Single facility scheduling with nonlinear processing times
Computers and Industrial Engineering
Scheduling jobs under simple linear deterioration
Computers and Operations Research
A discrete version of particle swarm optimization for flowshop scheduling problems
Computers and Operations Research
Computers and Operations Research
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
A two-machine flowshop makespan scheduling problem with deteriorating jobs
Computers and Industrial Engineering
Advances in Engineering Software
A branch-and-bound algorithm for solving a two-machine flow shop problem with deteriorating jobs
Computers and Operations Research
A case study on using evolutionary algorithms to optimize bakery production planning
Expert Systems with Applications: An International Journal
Minimizing resource consumption on uniform parallel machines with a bound on makespan
Computers and Operations Research
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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.