Flow Shop Scheduling Based on A Novel Cooling Temperature Driven Simulated Annealing Algorithm
Journal article, Peer reviewed
“ n o t i c e: this is the author’s version of a work that was accepted for publication in scientia iranica. international journal of science and technology. changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. changes may have been made to this work since it was submitted for publication. a definitive version was subsequently published in chen, r. m., & sandnes, f. e. (2015). flow shop scheduling based on a novel cooling temperature driven simulated annealing algorithm. scientia iranica. transaction b, mechanical engineering, 22(4), 1545.” this postprint version is published with a creative commons attribution non- commercial no derivatives license
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2015-01-05Metadata
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Chen, R. M., & Sandnes, F. E. (2015). Flow shop scheduling based on a novel cooling temperature driven simulated annealing algorithm. Scientia Iranica. Transaction B, Mechanical Engineering, 22(4), 1545.Abstract
The permutation flow shop problem (PFSP) has been applied to many types of problems. The PFSP is an NP - hard permutation sequencing scheduling problem. A local search with simulated annealing scheme involving two phases is proposed in this investigation for solving PFSP. First, for lowering computation complexity, a simple insertion local search is applied to generate the solution of the PFSP. Second, two non-decreasing cooling temperature driven simulated annealing (SA) named steady SA and reheating SA are employed to maintain successive exploration or exploitation in the solution space. The steady SA maintains the same temperature and keeps the same search behavior and thereby allows the neighbors of the worse solutions to be explored, consequently increasing the chances of finding better solutions, while the reheating SA increases the temperature and increases the exploration ability. The most important feature of the proposed method is its simple implementation and low computation time complexity. Experimental results are compared with other state - of - the - art algorithms and reveal that the proposed simple insertion with steady SA (SI - SSA) method is able to efficiently yield the best permutation schedules.