Single machine flow-time scheduling with scheduled maintenance
Acta Informatica
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Single-machine scheduling with periodic maintenance and nonresumable jobs
Computers and Operations Research
Using the particle swarm optimization technique to train a recurrent neural model
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Genetic algorithms for a two-agent single-machine problem with release time
Applied Soft Computing
Particle swarm optimization algorithm for the berth allocation problem
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a particle swarm optimization (PSO) algorithm is presented to solve the single-machine scheduling problem with periodic maintenance activities. The most important issue for applying PSO algorithm successfully to the addressed scheduling problem is how to develop an effective 'problem mapping' and 'construction of a particle sequence' mechanism. For the problem mapping aspect, we apply the ''job-to-position'' representation for the particles. For the construction of a particle sequence aspect, we use the largest position value (LPV) rule. Besides, to enhance the effective of the proposed PSO algorithm, we embedded a restarting strategy and three stopping criteria. The objective is to find a schedule that minimizes the makespan. The addressed problem is shown to be NP-hard in the strong sense by transforming to the 3-partition problem. Computational results show that the proposed PSO-M algorithm is quite satisfactory on both solution accuracy and efficiency to solve the addressed problem.