Abstract
Job-shop scheduling problem is a NP-hard complete. For some JSSP, even with moderate size, optimality can’t be guaranteed. Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO), which uses the dynamic inertia weight that decreases according iterative generation increasing to improve the performance of standard PSO. The proposed IPSO can be used to solve the traditional JSSP and the computational experiments show that the IPSO algorithm is more effective than GA and standard PSO algorithm for JSSP to minimize makespan.
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© 2009 Springer-Verlag Berlin Heidelberg
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Chen, Qx. (2009). Job-Shop Scheduling Based on Improved Particle Swarm. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_11
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DOI: https://doi.org/10.1007/978-3-642-03664-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03663-7
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