RESEARCH ARTICLE

Technology and system of constraint programming for industry production scheduling Part I: A brief survey and potential directions

  • Yarong CHEN 1,2 ,
  • Zailin GUAN 3 ,
  • Yunfang PENG , 3 ,
  • Xinyu SHAO 3 ,
  • Muhammad HASSEB 4,5
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  • 1. State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2. Mechanical & Electrical Engineering College, Wenzhou University, Wenzhou 325035, China
  • 3. State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • 4. State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
  • 5. Comsats Institute of Information Technology, Abbottabad 22010, Pakistan

Received date: 12 Apr 2010

Accepted date: 16 May 2010

Published date: 05 Dec 2010

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

Abstract

The use of techniques and system of constraint programming enables the implementation of precise, flexible, efficient, and extensible scheduling systems. It has been identified as a strategic direction and dominant form for the application into planning and scheduling of industrial production. This paper systematically introduces the constraint modeling and solving technology for production scheduling problems, including various real-world industrial applications based on the Chip system of Cosytec Company. We trend of some concrete technology, such as modeling, search, constraint propagation, consistency, and optimization of constraint programming for scheduling problems. As a result of the application analysis, a generic application framework for real-life scheduling based on commercial constraint propagation (CP) systems is proposed.

Cite this article

Yarong CHEN , Zailin GUAN , Yunfang PENG , Xinyu SHAO , Muhammad HASSEB . Technology and system of constraint programming for industry production scheduling Part I: A brief survey and potential directions[J]. Frontiers of Mechanical Engineering, 0 , 5(4) : 455 -464 . DOI: 10.1007/s11465-010-0106-x

Acknowledgments

This research work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 70772056 and 50825503), the State Hi-Tech R&D Program of China (No. 2007AA04Z110), and the Science and Technology Plan Project of Wenzhou (G20090038)
1
Barták R. Constraint programming: in pursuit of the Holy Grail. In: Proceedings of the Week of Doctoral Students, Part IV, Prague, Czech Republic, 1999: 555–564

2
Kumar V. Algorithms for constraint-satisfaction problems: a survey. Artificial Intelligence, 1992, 13(2): 32–44

3
Le Pape C. Constraint-Based Programming for Scheduling: An historical Perspective. Working Paper, Operation Research Society Seminar on Constraint Handling Techniques, London, United Kingdom, 1994

4
Freuder E C. In pursuit of the Holy Grail. Constraints, 1997, 2(1): 57–61

DOI

5
Nuitjen W P M, Aarts E H L. A Computational study of constraint satisfaction for multiple capacitated job-shop scheduling. European Journal of Operational Research, 1996, 90(2): 269–284

DOI

6
Guéret C, Jussien N, Prins C. Using intelligent backtracking to improve branch-and-bound methods: An application to open-shop problems. European Journal of Operational Research, 2000, 127(2): 344–354

DOI

7
Zhang X H, Bard J F. A multi-period machine assignment problem. European Journal of Operational Research, 2006, 170(2): 398–415

DOI

8
Le Pape C. Constraint-based scheduling: a tutorial. http://www.math.unipd.it/%7Efrossi/cp-school/lepape.pdf

9
Bessière C. Constraint Propagation (Ch 3). Rossi F, Van Beek P, Walsh T. Handbook of Constraint Programming. Amsterdam, Elsevier Science Ltd, Boston, 2006

10
Le Pape C. Implementation of resource constraints in ILOG schedule: A library for the development of constraint-based scheduling systems. Intelligent System Engineering, 1994, 3(2): 55–66

DOI

11
Baptiste P, Le Pape C. Disjunctive constraints for manufacturing scheduling: principles and extensions. International Journal of Computer Integrated Manufacturing, 1996, 9(4): 306–310

DOI

12
Dash Optimization Ltd. Xpress-Kalis Reference Manual, 2007

13
ILOG Inc. ILOG Scheduler 6.2 Reference Manual, 2006

14
Dubois D, Fargier H, Prade H. Fuzzy constraints in job-shop scheduling. Journal of Intelligent Manufacturing, 1995, 6(4): 215–234

DOI

15
Barták R. Modelling soft constraints: a survey. Neural Network World, 2002, 12(5): 1–10

16
Sadeh N, Sycara K, Xiong Y L. Backtracking techniques for the job shop scheduling constraint satisfaction problem. Artificial Intelligence, 1995, 76(1-2): 455–480

DOI

17
Stergiou K, Koubarakis M. Backtracking algorithms for disjunctions of temporal constraints. Artificial Intelligence, 2000, 120(1): 81–117

DOI

18
Wu H, Beek P. On universal restart strategies for backtracking search. In: Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming, 2007: 681–695

19
Dcchter R, Meiri I. Experimental evaluation of preprocessing algorithms for constraint satisfaction problems. Artificial Intelligence, 1994, 68(2): 211–241

DOI

20
Minton S, Johnston M D, Philips A B, Laird P. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 1992, 58(1-3): 161–205

DOI

21
Sadeh N, Fox M S. Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem. Artificial Intelligence, 1996, 86(1): l–41

DOI

22
Cheng C C, Smith S F. Applying constraint satisfaction techniques to job shop scheduling. Annual of Operation Resource, 1997, 70: 327–378

DOI

23
Nuijten W P M. Time and resource constrained scheduling: A constraint satisfaction approach, Ph.D. Thesis at Eindhoven University of Technology, 1994

24
Beck J C, Fox M S. Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics. Artificial Intelligence, 2000, 117(1): 31–81

DOI

25
Tsang E. Foundations of Constraint Satisfaction. London: Academic Press , 1993

26
Beck J C. Solution-guided multi-point constructive search for job shop scheduling. Journal of Artificial Intelligence Research, 2007, 29(3): 49–77

27
Watson J P, Beck J C. A hybrid constraint programming/local search approach to the job-shop scheduling problem. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 2008, 5015: 263–277

DOI

28
Baptiste P, Le Pape C. Edge-Finding Constraint Propagation Algorithms for Disjunctive and Cumulative Scheduling. In: Proceedings of the Fifteenth Workshop of the U.K. Planning Special Interest Group, Liverpool, United Kingdom, 1996. Available from http://www.hds.utc.fr/ baptiste/

29
Baptiste P, Le Pape C. A Theoretical and experimental comparison of constraint propagation techniques for disjunctive scheduling. International Joint Conference on Artificial Intelligence, Montreal, Quebec, 1995

30
Laborie P. Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results. Artificial Intelligence, 2003, 143(2): 151–188

DOI

31
Dorndorf U, Pesch E, Phan-Huy T. Solving the open shop scheduling problem. Journal of Schdeuling, 2001, (4): 157–174

32
Jussien N, Lhomme O. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 2002, 139(1): 21–45

DOI

33
Barták R. Practical Constraints: A Tutorial on Modeling with Constraints. 5th Workshop on Constraint Programming for Decision, Gliwice, Poland, 2003: 7–17

34
Law Y C, Lee J H M. Automatic generation of redundant models for permutation constraint satisfaction problems. Journal of Consrtraints, 2007, 12(4): 469–505

DOI

35
Barták R. Theory and practice of constraint propagation. In: Proceedings of the third Workshop on Constraint Programming in Decision and Control, Silesian University, Poland, 2001: 7–14

36
Bessière C, Régin J C, Yap R H C, Zhang Y. An optimal coarse-grained arc consistency algorithm. Artificial Intelligence, 2005, 165(2): 165–185

DOI

37
Brailsford S C, Potts C N, Smith B M. Constraint satisfaction problems: Algorithms and applications. European Journal of Operational Research, 1999, 119(3): 557–581

DOI

38
Bessière C, Debruyne R. Theoretical analysis of singleton arc consistency and its extensions. Artificial Intelligence, 2008, 172(1): 29–41

DOI

39
Baptiste P, Le Pape C, Nuijten W P M. Incorporating efficient operations research algorithms in constraint-based scheduling. In: Proceedings of the First International Joint Workshop on Artificial Intelligence and Operations Research, Timberline Lodge, Oregon, 1995

40
Hooker J N. Logic, optimization and constraint programming. INFORMS Journal on Computing, 2002, 14(4): 295–321

DOI

41
Jain V, Grossmann I E. Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 2001, 13(4): 258–276

DOI

42
Cambazard H, Jussien N. Integrating Benders decomposition within constraint programming. In: Proceedings of CP, Sitges, Spain, 2005, 752–756

43
Milano M, Wallace M. Integrating operations research in constraint programming. Annals of Operations Research, 2005, 4(3): 175–219

44
Timpe C. Solving planning and scheduling problems with combined integer and constraint programming. Operation Research Spectrum, 2002, 24(4): 431–448

45
Jahangirian M, Conroy G V. Intelligent dynamic scheduling system: the application of genetic algorithms. Integrated Manufacturing Systems, 2000, 11(4): 247–257

DOI

46
Loudni S, Boizumault P. Combining VNS with constraint programming for solving anytime optimization problems. European Journal of Operational Research, 2008, 191(3): 705–735

DOI

47
Zupanic D. Optimal solution for a textile production unit. In: Proceedings of the Second International Conference, <month>April</month>1996

48
Freuder G, Wallace M. Constraint technology and the commercial world. IEEE Intelligent Systems, 2000, 15(1): 20–23

DOI

49
Simonis H. Building industrial applications with constraint programming. Principles and Practice of Constraint Programming, 2007, 4741: 271–309

50
Simonis H, Charlier P, Kay P. Constraint handling in an integrated transportation problem. IEEE Intelligent Systems, 2000, 15(1): 26–32

DOI

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