A fast taboo search algorithm for the job shop problem
Management Science
An effective hybrid optimization strategy for job-shop scheduling problems
Computers and Operations Research
From Natural to Artificial Swarm Intelligence
From Natural to Artificial Swarm Intelligence
Scheduling by Genetic Local Search with Multi-Step Crossover
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Robotics and Computer-Integrated Manufacturing
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Scheduling flow lines with buffers by ant colony digraph
Expert Systems with Applications: An International Journal
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This paper presents an advanced software system for solving the flexible manufacturing systems (FMS) scheduling in a job-shop environment with routing flexibility, where the assignment of operations to identical parallel machines has to be managed, in addition to the traditional sequencing problem. Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (GA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms. A modular approach is also adopted in order to obtain an easy scalable parallel evolutionary-ant colony framework. The performance of the proposed framework on properly designed benchmark problems is compared with effective GA and ACO approaches taken as algorithm components.