Particle swarm optimization for sequencing problems: a case study

PSO has been successfully used in different areas (e. g. multidimensional and multiobjective optimization, neural networks training, etc.) but there are few reports on research in sequencing problems. In this paper we present a hybrid particle swarm optimizer (HPSO) that incorporates a random key representation for particles and a dynamic mutation operator similar to those used in evolutionary algorithm. This algorithm was designed with permutation problems. Our preliminary study shows the algorithm performance when it is applied to a set of instances for the total weighted tardiness problem in single machine environments. Results show that the hybrid HPSO is a promising approach to solve sequencing problems.