Multi-level assembly process complexity analysis and its application for mixed-model assembly sequencing. (2017)

• Record Type: Journal Article
• Authors: He, Fei; Jiang, Ming-ming
• Is Part Of: International journal of innovative computing and applications. Volume 8:Number 4(2017)
• Page Start: 228
• Page End: 240
• Publication Date: 2017
• Subjects: process complexity -- information entropy -- assembly system -- mixed-model assembly sequencing -- fractal idea; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

This research aims at understanding the process complexity in assembly system, and the complexity is defined to describe the complexity for the production activities and their sequences. Four primary integer layers and other fractal layers are decomposed from the whole assembly process according to the idea of fractal theory. The four kinds of complexities are station operation complexity, assembly flow complexity, production sequence complexity and production cycle complexity; they are proposed to present the complexity characteristic for different integer layers. The information entropy is adopted to measure these process complexities, and two different measurements are proposed for the pull and push production models respectively. For conquering the two contradictory problems, high operation failure rate and decrease of working emotion, which are caused by inappropriate product similarity distribution, the optimisation objective minimising the diversity of the assembly flow complexity is exploited. Then the multi-objective genetic algorithm is adopted to modelling the mixed-model assembly sequencing problem with two optimisation objectives, and a case study is implemented to demonstrate the approach.