Alberto Gòmez - Academia.edu (original) (raw)
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Papers by Alberto Gòmez
Ai Edam Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2001
A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means ... more A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analysing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.
European Journal of Operational Research, 2003
In this paper we confront a family of scheduling problems by means of genetic algorithms: the job... more In this paper we confront a family of scheduling problems by means of genetic algorithms: the job shop scheduling problem with bottlenecks. Our main contribution is a strategy to introduce specific knowledge into the initial population. This strategy exploits a probabilistic-based ...
Obstetrical & Gynecological Survey, 1979
Ai Edam Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2001
A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means ... more A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analysing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.
European Journal of Operational Research, 2003
In this paper we confront a family of scheduling problems by means of genetic algorithms: the job... more In this paper we confront a family of scheduling problems by means of genetic algorithms: the job shop scheduling problem with bottlenecks. Our main contribution is a strategy to introduce specific knowledge into the initial population. This strategy exploits a probabilistic-based ...
Obstetrical & Gynecological Survey, 1979