Abstract
Job shop scheduling problem with machine maintenance has attracted the attention of many scholars over the past decades. However, only a limited number of studies investigate the availability of injection mould which is important to guarantee the regular production of plastic industry. Furthermore, most researchers only consider the situation that the maintenance duration and interval are fixed. But in reality, maintenance duration and interval may vary based on the resource age. This paper solves the job shop scheduling with mould maintenance problem (JSS-MMP) aiming at minimizing the overall makespan through a jointly schedule strategy. Particle Swarm Optimization Algorithm (PSO) and Genetic Algorithm (GA) are used to solve this optimization problem. The simulation results show that under the condition that the convergence time of two algorithms are similar, PSO is more efficient than GA in terms of convergence rate and solution quality.
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Acknowledgments
The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 15201414). The Natural Science Foundation of China (Grant No. 71471158, 71571120, 71271140); and under student account code RUKH.
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Fu, X., Chan, F.T.S., Niu, B., Chung, S.H., Bi, Y. (2017). Minimization of Makespan Through Jointly Scheduling Strategy in Production System with Mould Maintenance Consideration. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_51
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