Constraint-directed techniques for scheduling alternative activities

Artificial Intelligence

Volumn 121, Issue 1, 2000, Pages 211-250

  Beck, J Christopher ^a   Fox, Mark S ^b  

^a IBM   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; CONSTRAINT THEORY; DATA STRUCTURES; DECISION THEORY; HEURISTIC METHODS; PROBLEM SOLVING;

CONSTRAINT-DIRECTED METHODS;

ARTIFICIAL INTELLIGENCE;

EID: 0034250305     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(00)00035-7     Document Type: Article

References (44)

1. Baptiste P., Le Pape C. Disjunctive constraints for manufacturing scheduling: Principles and extensions. Proc. 3rd International Conference on Computer Integrated Manufacturing. 1995.
2. Baptiste P., Le Pape C. Edge-finding constraint propagation algorithms for disjunctive and cumulative scheduling. Proc. 15th Workshop of the UK Planning and Scheduling Special Interest Group. 1996;. Available from http://www.hds.utc.fr/ baptiste/.
3. Baptiste P., Le Pape C., Nuijten W. Incorporating efficient operations research algorithms in constraint-based scheduling. Proc. 1st Joint Workshop on Artificial Intelligence and Operations Research. June 1995;. Workshop proceedings available on world wide web from http://www.cirl.uoregon.edu/aior/.
4. Beck J.C. Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling, Ph.D. Thesis. 1999;University of Toronto.
5. Beck J.C., Davenport A.J., Davis E.D., Fox M.S. The ODO project: Toward a unified basis for constraint-directed scheduling. J. Scheduling. Vol. 1:(2):1998;89-125.
6. J.C. Beck, A.J. Davenport, C. Le Pape, Introduction to the special issue on industrial constraint-directed scheduling, CONSTRAINTS, in press.
7. Beck J.C., Davenport A.J., Sitarski E.M., Fox M.S. Beyond contention: Extending texture-based scheduling heuristics. Proc. AAAI-97, Providence, RI. 1997;AAAI Press, Menlo Park, CA.
8. Beck J.C., Davenport A.J., Sitarski E.M., Fox M.S. Texture-based heuristics for scheduling revisited. Proc. AAAI-97, Providence, RI. 1997;AAAI Press, Menlo Park, CA.
9. Beck J.C., Jackson K. Constrainedness and the phase transition in job shop scheduling, Technical Report. 1997;School of Computing Science, Simon Fraser University, Burnaby, BC.
10. Beck J.C., Fox M.S. Dynamic problem structure analysis as as basis for constraint-directed scheduling heuristics. Artificial Intelligence. Vol. 117:2000;31-81.
11. Brucker P., Thiele O. A branch & bound method for the general-shop problems with sequence dependent set-up times. OR Spektrum. Vol. 18:1996;145-161.
12. Carlier J., Pinson E. An algorithm for solving the job-shop problem. Management Sci. Vol. 35:(2):1989;164-176.
13. Carlier J., Pinson E. Adjustment of heads and tails for the job-shop problem. European J. Oper. Res. Vol. 78:1994;146-161.
14. Carnegie Group Inc., Knowledge-based logistics planning system, Internal Documentation. 1994.
15. Caseau Y., Laburthe F. Improved CLP scheduling with task intervals. Proc. 11th International Conference on Logic Programming. 1994;MIT Press, Cambridge, MA.
16. Caseau Y., Laburthe F. Improving branch and bound for jobshop scheduling with constraint propagation. Proc. 8th Franco-Japanese Conference CCS-95. 1995.
17. Cheng C.C., Smith S.F. Applying constraint satisfaction techniques to job shop scheduling. Ann. Oper. Res. (Special Volume on Scheduling: Theory and Practice). Vol. 70:1997;327-378.
18. Cohen P.R. Empirical Methods for Artificial Intelligence. 1995;MIT Press, Cambridge, MA.
19. Davenport A.J., Beck J.C. An investigation into two approaches for resource allocation and scheduling. INFORMS. 1999.
20. Dubois D., Fargier H., Prade H. The use of fuzzy constraints in job-shop scheduling. Proc. IJCAI-93, Chambéry, France. 1993.
21. Fox M.S. Constraint-directed search: A case study of job-shop scheduling, Ph.D. Thesis. 1983;Carnegie Mellon University, Intelligent Systems Laboratory, The Robotics Institute, Pittsburgh, PA. CMU-RI-TR-85-7.
22. Fox M.S., Sadeh N., Baykan C. Constrained heuristic search. Proc. IJCAI-89, Detroit, MI. 1989;309-316.
23. Garey M.R., Johnson D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness. 1979;W.H. Freeman, New York.
24. Gent I.P., MacIntyre E., Prosser P., Walsh T. The constrainedness of search. Proc. AAAI-96, Portland, OR, Vol. 1. 1996;246-252.
25. Gent I.P., Walsh T. The hardest random SAT problems. Nebel B., Dreschler-Fischer L. Proc. KI-94: Advances in Artificial Intelligence, 18th German Annual Conference on Artificial Intelligence. 1994;355-366 Springer, Berlin.
26. Hildum D.W. Flexibility in a knowledge-based system for solving dynamic resource-constrained scheduling problems, Ph.D. Thesis. 1994;Department of Computer Science, University of Massachusetts, Amherst, MA. UMass CMPSCI TR 94-77.
27. Kott A., Saks V. A multi-decompositional approach to integration of planning and scheduling - An applied perspective. Proc. Workshop on Integrating Planning, Scheduling and Execution in Dynamic and Uncertain Environments, Pittsburgh, PA. June 1998.
28. Le Pape C. Using a constraint-based scheduling library to solve a specific scheduling problem. Proc. AAAI-SIGMAN Workshop on Artificial Intelligence Approaches to Modelling and Scheduling Manufacturing Processes. 1994.
29. Lhomme O. Consistency techniques for numeric CSPs. Proc. IJCAI-93, Chambéry, France, Vol. 1. 1993;232-238.
30. Mittal S., Falkenhainer B. Dynamic constraint satisfaction problems. Proc. AAAI-90, Boston, MA. 1990;25-32.
31. Nuijten W.P.M. Time and resource constrained scheduling: A constraint satisfaction approach, Ph.D. Thesis. 1994;Department of Mathematics and Computing Science, Eindhoven University of Technology.
32. Nuijten W.P.M., Aarts E.H.L., van Arp Taalman Kip D.A.A., van Hee K.M. Randomized constraint satisfaction for job shop scheduling. Proc. IJCAI-93, Chambéry, France. 1993;251-262.
33. Roberts F.S. Applied Combinatorics. 1984;Prentice Hall, Englewood Cliffs, NJ.
34. Ruttkay Z. Fuzzy constraint satisfaction. Proc. 3rd IEEE Conference on Fuzzy Systems. 1994;1263-1268.
35. Sabin M., Freuder E.C. Detecting and resolving inconsistency and redundancy in conditional constraint satisfaction problems. Proc. CP-98 Workshop on Constraint Reformulation. 1998.
36. Sadeh N. Lookahead techniques for micro-opportunistic job-shop scheduling, Ph.D. Thesis. 1991;Carnegie-Mellon University, Pittsburgh, PA. CMU-CS-91-102.
37. Sadeh N., Fox M.S. Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem. Artificial Intelligence. Vol. 86:(1):1996;1-41.
38. Sadeh N.M., Hildum D.W., Laliberty T.J., McA'Nulty J., Kjenstad D., Tseng A. A blackboard architecture for integrating process planning and production scheduling. Concurrent Engineering: Research and Applications. Vol. 6:(2):1998.
39. Saks V. Distribution planner overview, Technical Report. 1992;Carnegie Group, Inc, Pittsburgh, PA.
40. Schiex T., Fargier H., Verfaillie G. Valued constraint satisfaction problems: Hard and easy problems. Proc. IJCAI-95, Montreal, Quebec. 1995.
41. Simon H.A. The structure of ill-structured problems. Artificial Intelligence. Vol. 4:1973;181-200.
42. Smith S.F., Cheng C.C. Slack-based heuristics for constraint satisfaction scheduling. Proc. AAAI-93, Washington, DC. 1993;139-144.
43. Wagner T., Garvey A., Lesser V. Design-to-criteria scheduling: Managing complexity through goal-directed satisficing. Proc. AAAI-97, Providence, RI. 1997.
44. Wagner T., Garvey A., Lesser V. Criteria directed task scheduling. Internat. J. Approx. Reason. Vol. 19:1998;91-118.