of ECAI-96.

  29. Le Pape, C. and Baptiste, P. (1997a). A constraint programming library for preemptive and non-preemptive scheduling. In Proceedings of the Third International Conference and Exhibition on the Practical Application of Constraint Technology, London, United Kingdom, 1997.

  30. Le Pape, C. and Baptiste, P. (1997b). An experimental comparison of constraint-based algorithms for the preemptive job shop scheduling problem. In CP97 Workshop on Industrial Constraint-Directed Scheduling.

  31. Little, J., Murty, K., Sweeney, D., and Karel, C. (1963). An algorithm for the traveling salesman problem. Operations Research, 11(6): 972-989.

    Google Scholar 

  32. Miyashita, K., and Sycara, K. (1995). CABINS:Aframework of knowledge acquisition and iterative revision for schedule improvement and reactive repair. Artificial Intelligence Journal, 76(1-2): 377-426.

    Google Scholar 

  33. Neiman, D. E., Hildum, D. W., Lesser, V. R., and Sandholm, T. W. (1994). Exploiting meta-level information in a distributed scheduling system. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), volume 1, pages 394-400.

    Google Scholar 

  34. Nowicki, E. and Smutnicki, C. (1996). A fast taboo search algorithm for the job shop problem. Management Science, 42(6): 797-813.

    Google Scholar 

  35. Nuijten,W. and Le Pape, C. (1998). Constraint-based job shop scheduling with ILOG scheduler. Journal of Heuristics, 3: 271-286.

    Google Scholar 

  36. Nuijten, W. P. M. (1994). Time and resource constrained scheduling: a constraint satisfaction approach. Ph.D. thesis, Department of Mathematics and Computing Science, Eindhoven University of Technology.

  37. Sadeh, N. (1991). Lookahead techniques for micro-opportunistic job-shop scheduling. Ph.D. thesis, Carnegie-Mellon University. CMU-CS-91-102.

  38. Sadeh, N., and Fox, M. S. (1989). Focus of attention in an activity-based scheduler. In Proceedings of the NASA Conference on Space Telerobotics.

  39. Smith, S.F., Ow, P., Matthys, D., and Potvin, J. (1989). OPIS: An opportunistic factory scheduling system. In Proceedings of International Symposium for Computer Scientists.

  40. Stallman, R. and Sussman, G. (1977). Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9: 135-196.

    Google Scholar 

  41. Steele Jr., G. L. (1980). The definition and implementation of a computer programming language based on constraints. Ph.D. thesis, Massachusetts Institute of Technology.

  42. Van Hentenryck, P. (1989). Constraint Satisfaction in Logic Programming. MIT Press.

  43. Zweben, M., Daun, B., Davis, E., and Deale, M. (1994). Scheduling and rescheduling with iterative repair. In M. Zweben and M. S. Fox (eds.), Intelligent Scheduling. Morgan Kaufmann Publishers, San Francisco, chap. 8, pp. 241-256.

    Google Scholar 

  44. Zweben, M., Davis, E., Daun, B., and Deale, M. (1993). Informedness vs. computational cost of heuristics in iterative repair scheduling. In Proceedings of IJCAI-93, pages 1416-1422.

Download references

Author information

Authors and Affiliations

  1. ILOG S.A., France

    J. Christopher Beck

  2. Enterprise Integration Laboratory, University of Toronto, Canada

    Andrew J. Davenport

  3. Bouygues Telecom, France

    Claude Le Pape

Authors
  1. J. Christopher Beck
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew J. Davenport
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claude Le Pape
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Beck, J.C., Davenport, A.J. & Pape, C.L. Introduction. Constraints 5, 327–334 (2000). https://doi.org/10.1023/A:1009825009635

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1009825009635

Search

Navigation