Research on manufacturing productivity based on improved genetic algorithms under internet information technology. (14th October 2018)

• Record Type: Journal Article
• Authors: Luo, Tingting; Li, Guangyao; Yu, Naijiang
• Other Names: Manogaran Gunasekaran guestEditor.; Chilamkurti Naveen guestEditor.; Hsu Ching‐Hsien guestEditor.; Vijayakumar V. guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 10(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-14
• Subjects: improved genetic algorithm -- internet information technology -- manufacturing -- productivity; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4859
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The manufacturing characteristics of the manufacturing industry make the processing process more complex and difficult to control. How to have more effective production management and scheduling is the key link for enterprises to ensure balanced production. The problem of production scheduling and improvement of production efficiency is one of the core contents of manufacturing production management. It plays an important role in optimizing resource utilization, saving production costs and improving work efficiency. First, combined with the production characteristics of the manufacturing industry, the preliminary search ability for the basic genetic algorithm (GA) is high. The characteristics of poor search ability and strong ability of global optimization are in the late period, and the Particle Swarm Optimization (PSO) has the characteristics of poor early search ability, strong late search ability, and easy to fall into local optimum. In order to obtain better scheduling results, we make the two algorithms complementary to each other, and propose an improved genetic algorithm to solve the manufacturing efficiency problem. It is verified that the algorithm has better search performance. Second, aiming at the dynamic characteristics of manufacturing in manufacturing and processing, using the multi‐module technology of Internet information technology, the Internet information technology production planning model was constructed, and the functions, structural models, and interactive of each module were studied. Finally, the shortest path is solved by optimizing the genetic algorithm. Experiments show that, based on the improved genetic algorithm with rapid convergence, it can effectively improve the manufacturing efficiency.