Constraint-Based Scheduling: Applying Constraint Programming to Scheduling ProblemsSpringer Science & Business Media, 31 juil. 2001 - 198 pages Constraint Programming is a problem-solving paradigm that establishes a clear distinction between two pivotal aspects of a problem: (1) a precise definition of the constraints that define the problem to be solved and (2) the algorithms and heuristics enabling the selection of decisions to solve the problem. It is because of these capabilities that Constraint Programming is increasingly being employed as a problem-solving tool to solve scheduling problems. Hence the development of Constraint-Based Scheduling as a field of study. The aim of this book is to provide an overview of the most widely used Constraint-Based Scheduling techniques. Following the principles of Constraint Programming, the book consists of three distinct parts:
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Table des matières
INTRODUCTION | 1 |
12 Scheduling Theory | 9 |
121 Grahams Classification | 10 |
13 A ConstraintBased Scheduling Model | 12 |
132 Temporal Relations | 15 |
134 Extensions of the Model | 16 |
135 Objective Function | 18 |
PROPAGATION OF THE ONEMACHINE RESOURCE CONSTRAINT | 19 |
445 The OneMachine NonPreemptive Case | 101 |
PROPAGATION OF OBJECTIVE FUNCTIONS | 105 |
511 Complexity Results | 106 |
512 A Lower Bound of the Number of Late Activities | 108 |
513 Constraint Propagation | 117 |
52 Sum of Transition Times and Sum of Transition Costs | 122 |
521 Route Optimization Constraint | 124 |
522 Precedence Graph Constraint | 126 |
212 Disjunctive Constraint Propagation | 21 |
213 EdgeFinding | 22 |
214 NotFirst NotLast | 26 |
215 More Propagation | 29 |
22 Preemptive Problems | 31 |
221 TimeTable Constraint | 32 |
222 Disjunctive Constraint Propagation | 33 |
224 Preemptive EdgeFinding | 37 |
PROPAGATION OF CUMULATIVE CONSTRAINTS | 43 |
31 Fully Elastic Problems | 44 |
32 Preemptive Problems | 46 |
33 NonPreemptive Problems | 58 |
332 Disjunctive Constraint | 59 |
334 Extended EdgeFinding | 64 |
335 NotFirst NotLast | 65 |
336 Energetic Reasoning | 67 |
COMPARISON OF PROPAGATION TECHNIQUES | 77 |
411 TimeTable Constraints | 80 |
412 Disjunctive Constraints | 81 |
414 NotFirst NotLast | 83 |
42 Dominance Relations | 85 |
421 The Fully Elastic Case | 87 |
424 The Cumulative NonPreemptive Case | 88 |
425 The OneMachine NonPreemptive Case | 91 |
43 NonDominance Relations | 92 |
431 General Counterexamples | 93 |
432 A OneMachine Preemptive Counterexample | 96 |
44 Summary | 99 |
442 The Cumulative Preemptive Case | 100 |
53 Conclusion | 127 |
RESOLUTION OF DISJUNCTIVE PROBLEMS | 129 |
611 Branching Scheme | 130 |
612 Computational Results | 131 |
62 OpenShop Scheduling | 132 |
623 Computational Results | 133 |
624 Conclusion | 134 |
63 Preemptive JobShop Scheduling | 135 |
631 Dominance Relation | 136 |
632 Branching Scheme | 138 |
633 Heuristic Algorithms | 140 |
634 Limited Discrepancy Search | 142 |
CUMULATIVE SCHEDULING PROBLEMS | 149 |
71 General Framework | 151 |
72 Hybrid FlowShop Scheduling | 154 |
73 ResourceConstrained Project Scheduling | 155 |
74 Conclusion | 156 |
MINSUM SCHEDULING PROBLEMS | 159 |
811 Dominance Relations | 160 |
812 Branching Scheme | 163 |
813 Computational Results | 164 |
82 Minimizing Makespan and Sum of Transition Times | 166 |
822 Problem Solving | 167 |
823 Computational Results | 169 |
CONCLUSION | 175 |
SUMMARY OF NOTATION | 179 |
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Expressions et termes fréquents
A₁ activity A₁ alternative machines applied arc-B-consistency backtracking branch and bound capacity CNP-EF2 CNP-ER-DISJ CNP-NFNL Constraint Programming constraint propagation algorithms Constraint-Based Scheduling corresponding Counterexample CP-DISJ CP-ER CP-TT cumulative deadline deduce defined disjunctive constraint domain dominance relation E(Ai earliest end Edge-Finding eeti end(Ai execute FE-EF FE-TT feasible partially elastic given Hence heuristic instances interrupted interval t1 iteration job Ji Job-Shop Job-Shop Problem Late Jobs latest end LBWS linear linear program lower bound lsti makespan minimal node non-preemptive Not-First Not-Last NP-hard number of late O(n² on-time Open-Shop Operations Research optimal partially elastic schedule precedence graph preemptive schedule proc(Ai processing Proof Proposition r₁ RCPSP release date required energy consumption resource constraint scheduling problems search tree Section set of activities set(Ai solution solved start(Ai subset sum of setup t₁ Time-Table Constraint update upper bound variable Yes Yes
