Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities
Abstract
The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization,... while the experimental results show the effectiveness and superiority of the WOA algorithm.
Keywords:
Multi-objective optimization / Population-based algorithms / Manufacturing resources schedulingSource:
Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022), 2022, 403-407-Funding / projects:
- MISSION4.0 - Deep Machine Learning and Swarm Intelligence-Based Optimization Algorithms for Control and Scheduling of Cyber-Physical Systems in Industry 4.0 (RS-ScienceFundRS-AI-6523109)
- Ministry of Science, Technological Development and Innovation of the Republic of Serbia, institutional funding - 200105 (University of Belgrade, Faculty of Mechanical Engineering) (RS-MESTD-inst-2020-200105)
- Project “Biologically inspired optimization algorithms for control and scheduling of intelligent robotic systems”, Grant No. PPN/ULM/2019/1/00354/U/00001
- Polish Ministry of Science and Higher Education, Grant No. WZ/WE-IA/4/2020
DOI: 10.1109/MMAR55195.2022.9874301
ISBN: 978-1-6654-6857-2
Scopus: 2-s2.0-85139037070
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Institution/Community
Mašinski fakultetTY - CONF AU - Petrović, Milica AU - Jokić, Aleksandar AU - Miljković, Zoran AU - Kulesza, Zbigniew PY - 2022 UR - https://machinery.mas.bg.ac.rs/handle/123456789/3968 AB - The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm. C3 - Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022) T1 - Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities SP - 403-407 DO - 10.1109/MMAR55195.2022.9874301 ER -
@conference{ author = "Petrović, Milica and Jokić, Aleksandar and Miljković, Zoran and Kulesza, Zbigniew", year = "2022", abstract = "The contemporary manufacturing systems face a challenging and uncertain future due to frequent customer demands for customized products. A promising direction that can enable manufacturing systems to fulfill the market requirements is the adaptation of a reconfigurable manufacturing system paradigm. Physical reconfigurability can be achieved by developing systems that can satisfy conflicting production priorities such as minimal production time and maximal profit. Having that in mind, in this paper, the authors present a comprehensive analysis of population-based multi-objective optimization algorithms utilized for scheduling manufacturing entities. The output of multi-objective optimization is a set of Pareto optimal solutions in the form of production scheduling plans with transportation constraints. Three state-of-the-art population-based algorithms i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), are employed for optimization, while the experimental results show the effectiveness and superiority of the WOA algorithm.", journal = "Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022)", title = "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities", pages = "403-407", doi = "10.1109/MMAR55195.2022.9874301" }
Petrović, M., Jokić, A., Miljković, Z.,& Kulesza, Z.. (2022). Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022), 403-407. https://doi.org/10.1109/MMAR55195.2022.9874301
Petrović M, Jokić A, Miljković Z, Kulesza Z. Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities. in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022). 2022;:403-407. doi:10.1109/MMAR55195.2022.9874301 .
Petrović, Milica, Jokić, Aleksandar, Miljković, Zoran, Kulesza, Zbigniew, "Multi-Objective Population-based Optimization Algorithms for Scheduling of Manufacturing Entities" in Proceedings of the 26th International Conference on Methods and Models in Automation and Robotics (MMAR 2022) (2022):403-407, https://doi.org/10.1109/MMAR55195.2022.9874301 . .