Optimization of PCB component placements for the collect-and-place machines

William Ho, Ping Ji, Prasanta Kumar Dey

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)


This paper presents two hybrid genetic algorithms (HGAs) to optimize the component placement operation for the collect-and-place machines in printed circuit board (PCB) assembly. The component placement problem is to optimize (i) the assignment of components to a movable revolver head or assembly tour, (ii) the sequence of component placements on a stationary PCB in each tour, and (iii) the arrangement of component types to stationary feeders simultaneously. The objective of the problem is to minimize the total traveling time spent by the revolver head for assembling all components on the PCB. The major difference between the HGAs is that the initial solutions are generated randomly in HGA1. The Clarke and Wright saving method, the nearest neighbor heuristic, and the neighborhood frequency heuristic are incorporated into HGA2 for the initialization procedure. A computational study is carried out to compare the algorithms with different population sizes. It is proved that the performance of HGA2 is superior to HGA1 in terms of the total assembly time.
Original languageEnglish
Pages (from-to)828-836
Number of pages9
JournalInternational Journal of Advanced Manufacturing Technology
Issue number7-8
Publication statusPublished - 1 Jun 2008


  • Collect-and-place machines
  • Component grouping
  • Component sequencing
  • Feeder arrangement
  • Genetic algorithms
  • Printed circuit board manufacturing

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Optimization of PCB component placements for the collect-and-place machines'. Together they form a unique fingerprint.

Cite this