Abstract
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 language | English |
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Pages (from-to) | 828-836 |
Number of pages | 9 |
Journal | International Journal of Advanced Manufacturing Technology |
Volume | 37 |
Issue number | 7-8 |
DOIs | |
Publication status | Published - Jun 2008 |
Keywords
- printed circuit board manufacturing
- collectand-place machines
- genetic algorithms
- component grouping
- component sequencing
- feeder arrangement