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 language | English |
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Pages (from-to) | 175-189 |
Number of pages | 15 |
Journal | Artificial Intelligence in Engineering |
Volume | 14 |
Issue number | 2 |
DOIs | |
State | Published - 1 Jan 2000 |
ASJC Scopus subject areas
- General Computer Science
- General Engineering