An algorithm for solving the job-shop problem
Management Science
Formulating the single machine sequencing problem with release dates as a mixed integer program
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
A practical use of Jackson's preemptive schedule for solving the job shop problem
Annals of Operations Research
A New Approach to Computing Optimal Schedules for the Job-Shop Scheduling Problem
Proceedings of the 5th International IPCO Conference on Integer Programming and Combinatorial Optimization
Solving Project Scheduling Problems by Minimum Cut Computations
Management Science
A random key based genetic algorithm for the resource constrained project scheduling problem
Computers and Operations Research
Solving a Stochastic Queueing Control Problem with Constraint Programming
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Solving a stochastic queueing design and control problem with constraint programming
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A constraint programming approach for solving a queueing control problem
Journal of Artificial Intelligence Research
Event-based MILP models for resource-constrained project scheduling problems
Computers and Operations Research
New concepts for activity float in resource-constrained project management
Computers and Operations Research
Generalized disjunctive constraint propagation for solving the job shop problem with time lags
Engineering Applications of Artificial Intelligence
Lower bounds for the multi-skill project scheduling problem with hierarchical levels of skills
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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We propose a cooperation method between constraint programming and integer programming to compute lower bounds for the resource-constrained project scheduling problem (RCPSP). The lower bounds are evaluated through linear-programming (LP) relaxations of two different integer linear formulations. Efficient resource-constraint propagation algorithms serve as a preprocessing technique for these relaxations. The originality of our approach is to use additionally some deductions performed by constraint propagation, and particularly by the shaving technique, to derive new cutting planes that strengthen the linear programs. Such new valid linear inequalities are given in this paper, as well as a computational analysis of our approach. Through this analysis, we also compare the two considered linear formulations for the RCPSP and confirm the efficiency of lower bounds computed in a destructive way.