Authors:
Ana Madureira
1
;
Carlos Ramos
1
and
Sílvio do Carmo Silva
2
Affiliations:
1
Instituto Superior de Engenharia do Porto, Portugal
;
2
Universidade do Minho, Portugal
Keyword(s):
Dynamic scheduling, Extended Job-Shop Scheduling, Manufacturing, Genetic Algorithms
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
This paper presents a simple and general framework exploring the potential of evolutionary algorithms, which is of practical utility, embedded in a simple framework to solve difficult problems in dynamic environments. The proposed evolutionary approach is in line with reality and away from the approaches that deal with static and classic or basic Job-Shop scheduling problems. In fact, in real world, where problems are essentially of dynamic and stochastic nature, the traditional methods or algorithms are of very little use. This is the case with most algorithms for solving the so-called static scheduling problem for different setting of both single and multi-machine systems arrangements. This reality, motivated us to concentrate on tools, which could deal with such dynamic, disturbed scheduling problems, both for single and multi-machine manufacturing settings, even though, due to the complexity of these problems, optimal solutions may not be possible to find. We decided to address t
he problem drawing upon the potential of Genetic Algorithms to deal with such complex situations.
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