Multiobjective Flexible Job-Shop Rescheduling With New Job Insertion and Machine Preventive Maintenance

In the actual production, the insertion of new job and machine preventive maintenance (PM) are very common phenomena. Under these situations, a flexible job-shop rescheduling problem (FJRP) with both new job insertion and machine PM is investigated. First, an imperfect PM (IPM) model is established...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics 2023-05, Vol.53 (5), p.3101-3113
Main Authors: An, Youjun, Chen, Xiaohui, Gao, Kaizhou, Li, Yinghe, Zhang, Lin
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive algorithms
Algorithms
Design of experiments
Dynamic scheduling
Electric breakdown
Flexible job-shop rescheduling
Genetic algorithms
Impact analysis
Insertion
Job shop scheduling
machine preventive maintenance (PM)
Maintenance engineering
multiobjective evolutionary algorithm (MOEA)
Multiple objective analysis
new job insertion
Optimization
Optimization models
Preventive maintenance
Production
Production scheduling
Rescheduling
Sorting algorithms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the actual production, the insertion of new job and machine preventive maintenance (PM) are very common phenomena. Under these situations, a flexible job-shop rescheduling problem (FJRP) with both new job insertion and machine PM is investigated. First, an imperfect PM (IPM) model is established to determine the optimal maintenance plan for each machine, and the optimality is proven. Second, in order to jointly optimize the production scheduling and maintenance planning, a multiobjective optimization model is developed. Third, to deal with this model, an improved nondominated sorting genetic algorithm III with adaptive reference vector (NSGA-III/ARV) is proposed, in which a hybrid initialization method is designed to obtain a high-quality initial population and a critical-path-based local search (LS) mechanism is constructed to accelerate the convergence speed of the algorithm. In the numerical simulation, the effect of parameter setting on the NSGA-III/ARV is investigated by the Taguchi experimental design. After that, the superiority of the improved operators and the overall performance of the proposed algorithm are demonstrated. Next, the comparison of two IPM models is carried out, which verifies the effectiveness of the designed IPM model. Last but not least, we have analyzed the impact of different maintenance effects on both the optimal maintenance decisions and integrated maintenance-production scheduling schemes.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2022.3151855