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Optimizing robotic part feeder throughput with queueing theory

Dadi Gudmundsson (Sensor Analytics, Inc., San Francisco, California, USA)
Ken Goldberg (University of California, Berkeley, California, USA)

Assembly Automation

ISSN: 0144-5154

Article publication date: 24 April 2007




This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.


Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.


The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Practical implications

Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.


This paper advances the scientific understanding of automation and part feeding.



Gudmundsson, D. and Goldberg, K. (2007), "Optimizing robotic part feeder throughput with queueing theory", Assembly Automation, Vol. 27 No. 2, pp. 134-140.



Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

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