Robust and stable flexible job shop scheduling with random machine breakdowns: multi-objectives genetic algorithm approach. (2019)

• Record Type: Journal Article
• Authors: Sajadi, Seyed Mojtaba; Alizadeh, Azar; Zandieh, Mostafa; Tavan, Fereshteh
• Is Part Of: International journal of mathematics in operational research. Volume 14:Number 2(2019)
• Page Start: 268
• Page End: 289
• Publication Date: 2019
• Subjects: flexible job-shop scheduling problem -- machine breakdowns -- robustness -- stability -- SMEs; Operations research -- Mathematical models -- Periodicals; Operations research -- Mathematics -- Periodicals; Decision making -- Mathematical models -- Periodicals; 658.4033
• Languages: English
• ISSNs: 1757-5850

Abstract

In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.