A Hybrid Particle Swarm Algorithm for Resource-Constrained Project Scheduling

A Hybrid Particle Swarm Algorithm for Resource-Constrained Project Scheduling

Jens Czogalla (Helmut-Schmidt-University Hamburg, Germany) and Andreas Fink (Helmut-Schmidt-University Hamburg, Germany)
DOI: 10.4018/978-1-61350-086-6.ch007
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Abstract

The authors present and analyze a particle swarm optimization (PSO) approach for the resource-constrained project scheduling problem (RCPSP). It incorporates well-known procedures such as the serial schedule generation scheme and it is hybridized with forward-backward improvement. The authors investigate the application of PSO in comparison to state-of-the-art methods from the literature. They conduct extensive computational experiments using a benchmark set of problem instances. The reported results demonstrate that the proposed hybrid particle swarm optimization approach is competitive. They significantly improve previous results of PSO for the RCPSP and provide new overall best average results for the medium size data set. Furthermore, the authors provide insights into the importance of crucial components for achieving high-quality results.
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2 Background

In this section we formally introduce the RCPSP and briefly review the related literature.

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