A neural network approach for a robot task sequencing problem

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 minimiz...

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Bibliographic Details
Published in:	Artificial intelligence in engineering Vol. 14; no. 2; pp. 175 - 189
Main Authors:	Maimon, O., Braha, D., Seth, V.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.04.2000
Subjects:	Asymmetric traveling salesman Problem Automated storage and retrieval Parallel and distributed computation Recurrent neural networks Robot task sequencing Robot task sequencing Recurrent neural networks Asymmetric traveling salesman Problem Automated storage and retrieval Parallel and distributed computation
ISSN:	0954-1810
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0954-1810
DOI:	10.1016/S0954-1810(00)00008-X