Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms

Abstract

Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done “on-line” by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence.The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an “off-line” manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a “generate and test” fashion. The obtained results showed that with the improved convergence gained by using multi-level optimization, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.