Abstract
This paper considers uniform parallel machines scheduling problem with time-dependent learning effects, release dates, allowable preemption and machine idle time to minimise the total weighted earliness and tardiness penalties which is known to be strongly NP-hard. To solve this problem, this research proposes a mixed integer nonlinear programming (MINLP) model. Afterward, in order to find the best solution in an effective solution space, a dominant set is proposed for the length of the schedule experimentally. Also, based on the allowable idle time, a new time-dependent learning model on parallel machines is proposed. Furthermore, a genetic algorithm (GA) and a hybrid of genetic algorithm and particle swarm optimisation (HGA-PSO) are proposed. Taguchi method is applied to calibrate the parameters of the proposed algorithms. Finally, the computational results are provided to compare the results of the algorithms. Then, the efficiency of the proposed algorithms is discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
