A multi-objective ant colony system algorithm for flow shop scheduling problem

Authors:
Betul Yagmahan;Mehmet Mutlu Yenisey
Affiliations:
Department of Industrial Engineering, Uludag University, Gorukle 16059, Bursa, Turkey;Department of Industrial Engineering, Istanbul Technical University, Macka 34367, Istanbul, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 7
Cited 11

A genetic algorithm for flowshop sequencing

Computers and Operations Research - Special issue on genetic algorithms
The ant colony optimization meta-heuristic

New ideas in optimization
Two-machine flowshop scheduling with a secondary criterion

Computers and Operations Research
Fragmental Optimization on the 2-Machine Bicriteria Flowshop Scheduling Problem

ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
An ant colony system for permutation flow-shop sequencing

Computers and Operations Research
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant system: optimization by a colony of cooperating agents

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Ants with three primary colors for track initiation

Expert Systems with Applications: An International Journal
An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem

Expert Systems with Applications: An International Journal
Short Communication: A simple and better algorithm to solve the vendor managed inventory control system of multi-product multi-constraint economic order quantity model

Expert Systems with Applications: An International Journal
A hybrid ant colony optimization algorithm for optimal multiuser detection in DS-UWB system

Expert Systems with Applications: An International Journal
SmartGantt - An intelligent system for real time rescheduling based on relational reinforcement learning

Expert Systems with Applications: An International Journal
A new ant colony algorithm for makespan minimization in permutation flow shops

Computers and Industrial Engineering
Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling

Engineering Applications of Artificial Intelligence
An Ant Colony System Algorithm for the Hybrid Flow-Shop Scheduling Problem

International Journal of Applied Metaheuristic Computing
Self-Optimization module for Scheduling using Case-based Reasoning

Applied Soft Computing
Scheduling flow lines with buffers by ant colony digraph

Expert Systems with Applications: An International Journal
A case study on using evolutionary algorithms to optimize bakery production planning

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

In this paper, we consider the flow shop scheduling problem with respect to the both objectives of makespan and total flowtime. This problem is known to be NP-hard type in literature. Several algorithms have been proposed to solve this problem. We present a multi-objective ant colony system algorithm (MOACSA), which combines ant colony optimization approach and a local search strategy in order to solve this scheduling problem. The proposed algorithm is tested with well-known problems in literature. Its solution performance was compared with the existing multi-objective heuristics. The computational results show that proposed algorithm is more efficient and better than other methods compared.