Neural network approach for a robot task sequencing problem

O. Maimon, D. Braha, V. Seth

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

This paper presents a neural network approach with successful implementation for the robot task-sequencing problem. The problem addresses the sequencing of tasks comprising loading and unloading of parts into and from the machines by a material-handling robot. The performance criterion is to minimize a weighted objective of the total robot travel time for a set of tasks and the tardiness of the tasks being sequenced. A three-phased parallel implementation of the neural network algorithm on Thinking Machine's CM-5 parallel computer is also presented which resulted in a dramatic increase in the speed of finding solutions. To evaluate the performance of the neural network approach, a branch-and-bound method and a heuristic procedure have been developed for the problem. The neural network method is shown to give good results and is especially useful for solving large problems on a parallel-computing platform.

Original languageEnglish
Pages (from-to)175-189
Number of pages15
JournalArtificial Intelligence in Engineering
Volume14
Issue number2
DOIs
StatePublished - 1 Jan 2000

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering

Fingerprint

Dive into the research topics of 'Neural network approach for a robot task sequencing problem'. Together they form a unique fingerprint.

Cite this