Shop and batch scheduling with constraints

This is a summary of the PhD thesis defended by the author in September 2011 at thé Ecole des Mines de Nantes (EMNantes). The thesis was advised by Narendra Jussien (EMNantes) and co-advised by Louis-Martin Rousseau (´ Ecole Polytechnique de Montréal – Poly), Christelle Guéret (EMNantes) and André Langevin (Poly). The manuscript is written in French and can be obtained from The focus of the dissertation is to study the resolution of shop and batch scheduling problems derived from real-world applications using constraint programming. The remainder of this abstract summarizes the results of the dissertation. In shop problems, n jobs, consisting each of m tasks, must be processed on m machines. The processing orders of tasks which belong to a job can vary: global order (flow shop); order per job (job shop); no order (open shop). We consider the construction of non-preemptive schedules of minimal makespan, which is NP-Hard for m ≥ 3. The study and classification of models and search algorithms show that one of the major challenge of solving optimally these problems is to provide good solutions as quick as the metaheuristics. Therefore , we introduce a new algorithm characterized by its: simplicity, most of its components are available in constraint solvers; flexibility, two constraint models offer different trade-offs among propagation strength, speed and simplicity; genericity, it is applicable to any disjunctive scheduling problem. The algorithm relies on recent constraint filtering algorithm and (randomized) branching techniques with new upper bound heuristics. The algorithm starts with a randomized constructive heuristic (without propagation) which initializes the upper bound so that the selection and propagation of initial choices are improved. During the search, the filtering is based on the disjunctive graph model and disjunctive global constraints, whereas the branching is conducted by adding precedences to the disjunctive graph with the profile strategy. Be