Data on Algorithms Reported by Researchers at National Taipei College of Business (Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms)
By a News Reporter-Staff News Editor at Life Science Weekly -- Fresh data on Algorithms are presented in a new report. According to news originating from Taipei, Taiwan, by NewsRx correspondents, research stated, "This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed."
Our news journalists obtained a quote from the research from the National Taipei College of Business, "In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could."
According to the news editors, the research concluded: "Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did."
For more information on this research see: Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms. Thescientificworldjournal [electronic Resource], 2014;2014():595902 (see also Algorithms).
The news correspondents report that additional information may be obtained from T.A. Chiang, Dept. of Business Administration, National Taipei College of Business, Taipei 10051, Taiwan. Additional authors for this research include Z.H. Che and Z. Cui.
Keywords for this news article include: Asia, Taipei, Taiwan, Algorithms, Machine Learning, Emerging Technologies, Particle Swarm Optimization.
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