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Study on flexible job shop scheduling problem considering energy saving

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第3期46卷 5493-5520页

作者： Xiao, Yanjun Yin, Shanshan Ren, Guoqing Liu, Weiling Hebei Univ Technol Sch Mech Engn Tianjin Peoples R China Career Leader Intelligent Control Automat Co Suqian Jiangsu Peoples R China Hebei Univ Technol Tianjin Key Lab Power Transmiss & Safety Technol Sch Mech Engn Tianjin Peoples R China

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation.

关键词： Flexible job shop scheduling energy consumption non-dominated sorting genetic algorithm sparsity theory neighborhood search

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Additional Acceleration of Antenna Optimal Characterization with Modeling Support

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2024年第5期39卷 396-404页

作者： Uluslu, Ahmet Istanbul Univ Cerrahpasa Dept Elect & Automat TR-34500 Istanbul Turkiye

Optimal characterization is assumed to provide the best solution for the designed cost function among the possible solutions within the specified range. These processes can take a long time depending on the applications and computer hardware used. Here, the optimization process is supported by ANN modeling in order to shorten the current optimization processes as much as possible. For this purpose, the selection of design parameters of the bowtie patch antenna (BPA) is presented as a multi-dimensional, multi-objective modeling-supported design optimization problem. The operating frequency of the proposed antenna is 28 GHz, which is the standard for millimeter wave band and 5G technologies. To overcome this challenging design optimization, a new, fast and powerful optimization algorithm was used by modifying the non-dominant sorting genetic algorithm (NSGA)-III, and the optimal characterization of the microwave antenna design was achieved. Although the proposed method gives the same results compared to the existing process, it takes much less time. Therefore, it is possible to shorten the process and reduce costs without the need for extra applications or hardware. As a whole, the proposed design optimization process is an efficient, fast and reliable solution for all design problems.

关键词： Accelerated optimal characterization antenna applications modeling support non-dominated sorting genetic algorithm optimization

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Multi-objective optimization in electrochemical micro-drilling of Ti6Al4V using nature-inspired techniques

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MATERIALS AND MANUFACTURING PROCESSES 2023年第15期38卷 1942-1954页

作者： Acharya, Biswesh Ranjan Sethi, Abhijeet Das, Amit Kumar Saha, Partha Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 India Natl Inst Ind Engn NITIE Mumbai India

Current study investigates the efficacy of electrochemical micro-drilling (ECMD) process during micro-hole fabrication on a Ti6Al4V alloy sheet of 1.2 mm thickness using a cylindrical-shaped micro-tool. Ethylene glycol-based electrolyte was selected to overcome excessive passivation. Experiments were conducted by altering voltage, feed rate (FR), and electrolyte concentration (EC) using a central-composite-design to observe their effects on the output parameters. The desired response parameters, such as radial overcut (ROC), and taper were examined while fabricating dimensionally accurate micro-holes including the volume removal rate (VRR) that occurred during the process. ANOVA results established voltage as the major significant input parameter that affects all the responses, followed by FR. In addition, different nature-inspired optimization techniques, namely Multi-Objective Grey-Wolf Optimizer (MOGWO), Multi-Objective Bonobo Optimizer (MOBO), and Multi-Objective Particle Swarm Optimization (MOPSO), were employed. MOBO reported the best Pareto-surface in terms of uniformity and spread measures and suggested a FR of 0.3 mu m/s for optimal micro-drilling operations.

关键词： Ti6Al4V Electrochemical micro-drilling Multi-objective bonobo optimizer Multi-objective particle swarm optimization Multi-objective grey wolf optimizer non-dominated sorting genetic algorithm

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Multiobjective Optimization for Vehicle Routing Optimization Problem in Low-Carbon Intelligent Transportation

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第11期24卷 13161-13170页

作者： Yin, Nan Suzhou City Univ Dept Business Adm Suzhou 215104 Peoples R China Nanjing Univ Sci & Technol Sch Econ & Management Nanjing 210094 Peoples R China

The work aims to reduce the energy consumption and carbon emissions generated during the urban logistics transportation and distribution and make the actual path planning flexible. Based on Vehicle Routing Problem (VRP), the routing problem of distribution vehicles is optimized under satisfying customers' cargo demand and time requirements. Because non-dominated sorting genetic algorithm (NSGA-II) reduces the complexity of non-inferior sorting genetic algorithm and is characterized by fast running speed and good convergence, it is deeply improved. NSGA-II algorithm based on Multifactorial Evolutionary algorithm (MFEA) (M-NSGA-II) is proposed. In terms of the solution of the stability of the optimal values of four target functions, including distribution cost, customer satisfaction, fuel conservation, and carbon emission, the lowest distribution costs of M-NSGA-II algorithm in ten experiments were all lower than those of other three standard algorithms. The solution duration of M-NSGA-II algorithm was 85.2s and the corresponding average frontier value amounted to 20. The multi-objective path optimization model designed is of great value for reducing carbon emissions under satisfying customers' cargo demand and time requirements.

关键词： Carbon dioxide Optimization Costs Green products Vehicle routing Smart transportation genetic algorithms Intelligent transportation vehicle routing optimization multi-objective optimization green and low-carbon non-dominated sorting genetic algorithm

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The Impact of Dual Objective Optimization Model Combining non-dominated genetic algorithm on Rural Industrial Ecological Economy

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 271-280页

作者： Wang, Ying Shaanxi Tech Coll Finance & Econ Sch Accounting Xianyang 712000 Peoples R China

Due to the development of industrial economy, it has caused serious damage to the ecological environment. Based on the industrial structure and production scale, rural industrial economic parks are planned to analyze the quantity and weight of pollutants emitted from the original industries. The results showed that the quantity and weight of hydrogen sulfide in the coking industry were 10kg/t and 94, respectively. The weight of smoke and carbon monoxide in the steelmaking industry was relatively high, with 54 and 34, respectively. non-dominated sorting genetic algorithm and multi-objective programming model are used to optimize the comprehensive benefits and industrial structure of rural industrial ecological economy. According to the experimental results, when the scale of the coking industry was 135600 tons, the steelmaking industry was 314900 tons, the ironmaking industry was 148100 tons, and the underground coal gasification industry was 424.76 million Nm3. The comprehensive economic benefits of the industry reached the optimal level of 0.6415. The environmental and comprehensive benefits generated by the increased power generation industry were 64.98 and 40.87, respectively. Therefore, it indicates that the dual objective programming model combining non-dominated sorting genetic algorithm can improve the rural industrial ecological economy.

关键词： Industrial chain production mode ecological economy environmental benefits non-dominated sorting genetic algorithm dual objective programming model

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System design and Optimization of Mobile Edge Computing in the NOMA Wireless Tactile Internet of Things Network

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ALEXANDRIA ENGINEERING JOURNAL 2023年 73卷 737-749页

作者： Truong, Truong Van Nayyar, Anand Bilal, Muhammad Kwak, Kyung Sup Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Gradute Sch Da Nang 550000 Vietnam Hankuk Univ Foreign Studies Dept Comp Engn Yongin 17035 South Korea Inha Univ Dept Informat & Commun Engn Incheon 22212 South Korea

Mobile edge computing (MEC) is an essential technique in next-generation networks to serve ultra-low latency and computation-intensity applications. At the same time, nonorthogonal multiple access (NOMA) is a technique to help multi-user service, saving energy and increasing spectrum efficiency. In this study, we investigate the NOMA MEC-based wireless Tactile Internet of Things (IoT) network and propose the optimization algorithms for system and users perfor-mance: We propose a network model consisting of a MEC server at the access point that supports computation for two sensor clusters in the Tactile IoT environment. We analyze the performance of the system and cluster heads (CHs) using the successful computation probability (SCP). Asymptotic SCP at high SNRs was analyzed and compared by us to give a better view of the system's behavior. Then, we maximize the SCP of the proposed system and simultaneously maximize the SCP of the CHs to clarify the performance trade-off problem in the NOMA MEC network by proposing low -complexity meta-heuristic algorithms. Monte-Carlo simulation results show that our proposed approach can significantly improve system performance by up to 30% compared to OMA tradi-tional methods.(c) 2023 Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Mobile edge computing non -orthogonal multiple Successful computation genetic algorithm Tactile IoT Multiuser Best user selection access probability non-dominated sorting genetic algorithm III

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Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation

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ENERGIES 2023年第13期16卷 4896页

作者： Bhurt, Faraz Ali, Aamir Keerio, Muhammad U. Abbas, Ghulam Ahmed, Zahoor Mugheri, Noor H. Kim, Yun-Su Quaid E Awam Univ Engn Sci & Technol Dept Elect Engn Sindh 67450 Pakistan Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Balochistan Univ Engn & Technol Dept Elect Engn Khuzdar 89100 Balochistan Pakistan Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain;therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature.

关键词： non-dominated sorting genetic algorithm renewable power sources optimal reactive power dispatch probability distribution function

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Stacking-based multi-objective approach for detection of smart power grid attacks using evolutionary ensemble learning

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURES 2024年第3期20卷 195-215页

作者： Panthi, Manikant Das, Tanmoy Kanti Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Chhattisgarh India

Smart power grid (SPG) has gained a reputation as the advanced paradigm of the power grid. It provides a medium for exchanging real-time data between the company and users through the advanced metering infrastructure delivering transparent and resilient service to electricity consumers. The widespread deployment of remotely accessible networked equipment for grid monitoring and control has vastly increased the surface of SPG for attackers to locate vulnerable points. The early and accurate identification of the above counteracts is paramount to ensure stable and efficient power distribution. This paper proposes a stacking-based multi-objective evolutionary ensemble scheme to identify various attacks in the SPG. The proposed method used a non-dominated sorting genetic algorithm to learn the non-linear, overlapping, and complex electrical grid features to predict the type of malicious attacks. The experimental results and comparison using multiclass dataset validate the presented 'Stacking-NSGA-II' approach notably outperformed the others benchmark classifiers.

关键词： non-dominated sorting genetic algorithm cyber-attack power grid machine learning

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Multi-objective optimization configuration of redundant electromagnetic actuators in fault-tolerant control of active magnetic bearing system

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ISA TRANSACTIONS 2023年 140卷 293-308页

作者： Deng, Shuai Cheng, Xin Wu, Huachun Hu, Yefa Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China Hubei Prov Engn Technol Res Ctr Magnet Suspens Wuhan Peoples R China

Fault-tolerant control of active magnetic bearing (AMB) systems with redundant electromagnetic actuators (EMAs) based on generalized bias current linearization has become a practical technique to address EMA/amplifier faults. In this method, the configuration of multi-channel EMAs involves solving a high-dimensional and nonlinear problem containing complex constraints offline. This article develops a general framework for the EMAs multi-objective optimization configuration (MOOC) by combining the non-dominated sorting genetic algorithm III (NSGA-III) and the sequential quadratic programming (SQP) with the designing of objectives, handling of constraints, consideration of the iterative efficiency and the diversity of solutions. The numerical simulation results confirm the feasibility of the framework for searching the non-inferior configurations and reveal the function mechanism that intermediate variables of the nonlinear optimization model on AMB performance. Finally, the best configurations identified using the technique for order preference by similarity to an ideal solution (TOPSIS) are applied to the 4-DOF AMB experimental platform. Experiments further indicate that the work in this paper provides a novel way with good performance and high reliability for solving the EMAs MOOC problem in fault-tolerant control of AMB systems. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Active magnetic bearing (AMB) Electromagnetic actuators (EMAs) Multi-objective optimization non-dominated sorting genetic algorithm

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Multi-objective mixed mode sugarcane transportation model using fuzzy NSGA

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OPSEARCH 2025年第1期62卷 149-177页

作者： Kurade, Sandesh Latpate, Raosaheb Gedam, Vinayak MES Abasaheb Garware Coll Autonomous Dept Stat Karve Rd Pune 411004 India Savitribai Phule Pune Univ Dept Stat Pune 411007 India

The sugar factory business plays a significant role as a source of employment for the developed countries like India, Brazil, South Africa etc. Sugar factory business in India impacts the livelihood of almost each and every sugarcane farmers and workers employed in these factories. Hence in this paper, a multi-objective mixed mode transportation model for sugarcane industry is formulated. To make the model realistic, uncertainty in costs, time, supply and demand are considered. Uncertainty in these key parameters of the sugarcane industry are occur due to competitive nature of the sugar market. The sugar industries located to nearby places to the market always have huge impact on the sugar market. In the formulated model, parameters are expressed by means of triangular fuzzy number due to uncertainty in the market. Various modes of transportation with their restricted capacities like bullock carts, trucks, tractors, tractor trolleys etc. are considered in the developed model. For solving the model, a new optimization algorithm using fuzzy set theory, convex set theory and evolutionary algorithm is developed. For applicability of the model, a real world case study of supply chain of the co-operative sugarcane factory located in Maharashtra state for season 2018 is considered. Finally, Pareto decision space of the model is discussed at different levels of uncertainty. The developed model has several industrial applications. It will be helpful to sugar factories to optimize their supply chains.

关键词： Mixed mode Multi-objective non-dominated sorting genetic algorithm Supply chain Transportation Triangular fuzzy number

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