Open AccessA Bottleneck Detection Algorithm for Complex Product Assembly Line Based on Maximum Operation Capacity
Author(s) -
Dongping Zhao,
Xitian Tian,
Junhao Geng
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/258173
Subject(s) - bottleneck , adjacency matrix , algorithm , adjacency list , convergence (economics) , workstation , matrix (chemical analysis) , line (geometry) , rate of convergence , genetic algorithm , computer science , product (mathematics) , chromosome , mathematical optimization , mathematics , key (lock) , theoretical computer science , materials science , geometry , computer security , economics , composite material , gene , embedded system , economic growth , operating system , graph , biochemistry , chemistry
Because of the complex constraints in complex product assembly line, existing algorithms not always detect bottleneck correctly and they have a low convergence rate. In order to solve this problem, a hybrid algorithm of adjacency matrix and improved genetic algorithm (GA) was proposed. First, complex assembly network model (CANM) was defined based on operation capacity of each workstation. Second, adjacency matrix was proposed to convert bottleneck detection of complex assembly network (CAN) into a combinatorial optimization problem of max-flow. Third, an improved GA was proposed to solve this max-flow problem by retaining the best chromosome. Finally, the min-cut sets of CAN were obtained after calculation, and bottleneck workstations were detected according to the analysis of min-cut sets. A case study shows that this algorithm can detect bottlenecks correctly and its convergence rate is high
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom