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Scientific Programming
Volume 2017, Article ID 9016303, 11 pages
Research Article

Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China

Correspondence should be addressed to Lei Wang; moc.621@0002ieladgnaw

Received 27 May 2016; Revised 31 October 2016; Accepted 24 November 2016; Published 26 January 2017

Academic Editor: Fabrizio Riguzzi

Copyright © 2017 Lei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP) plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO) has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO) is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.