Abstract
The classical Particle Swarm Optimization (PSO) is a powerful method to find the minimum of for a function optimization problem, especially with a continuous solution space. So far, it has not been widely applied to solving those problems with discrete features. We attempt to deal with the problem of scheduling jobs on a single machine against common due dates with respect to earliness and tardiness penalties in this paper. A PSO strategy integrated with a kind of heuristic algorithm is proposed, where the heuristic information is composed of the processing time and tardiness penalty for each job. It is indicated that such strategy can significantly improve the performance of the solutions. Furthermore, we demonstrate by numerical benchmark examples from OR-Library that our algorithm is both effective and efficient in achieving satisfied solutions for scheduling problems with earliness and tardiness penalties.
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