Job shop scheduling by simulated annealing
Operations Research
Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A fast taboo search algorithm for the job shop problem
Management Science
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Scheduling Algorithms
Production scheduling and rescheduling with genetic algorithms
Evolutionary Computation
New codification schemas for scheduling with genetic algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Hi-index | 0.00 |
The Job Shop Scheduling Problem is a paradigm of Constraint Satisfaction Problems that has interested to researchers over the last decades. In this paper we confront this problem by means of a Genetic Algorithm that is hybridized with a local search method. The Genetic Algorithm searches over the space of active schedules, whereas the local search does it over the space of semi-active ones. We report results from an experimental study over a set of selected problem instances showing that this combination of search spaces is better than restricting both algorithms to search over the same space. Furthermore we compare with the well-known Genetic Algorithms proposed by D. Mattfeld and the Branch and Bound procedure proposed by P. Brucker and obtain competitive results.