Abstract
With the development of intelligent manufacturing and the customized product demand of customers, manufacturing enterprises are urgently required to carry out high-efficiency, high-quality, flexibility and low-cost manufacturing to enhance the competitiveness of enterprises. Intelligent job shop scheduling problem is the core decision of intelligent manufacturing production management. Many-objective job shop scheduling algorithms can effectively solve this problem. However, existing optimization algorithms cannot effectively solve many-objective flexible job shop scheduling problem. This paper establishes the many-objective job shop intelligent scheduling model with complex constraints, and proposes an improved intelligent decision optimization algorithm named NSGA-III-APEV based on NSGA-III. This algorithm uses the penalty-based boundary intersection distance that takes into account both convergence and diversity simultaneously to define the distance between the population individual and the reference vector in the association operation. This paper exploits the penalty-based boundary intersection distance-based elimination mechanism to preserve individuals and reduce the computational cost in the individual preservation strategy. Meanwhile, the adaptive mutation strategy based on consanguinity is employed in genetic operators. The presented method effectively improves the convergence and diversity of the population. Finally, NSGA-III-APEV with other algorithms was compared through benchmarks. Experimental results demonstrated the effectiveness and superiority of the improved method. The feasibility of the improved method in solving the many-objective flexible job shop scheduling problem are verified by engineering examples.
Highlights
With the development of artificial intelligence technologies such as big data and machine learning, global manufacturing is turning to be intelligent
NSGA-III-APEV, NSGA-III, Discrete multi-objective PSO algorithm (MOPSO) and NSGA-II are used for optimization, and each algorithm runs independently 30 times
2) RESULTS AND ANALYSIS Table 5 and Table 6 show that the performance metrics value obtained by the NSGA-III-APEV are better than NSGA-III, MOPSO and NSGA-II
Summary
With the development of artificial intelligence technologies such as big data and machine learning, global manufacturing is turning to be intelligent. Jia et al [12] established a job shop scheduling model including the maximum completion time, total workload of machines and critical machine workload, and proposed a path relinking algorithm based on the Tabu search (PRMOTS) The above methods have good convergence, the lack of population diversity protection mechanism usually results in local optimization and cannot obtain excellent scheduling schemes in solving Ma-OFJSSPs. The performance indicators based EAs indicate that various evaluation indicators are added to the MOEA. These methods can effectively balance diversity and convergence Such methods lead to a sharp increase in the computational complexity of the many-objective evolutionary algorithm, and cannot efficiently solve the Ma-OFJSSPs. Decomposition based EAs transform the MOP into a series of single-objective optimization sub-problems. Compared with the NSGA-III, the calculation efficiency is greatly improved in environment selection
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
