Heuristics for Constraint-Directed Scheduling with Inventory

Despite the importance of the management of inventory in industrial scheduling applications, there has been little research that has addressed reasoning about inventory directly as part of a scheduling problem. In this paper, we represent inventory, inventory storage constraints, and inventory production and consumption in a constraint-directed scheduling framework. Inventory scheduling is then used to investigate heuristic commitment techniques based on the understanding and the exploitation of problem structure. A technique for the estimation of probability of breakage for resource and inventory constraints is presented together with a heuristic commitment technique based on the estimate of constraint criticality. It is empirically demonstrated that a heuristic commitment technique that exploits dynamic constraint criticality achieves superior overall performance.

[1]  Jacek Blazewicz,et al.  The job shop scheduling problem: Conventional and new solution techniques , 1996 .

[2]  Toby Walsh.  Depth-bounded Discrepancy Search , 1997, IJCAI.

[3]  Norman Sadeh,et al.  Look-ahead techniques for micro-opportunistic job shop scheduling , 1992 .

[4]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[5]  Mark S. Fox,et al.  Constraint-Directed Search: A Case Study of Job-Shop Scheduling , 1987 .

[6]  Claude Le Pape,et al.  Implementation of resource constraints in ILOG SCHEDULE: a library for the development of constraint-based scheduling systems , 1994 .

[7]  Helmut Simonis,et al.  Modelling Producer/Consumer Constraints , 1995, CP.

[8]  Toby Walsh,et al.  The Constrainedness of Search , 1996, AAAI/IAAI, Vol. 1.

[9]  Wpm Wim Nuijten,et al.  Time and resource constrained scheduling : a constraint satisfaction approach , 1994 .

[10]  Nicola Muscettola,et al.  Scheduling by iterative partition of bottleneck conflicts , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.

[11]  J. C. Jackson.  Constrainedness and the Phase Transition in Job Shop Scheduling , 1997 .

[12]  S.J.J. Smith,et al.  Empirical Methods for Artificial Intelligence , 1995 .

[13]  J. Christopher Beck,et al.  Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling , 1999 .

[14]  J. Christopher Beck,et al.  THE ODO PROJECT: TOWARD A UNIFIED BASIS FOR CONSTRAINT-DIRECTED SCHEDULING , 1998 .

[15]  Stephen F. Smith,et al.  Applying constraint satisfaction techniques to job shop scheduling , 1997, Ann. Oper. Res..

[16]  Norman M. Sadeh,et al.  Constrained Heuristic Search , 1989, IJCAI.

[17]  J. Christopher Beck,et al.  Texture-Based Heuristics for Scheduling Revisited , 1997, AAAI/IAAI.

[18]  Herbert A. Simon,et al.  The Structure of Ill-Structured Problems , 1977 .

[19]  Olivier Lhomme,et al.  Consistency Techniques for Numeric CSPs , 1993, IJCAI.

[20]  J. Christopher Beck,et al.  Beyond Contention: Extending Texture-Based Scheduling Heuristics , 1997, AAAI/IAAI.

[21]  Matthew L. Ginsberg,et al.  Limited Discrepancy Search , 1995, IJCAI.

[22]  Toby Walsh,et al.  The Hardest Random SAT Problems , 1994, KI.

[23]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .

[24]  J. Carlier,et al.  Adjustment of heads and tails for the job-shop problem , 1994 .

[25]  Claude Le Pape,et al.  Time-versus-Capacity Compromises in Project Scheduling1 , 1994 .