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Discrete Particle Swarm Optimization Algorithm in Flexible Hybrid Flow Shop Scheduling
Liu Dongdong,Liu Kai,Zhao Zhengping Han Bo,Zhang Yan 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Traditional flexible flow shop scheduling cannot adapt to the work processes with existence of parallel machines, and blocks or limits the processes with no-wait constraints. Firstly, according to the problem in NWBFFSSP, which minimizes the maximum time used in the flow shop, an optimal solving model has been designed to realize the flexible flow shop scheduling with multi constraints; besides, for the distribution of machinery is improved, Finally, in the solving process, a real-time release priority strategy has been proposed to determine processing machine for each work piece. Furthermore, a methodology to detect work piece conflicts has been introduced while the conflicts are then eliminated by a kind of right moving strategy based on the maximum difference. The experimental results verify the effectiveness and feasibility of the proposed algorithm.
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Liu Dongdong,Liu Kai,Wang Feng,Han Bo,Zhao Zhengping,Tan Fuxiao,Niu Lei 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.8
In allusion to such problems as low accuracy and long convergence time in traditional two-echelon vehicle routing optimization algorithm, a Memetic algorithm (QDEMA) based on Q learning theory and differential evolution is proposed in this article to solve above problems. Firstly, it is necessary to research the two-echelon vehicle routing optimization problem and adopt the optimal segmentation method to obtain the relatively reasonable distribution plan for the first-echelon SDVRP problem in order to accordingly determine the distribution quantity of the transfer stations; secondly, the second-echelon MDVRP distribution scheme is solved to obtain the total distance and the total number of the distribution vehicles for the two-echelon optimization problem; thirdly, in allusion to the solution of the second-echelon MDVRP distribution scheme, Q learning theory and the differential evaluation algorithm are adopted to design new Memetic algorithm in order to globally optimize MDVRP distribution scheme; finally, the simulation experiment is carried out to verify the algorithm effectiveness.
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