A simulation engine to support production scheduling using genetics-based machine learning

TL;DR

This paper introduces a genetics-based machine learning simulation engine for flow shop production scheduling, aiming to enhance flexibility and responsiveness in complex manufacturing environments.

Contribution

It presents a novel GBML approach for scheduling, integrating a simulator and decision support system to improve manufacturing process adaptability.

Findings

Preliminary results show promising scheduling performance.

The system supports human decision-making effectively.

Comparison with traditional methods is underway.

Abstract

The ever higher complexity of manufacturing systems, continually shortening life cycles of products and their increasing variety, as well as the unstable market situation of the recent years require introducing grater flexibility and responsiveness to manufacturing processes. From this perspective, one of the critical manufacturing tasks, which traditionally attract significant attention in both academia and the industry, but which have no satisfactory universal solution, is production scheduling. This paper proposes an approach based on genetics-based machine learning (GBML) to treat the problem of flow shop scheduling. By the approach, a set of scheduling rules is represented as an individual of genetic algorithms, and the fitness of the individual is estimated based on the makespan of the schedule generated by using the rule-set. A concept of the interactive software environment consisting of a simulator and a GBML simulation engine is introduced to support human decision-making during scheduling. A pilot study is underway to evaluate the performance of the GBML technique in comparison with other methods (such as Johnson's algorithm and simulated annealing) while completing test examples.