A solution method for the component allocation problem in printed circuit board assembly

Yongzhong Wu, Ping Ji

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


Purpose - The purpose of this paper is to propose an effective and efficient solution method for the component allocation problem (CAP) in printed circuit board (PCB) assembly, in order to achieve high-throughput rates of the PCB assembly lines. Design/methodology/approach - The investigated CAP is intertwined with the machine optimization problems for each machine in the line because the latter determine the process time of each machine. In order to solve the CAP, a solution method, which integrates a meta-heuristic (genetic algorithm) and a regression model is proposed. Findings - It is found that the established regression model can estimate the process time of each machine accurately and efficiently. Experimental tests show that the proposed solution method can solve the CAP both effectively and efficiently. Research limitations/implications - Although different regression models are required for different types of assembly machines, the proposed solution method can be adopted for solving the CAPs for assembly lines of any configuration, including a mixed-vendor assembly line. Practical implications - The solution method can ensure a high-throughput rate of a PCB assembly line, and thus improve the production capacity without further investment on the expensive PCB assembly equipment. Originality/value - The paper proposes an innovative solution method for the CAP in PCB assembly. The solution method integrates the meta-heuristic method and the regression method, which has not been studied in the literature.
Original languageEnglish
Pages (from-to)155-163
Number of pages9
JournalAssembly Automation
Issue number2
Publication statusPublished - 1 Jan 2010


  • Assembly
  • Parts
  • Printed circuits
  • Programming and algorithm theory
  • Regression analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering

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