Application of particle swarm optimization and simulated annealing algorithms in flow shop scheduling problem under linear deterioration

This paper studies a permutation flow shop scheduling problem with deteriorating jobs. Deteriorating jobs are the jobs which the processing time depends on the waiting time before process starts. A particle swarm optimization algorithm with and without a proposed local search is developed to determine a job sequence with minimization of the total tardiness criterion. Furthermore, a simulated annealing is proposed to solve the problem. We compare the performance of these algorithms to achieve an optimal or near optimal solution. It is concluded that the particle swarm optimization algorithm with local search gives promising solutions. The quality of solution obtained by particle swarm optimization algorithm with local search is superior to that of the simulated annealing algorithm, but the simulated annealing algorithm takes shorter time to find a schedule solution.