Meta-Heuristics for Manufacturing Systems Optimization Ⅱ

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 10869

Special Issue Editors


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Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430062, China
Interests: manufacturing system optimization and scheduling; vehicle routing problem; multi-objective optimization; intelligent optimization; intelligent control
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Guest Editor
School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
Interests: manufacturing system optimization and simulation

Special Issue Information

Dear Colleagues,

Meta-heuristics are effective tools inspired from the phenomena and behavior of nature and society. There are many meta-heuristics, including genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colony optimization, the estimation of distribution algorithms, differential evolution, shuffled frog-leaping algorithms, teaching–learning-based optimization, imperialist competitive algorithms, etc. The manufacturing industry is an important part of the economy in a number of countries, including in China. Many complicated optimization problems, including scheduling and routing, extensively exist in manufacturing systems. They may have symmetrical features or constraints, and some of them possess asymmetrical conditions that are difficult to tackle using traditional optimization methods. In the last decade, meta-heuristics have become the main path to solve manufacturing system optimization problems, and a number of results have been obtained.  

This Special Issue invites contributions addressing novel theories, techniques, and applications of meta-heuristic-based manufacturing system optimization. We intend to garner articles on a variety of topics, such as meta-heuristics for multi-objective optimization, meta-heuristics for constrained optimization, multi-objective production scheduling, production scheduling with uncertainty, energy-efficient scheduling, distributed scheduling, dynamic scheduling, etc. Extensive review papers on the latest research findings are also welcome.

Potential topics include but are not limited to the following:

  • Meta-heuristics for multi-objective optimization;
  • Meta-heuristics for constrained optimization;
  • Multi-objective production scheduling;
  • Production scheduling with uncertainty;
  • Energy-efficient scheduling;
  • Distributed scheduling;
  • Dynamic scheduling;
  • Machine learning for optimization and scheduling;
  • Meta-heuristic with machine learning for optimization and scheduling;
  • Assembly line balancing;
  • Vehicle routing problem;
  • Optimization problems in semiconductors, irons, automobiles, the chemical industry, etc.

Prof. Dr. Deming Lei
Dr. Jingcao Cai
Guest Editors

Manuscript Submission Information

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Published Papers (8 papers)

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Research

21 pages, 1896 KiB  
Article
A Hybrid Whale Optimization Algorithm for Quality of Service-Aware Manufacturing Cloud Service Composition
by Hong Jin, Cheng Jiang and Shengping Lv
Symmetry 2024, 16(1), 46; https://doi.org/10.3390/sym16010046 - 29 Dec 2023
Cited by 1 | Viewed by 890
Abstract
Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate [...] Read more.
Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services (MCSs) to generate an optimized MCSC that can meet the diverse requirements of customers. However, many available MCSs, deployed in the CMfg platform, have the same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with a high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm (WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, adaptive crossover inspired by the genetic algorithm is developed to balance exploration and exploitation. The Lévy flight is designed to expand the search space of the WOA and accelerate the convergence of the WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on various benchmark functions and different scale QoS-MCSC problems. The QoS attributes of the problems are randomly and symmetrically generated. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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21 pages, 5300 KiB  
Article
Topology Optimization of Continuum Structures Based on Binary Hunter-Prey Optimization Algorithm
by Zhuanzhe Zhao, Yujian Rui, Yongming Liu, Zhibo Liu and Zhijian Tu
Symmetry 2023, 15(5), 1118; https://doi.org/10.3390/sym15051118 - 19 May 2023
Cited by 2 | Viewed by 1261
Abstract
According to BESO’s principle of binarizing continuous design variables and the excellent performance of the standard HPO algorithm in terms of solving continuous optimization problems, a discrete binary Hunter-prey optimization algorithm is introduced to construct an efficient topology optimization model. It was used [...] Read more.
According to BESO’s principle of binarizing continuous design variables and the excellent performance of the standard HPO algorithm in terms of solving continuous optimization problems, a discrete binary Hunter-prey optimization algorithm is introduced to construct an efficient topology optimization model. It was used to solve the problems that the BESO method of topology optimization has, such as easily falling into the local optimal value and being unable to obtain the optimal topology configuration; the metaheuristic algorithm was able to solve the topology optimization model’s low computational efficiency and could easily produce intermediate elements and unclear boundaries. Firstly, the BHPO algorithm was constructed by discrete binary processing using the s-shape transformation function. Secondly, BHPO-BESO topology optimization theory was established by combining the BHPO algorithm with BESO topology optimization. Using the sensitivity information of the objective function and the updated principle of the meta-heuristic of the BHPO algorithm, a semi-random search for the optimal topology configuration was carried out. Finally, numerical simulation experiments were conducted by using the three typical examples of the cantilever beam, simply supported beam, and clamping beam as optimization objects and the results were compared with the solution results of BESO topology optimization. The experimental results showed that compared with BESO, BHPO-BESO could find the optimal topology configuration with lower compliance and maximum stiffness, and it has higher computational efficiency, which can solve the above problems. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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16 pages, 6454 KiB  
Article
Design of Axially Symmetric Fluid–Spring Vibration Absorber with Five DOFs Based on Orthogonal Experiment
by Youyu Liu, Zhao Fang, Liteng Ma, Wanbao Tao, Peng Wang and Zhijia Wang
Symmetry 2023, 15(5), 980; https://doi.org/10.3390/sym15050980 - 25 Apr 2023
Viewed by 1103
Abstract
The strong and complex vibration from a manipulator for anchor drilling will damage the key components of the manipulator and produce noise at the same time. According to its vibration characteristics, a fluid–spring vibration absorption approach with five degrees of freedom (DOFs) is [...] Read more.
The strong and complex vibration from a manipulator for anchor drilling will damage the key components of the manipulator and produce noise at the same time. According to its vibration characteristics, a fluid–spring vibration absorption approach with five degrees of freedom (DOFs) is proposed, which has perfect symmetry, and a vibration absorber was designed with a symmetrical structure. Employing the generalization formula of the Bernoulli equation and dynamic equation, a fluid–spring coupling damping coefficient equation was constructed. Vibration transmissibility was used as the evaluation index of vibration absorption performance. The elastic coefficients of the tension spring and torsion spring, the area ratio of circular through-holes to the vibration-absorbing plate, and the radius of circular through-holes were the main independent factors influencing the damping coefficients. An orthogonal experiment with four factors and four levels was designed. Using FLUENT and SIMULATION to implement joint simulations, the distribution law of the flow fields and the damping coefficients of each approach were obtained, and then the best combination of factors was selected. Taking a manipulator used for anchor drilling in Huainan of China as a case study, using the designed fluid–spring vibration absorber, the vibration displacements in the five DOFs were reduced by 68.32%, 49.82%, 52.17%, 49.01%, and 57.09% respectively, indicating a good vibration absorption performance with symmetry about the z-axis. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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15 pages, 328 KiB  
Article
A Dual-Population Genetic Algorithm with Q-Learning for Multi-Objective Distributed Hybrid Flow Shop Scheduling Problem
by Jidong Zhang and Jingcao Cai
Symmetry 2023, 15(4), 836; https://doi.org/10.3390/sym15040836 - 30 Mar 2023
Cited by 3 | Viewed by 1694
Abstract
In real-world production processes, the same enterprise often has multiple factories or one factory has multiple production lines, and multiple objectives need to be considered in the production process. A dual-population genetic algorithm with Q-learning is proposed to minimize the maximum completion time [...] Read more.
In real-world production processes, the same enterprise often has multiple factories or one factory has multiple production lines, and multiple objectives need to be considered in the production process. A dual-population genetic algorithm with Q-learning is proposed to minimize the maximum completion time and the number of tardy jobs for distributed hybrid flow shop scheduling problems, which have some symmetries in machines. Multiple crossover and mutation operators are proposed, and only one search strategy combination, including one crossover operator and one mutation operator, is selected in each iteration. A population assessment method is provided to evaluate the evolutionary state of the population at the initial state and after each iteration. Two populations adopt different search strategies, in which the best search strategy is selected for the first population and the search strategy of the second population is selected under the guidance of Q-learning. Experimental results show that the dual-population genetic algorithm with Q-learning is competitive for solving multi-objective distributed hybrid flow shop scheduling problems. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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18 pages, 645 KiB  
Article
A Shuffled Frog-Leaping Algorithm with Cooperations for Distributed Assembly Hybrid-Flow Shop Scheduling with Factory Eligibility
by Deming Lei and Tao Dai
Symmetry 2023, 15(4), 786; https://doi.org/10.3390/sym15040786 - 23 Mar 2023
Cited by 5 | Viewed by 1328
Abstract
The distributed assembly scheduling problem with a hybrid-flow shop for fabrication is seldom studied, and some real-life constraints such as factory eligibility are seldom handled. In this study, a distributed assembly hybrid-flow shop-scheduling problem (DAHFSP) with factory eligibility is investigated, which has some [...] Read more.
The distributed assembly scheduling problem with a hybrid-flow shop for fabrication is seldom studied, and some real-life constraints such as factory eligibility are seldom handled. In this study, a distributed assembly hybrid-flow shop-scheduling problem (DAHFSP) with factory eligibility is investigated, which has some symmetries on machines. A shuffled frog-leaping algorithm with cooperations (CSFLA) is applied to minimize makespan. A problem-related feature is used. Memeplexes are evaluated, and group 1, with the two best memeplexes, and group 2, with the two worst memeplexes, are formed. A new cooperation between memeplexes and an adaptive search strategy are implemented in groups 1 and 2, respectively. An adaptive cooperation between groups 1 and 2 is also given. Population shuffling is executed every T generations. A number of computational experiments are conducted. Computational results demonstrate that new strategies are effective and CSFLA is a very competitive algorithm for DAHFSP with factory eligibility. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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18 pages, 3662 KiB  
Article
A Particle Swarm Optimization Method for AI Stream Scheduling in Edge Environments
by Ming Zhang, Luanqi Liu, Changzhen Li, Haifeng Wang and Ming Li
Symmetry 2022, 14(12), 2565; https://doi.org/10.3390/sym14122565 - 5 Dec 2022
Cited by 1 | Viewed by 1270
Abstract
With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of [...] Read more.
With the development of IoT and 5G technologies, edge computing has become a key driver for providing compute, network and storage services. The dramatic increase in data size and the complexity of AI computation models have put higher demands on the performance of edge computing. Rational and optimal scheduling of AI data-intensive computation tasks can greatly improve the overall performance of edge computing. To this end, a particle swarm algorithm based on objective ranking is proposed to optimize task execution time and scheduling cost by designing a task scheduling model to achieve task scheduling in an edge computing environment. It is necessary to fully understand the concept of symmetry of resource utilization and task execution cost indicators. The method utilizes nonlinear inertia weights and shrinkage factor update mechanisms to improve the optimization-seeking ability and convergence speed of the particle-to-task scheduling solution space. The task execution time and scheduling cost are greatly reduced. Simulation experiments are conducted using the Cloudsim toolkit to experimentally compare the proposed algorithm TS-MOPSO with three other particle swarm improvement algorithms, and the experimental results show that the task execution time, maximum completion time and total task scheduling cost are reduced by 31.6%, 23.1% and 16.6%, respectively. The method is suitable for handling large and complex AI data-intensive task scheduling optimization efforts. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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18 pages, 3964 KiB  
Article
Topology Optimization Design of an Active Deformable Mirror Based on Discrete Orthogonal Zernike Polynomials
by Yongming Liu, Yujian Rui, Zhuanzhe Zhao, Manman Xu and Yang Zhou
Symmetry 2022, 14(11), 2469; https://doi.org/10.3390/sym14112469 - 21 Nov 2022
Viewed by 1244
Abstract
In order to design an active deformation mirror for projection objective aberration imaging quality control, a topology optimization design method of active deformation mirrors based on discrete orthogonal Zernike polynomials is proposed in this paper. Firstly, in order to solve the problem that [...] Read more.
In order to design an active deformation mirror for projection objective aberration imaging quality control, a topology optimization design method of active deformation mirrors based on discrete orthogonal Zernike polynomials is proposed in this paper. Firstly, in order to solve the problem that continuous Zernike polynomials do not have orthogonality on the discrete coordinates inside the unit circle, which causes the instability of topology optimization results, discrete orthogonal Zernike polynomials are used to characterize the active deformation mirror wave aberrations. Then, the optical and structural deformations are combined to establish an optical-mechanical coupling topology optimization model with the help of the variable density method to derive the sensitivity of the mathematical model. Finally, a wave aberration corrected deformation mirror in an optical machine system is used as an arithmetic example for topology optimization, and the results show that the absolute value of the Zernike coefficient Z4 after optimization is improved by nearly one order of magnitude compared with the value before optimization, and the vibration characteristics of the optimized structure meet the design requirements. The optimization effect is significant, which improves the optical performance of the deformed mirror and provides a new scheme for the design of the deformed mirror structure which has a certain practical value for engineering. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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15 pages, 3675 KiB  
Article
Digital Hydraulic Design for Low-Specific-Speed Propeller Runners with Fixed Blades
by Youyu Liu, Qijie Wang, Dezhang Xu and Qing Chen
Symmetry 2022, 14(11), 2250; https://doi.org/10.3390/sym14112250 - 26 Oct 2022
Cited by 1 | Viewed by 1082
Abstract
The operating point of a propeller hydropower station will deviate from the effective workspace while the discharge reduces excessively during dry seasons. It usually leads to a decrease in efficiency and even to being unable to work. To solve the above problem, a [...] Read more.
The operating point of a propeller hydropower station will deviate from the effective workspace while the discharge reduces excessively during dry seasons. It usually leads to a decrease in efficiency and even to being unable to work. To solve the above problem, a scheme named decreasing capacity to increase efficiency was presented in this article. A low-specific-speed propeller runner with fixed blades that has the same installing dimensions as the original one was redesigned and equipped in dry seasons. A positive circulation at the outlet of the blades bigger than in conventional runners is allowed. Some key technologies about hydraulic design for runner blades were researched, which include distribution of velocity circulation at the inlet and outlet of the runner, thickening of the epiphyseal line of an aerofoil, unfolding aerofoil being converted to a cylindrical section, etc. In the section on digital modeling for runner blades, aerofoils on the cylindrical sections at the rim and at the hub were constructed employing the trend extrapolation method. Moreover, a blade digital model was built at one time according to the aerofoils on all cylindrical sections by means of a successful redevelopment to UniGraphics, and it has perfect symmetry. A case presented indicates that the method of decreasing capacity to increase efficiency is feasible. Using the method, the turbine efficiency increased from less than 28.6% to 83.4% while the discharge decreased from 3.20 m3s−1 to 1.00 m3s−1, and then the hydropower unit was able to work properly. Full article
(This article belongs to the Special Issue Meta-Heuristics for Manufacturing Systems Optimization Ⅱ)
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