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Mathematical Problems in Engineering
Volume 2014, Article ID 258173, 9 pages
Research Article

A Bottleneck Detection Algorithm for Complex Product Assembly Line Based on Maximum Operation Capacity

Institute of CAPP & Manufacturing Engineering Software, Northwestern Polytechnical University, Xi’an 710072, China

Received 6 January 2014; Revised 4 March 2014; Accepted 5 March 2014; Published 19 May 2014

Academic Editor: Balaji Raghavan

Copyright © 2014 Dongping Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.