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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\u2019s guiding mechanism, select node method, and update pheromone\u2019s 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.<\/jats:p>","DOI":"10.1155\/2017\/9016303","type":"journal-article","created":{"date-parts":[[2017,1,26]],"date-time":"2017-01-26T17:22:54Z","timestamp":1485451374000},"page":"1-11","source":"Crossref","is-referenced-by-count":46,"title":["Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization"],"prefix":"10.1155","volume":"2017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6145-0705","authenticated-orcid":true,"given":"Lei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingcao","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, 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