A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems

Authors:
Li-Ning Xing;Ying-Wu Chen;Peng Wang;Qing-Song Zhao;Jian Xiong
Affiliations:
Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha 410073, China;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha 410073, China;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha 410073, China;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha 410073, China;Department of Management Science and Engineering, College of Information System and Management, National University of Defense Technology, Changsha 410073, China
Venue:
Applied Soft Computing
Year:
2010

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Abstract

A Knowledge-Based Ant Colony Optimization (KBACO) algorithm is proposed in this paper for the Flexible Job Shop Scheduling Problem (FJSSP). KBACO algorithm provides an effective integration between Ant Colony Optimization (ACO) model and knowledge model. In the KBACO algorithm, knowledge model learns some available knowledge from the optimization of ACO, and then applies the existing knowledge to guide the current heuristic searching. The performance of KBACO was evaluated by a large range of benchmark instances taken from literature and some generated by ourselves. Final experimental results indicate that the proposed KBACO algorithm outperforms some current approaches in the quality of schedules.