Programación de un sistema job shop-open shop por medio de una red neuronal

Abstract

A methodology based on neural networks is proposed for solving the job shop-open shop scheduling problem. The job shop scheduling problem consist of defining the best order sequence to minimize the total processing time (makespan) or other relevant variables. Several intelligent techniques have been applied to solve this kind of problem. However, when reprogramming is required these techniques present practical difficulties since they require to be restructured to find a new solution. The proposed methodology trains the network by combining the processing times at each node, through transfer functions that are multiplied by weights obtained from the algorithm of network programming. The proposed neural network obtains solutions not only for a particular problem but also for new situations without requiring a problem reconfiguration. When compared with other techniques, the obtained solution showed a superior performance ranging from 30% to 164% in terms of the makespan.