Research on Dynamic Flexible Job Shop Scheduling Problem for Energy Consumption

doi:10.16182/j.issn1004731x.joss.201709039

Abstract

Abstract: In order to solve the problem of uneven load and energy consumption under disturbance, a flexible job shop scheduling model with average flow time and energy consumption was constructed. Aiming at the above model, a genetic and simulated annealing algorithm (GASA) was designed, which is based on the genetic algorithm and the simulated annealing algorithm. A new group of individuals were generated by genetic algorithm. And then the individual simulated the annealing process, in order to avoid falling into the local optimal. Aiming at the dynamic flexible job shop scheduling problem, the rolling window technique and GASA algorithm were combined and applied in the case of machine disturbance. The effectiveness of the algorithm was proved by the simulation of an instance.

Key words: dynamic scheduling, flexible job shop scheduling problem, genetic algorithm, simulated annealing algorithm, rolling window technology

CLC Number:

TP278

Chen Chao, Wang Yan, Yan Dahu, Ji Zhicheng. Research on Dynamic Flexible Job Shop Scheduling Problem for Energy Consumption[J]. Journal of System Simulation, 2017, 29(9): 2168-2175.

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