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

fYear

2009

fDate

5-7 Aug. 2009

Firstpage

697

Lastpage

702

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICAL.2009.5262833

Filename

5262833