Title :
An improved ant colony algorithm for multi-objective flexible job shop scheduling problem
Author :
Li, Li ; Wang, Keqi
Author_Institution :
Inf. & Comput. Eng. Coll., Northeast Forestry Univ., Harbin, China
Abstract :
Flexible job shop scheduling problem is a very important research in the field of combinatorial optimization. An improved ant colony algorithm for multi-objective flexible job shop scheduling problem is presented in this paper. The rule of our algorithm is described from the following aspects: local update, global update, trail intensities, solution set, local search, suitable parameters. The algorithm we presented is validated by practical instances. The results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.
Keywords :
combinatorial mathematics; job shop scheduling; optimisation; combinatorial optimization; global update; improved ant colony algorithm; local search; local update; multiobjective flexible job shop scheduling problem; solution set; trail intensities; Ant colony optimization; Automation; Costs; Educational institutions; Forestry; Job shop scheduling; Logistics; Neural networks; Production; Scheduling algorithm; Ant Colony Algorithm; Flexible Job Shop Schedule; Multi-objective Optimization;
Conference_Titel :
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-4794-7
Electronic_ISBN :
978-1-4244-4795-4
DOI :
10.1109/ICAL.2009.5262833
