Recurrent neural network approach for cyclic job shop scheduling problem

TL;DR

This paper presents a neural network-based method to efficiently minimize cycle time in cyclic job shop scheduling, addressing the exponential complexity of such problems in manufacturing applications.

Contribution

It introduces a novel neural network model tailored for cyclic job shop scheduling, demonstrating efficiency, adaptability, and effectiveness over existing methods.

Findings

Validated approach reduces cycle time effectively

Neural network model adapts well to different manufacturing scenarios

Experimental results confirm the system's efficiency and accuracy

Abstract

While cyclic scheduling is involved in numerous real-world applications, solving the derived problem is still of exponential complexity. This paper focuses specifically on modelling the manufacturing application as a cyclic job shop problem and we have developed an efficient neural network approach to minimise the cycle time of a schedule. Our approach introduces an interesting model for a manufacturing production, and it is also very efficient, adaptive and flexible enough to work with other techniques. Experimental results validated the approach and confirmed our hypotheses about the system model and the efficiency of neural networks for such a class of problems.