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Designing a fuzzy sliding mode controller for a three-phase inverter via genetic algorithm

INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第5期11卷 2452-2465页

作者： Taran, Babak Mohajeri, Hamed Tarbiat Modares Univ Dept Mech Engn Jalal AleAhmad Highway POB 14115-111 Tehran Iran Univ Zanjan Dept Elect Zanjan Iran

In a three-phase inverter, if the electricity consumers' loads are not equal, balance will be disturbed and switching will be challenged. To overcome this problem, herein, a fuzzy sliding mode control is proposed to control the output voltages of a three-phase inverter. Control methods for commanding IGBTs have advantages over methods such as PWM and SVM as the effect of errors and uncertainties is neutralized by measuring the output error. Sliding mode control has long been used to control systems with uncertainty in modeling. This controller is robust because they provide sliding conditions by generating discontinuous control signals around the sliding surface. Despite these advantages, sliding mode control has its drawbacks. A popular method to solve these problems is fuzzy control, which promotes controller performance. In addition to paying attention to reducing the output error in inverters, changes in the state of switches should also be considered. In practice, the power loss increases and the inverter efficiency decreases, with increasing the disconnection and reconnection of IGBTs. In the present study, a multi-objective genetic algorithm is used so as to select the optimal control coefficients, in such a way that a compromise is made between the error and the number of times the keys are connected and disconnected.

关键词： Three-phase inverter Sliding mode control Fuzzy control multi-objective genetic algorithm

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Toward green container liner shipping: joint optimization of heterogeneous fleet deployment, speed optimization, and fuel bunkering

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2025年第6期32卷 3347-3384页

作者： Zhao, Yuzhe Peng, Zhongxiu Zhou, Jingmiao Notteboom, Theo Ma, Yiji Dalian Maritime Univ Collaborat Innovat Ctr Transport Studies Dalian 116026 Peoples R China Dalian Univ Foreign Languages Business Sch Dalian 116044 Peoples R China Univ Ghent Maritime Inst B-9000 Ghent Belgium Univ Antwerp Fac Business & Econ B-2000 Antwerp Belgium Antwerp Maritime Acad Fac Sci B-2030 Antwerp Belgium

Container liner shipping companies, under the international shipping carbon reduction indicators proposed by the International Maritime Organization, must transform two key aspects: technology and operations. This paper defines a green liner shipping problem (GLSP) that integrates the deployment of a heterogeneous fleet, speed determination, and fuel bunkering. The objective is to achieve low-carbon operations in liner shipping, taking into consideration the diversification of power systems, the use of alternative fuels in ships, and the continuous improvement of alternative fuel bunkering systems. For this purpose, we present a bi-objective mixed integer nonlinear programming model and develop two methodologies: an epsilon-constraint approach and a heuristic-based multi-objective genetic algorithm. We validate the effectiveness of our model and methods through a case study involving container ships of various sizes deployed on intra-Asian short sea routes by SITC International Holdings Co., Ltd. The experimental results highlight the crucial role of dual-fuel (DF) ships in the pursuit of low-carbon strategies by liner companies, with liquefied natural gas and ammonia DF ships being the most widely used. Additionally, fuel cell (FC) ships, particularly those powered by ammonia and hydrogen, demonstrate significant carbon reduction potential. Furthermore, ships with larger container capacities have a greater cost advantage. For the GLSP, speed determination is an auxiliary decision, and the lowest speed is not necessarily the optimal choice. Decision-makers must carefully balance competing economic and carbon emission reduction objectives, as deploying more alternative fuel ships may increase fuel bunkering and fuel consumption, resulting in a higher total operating cost.

关键词： liner shipping heterogeneous fleet deployment speed determination fuel bunkering carbon emission bi-objective mixed integer nonlinear programming multi-objective genetic algorithm

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multi-objective optimization of sandwich structures for reinforcing composite fuselages

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JOURNAL OF SANDWICH STRUCTURES & MATERIALS 2024年第5期26卷 567-585页

作者： Albanesi, Alejandro Eduardo Volpe, Nahuel Jose Langhi, Federico Fachinotti, Victor Daniel CONICET Santa Fe CIMEC Ctr Invest Metodos Computac Predio CONICET Santa Fe Santa Fe Argentina CONICET Santa Fe CIMEC Ctr Invest Metodos Computac Col Ruta 168 s-n RA-3000 Santa Fe Argentina

This study introduces a simulation-based optimization approach that combines a multi-objective genetic algorithm and the finite element method to design sandwich structures to reinforce composite fuselages. The sandwich structure has been parametrized with a set of mixed integer-continuous variables representing the polymer core thickness, the number and weight of laminated fiberglass layers, and size and position of the structure. Automatic scripting builds the geometry of the structure, then mounts it in the fuselage, and finally creates a conformal mesh. During the optimization, each reinforced fuselage is subjected to several load cases specified by the CS-22 EASA design standard, determining the worst load condition. Two objectives were considered: increasing the reinforced fuselage's minimum fiber stress safety factor for improved safety and reducing the number of layers of the sandwich structure to minimize manufacturing costs. Structural constraints were the mass of the sandwich structure and buckling and flexo-torsional deformation of the reinforced fuselage. Results show an efficient material arrangement due to the reduced number of layers in the sandwich structure and a stress safety factor surpassing that set by the standard. Finally, the reinforcement of a glider fuselage to incorporate a retractable electric propulsion system is presented as a real-world industrial application.

关键词： Sandwich structures multi-objective genetic algorithm optimization finite element method composite fuselage reinforced fuselage

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The robotic assembly line balancing problem under task time uncertainty

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第5-6期137卷 2991-3011页

作者： Zacharia, Paraskevi Nearchou, Andreas C. Univ West Attica Dept Ind Design & Prod Engn Athens 12244 Greece Univ Patras Dept Business Adm Patras Achaia Greece

Consideration is given to the robotic assembly line balancing problem (RALBP) under uncertain task (operation) times, a critical challenge encountered in automated manufacturing systems.. RALBP is a decision problem which seeks the optimal assignment of the assembly work as well as the most suitable robots to the workstations of the assembly line with respect to objectives related to the capacity of the line or/and its cost of operation. When multiple types of robots with different capabilities are being used, task times may vary depending on robot type and the nature of the task. Task variation is expected to be small for simple tasks but may be quite large for complex and failure sensitive operations. To deal with uncertainty in task variation we used fuzzy logic theory. First, we introduce formally the fuzzy RALBP and then we describe deeply the fuzzy representation of the task times. We address RALBP with respect to two optimization objectives namely, the production rate and workload smoothing. Since the problem is known to be NP-hard, we explore its heuristic solution through a new robust multi-objective genetic algorithm (MOGA) aiming to determine the Pareto optimal set. Simulation experiments assess MOGA's efficiency in comparison to the famous NSGA-II and MOPSO algorithms, while also exploring the trade-off between the two conflicting objectives.

关键词： Robots Flow lines Fuzzy task times multi-objective genetic algorithm Pareto optimization Metaheuristics

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An innovative optimal integrated solar-lignocellulosic biomass polygeneration system with biorefinery and solid oxide electrolyzer cell

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ENERGY CONVERSION AND MANAGEMENT 2025年 327卷

作者： Morid, Mehdi Manesh, Mohammad Hasan Khoshgoftar Univ Qom Energy Environm & Biol Syst Res Lab EEBRlab Div Thermal Sci & Energy Syst Dept Mech EngnFac Technol & Engn Qom Iran

Polygeneration systems combine various energy sources and processes to produce multiple products for residential and industrial needs in a unified system, offering enhanced sustainability and resilience compared to single-process systems. Optimizing these systems maximizes energy resource utilization. This study introduces a comprehensive polygeneration system generating eight products to satisfy power, cooling, heating, and secondary product requirements like fresh water, hydrogen, oxygen, carbon dioxide, and ethanol. The system is based on two renewable energy sources, solar and biomass, as well as traditional methane energy. Through a comprehensive evaluation encompassing energy, exergy, exergo-economic, exergo-environmental, emergoeconomic, and emergo-environmental considerations, the system is thoroughly evaluated. Mathematical modeling in MATLAB assesses thermal, economic, and ecological factors for optimization. Validation is performed using Thermoflex and Aspen Plus for an ethanol and power co-production biorefinery from corn stover. multi-objective optimization employs a machine learning and genetic algorithm approach in two phases. The initial phase targets three objectives for the hybrid multi-effect distillation adsorption desalination unit to optimize fresh water production, performance ratio, and energy consumption. Our results demonstrate that in the optimal scenario, there is an 8.2% increase in fresh water production rate, a 10.2% increase in performance ratio, and a 9.2% reduction in specific energy consumption. In the second phase, the overall system is optimized with the objective of maximizing thermodynamic performance while minimizing economic and environmental indicators. The six-objective optimization results in a 2.54% increase in overall energy efficiency, a 3.38% increase in overall exergy efficiency, and a 59.66% improvement in the coefficient of performance of the ejector refrigeration cycle. Additionally, it leads to a 1.88% reduction i

关键词： multi-objective genetic algorithm Solid Oxide Electrolysis Cell Recurrent Neural Network Optimization Bioethanol Biorefinery Renewable energy

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A High-Reliability Small-Area Task Offloading Mechanism With Trust Evaluation and Fuzzy Logic in Power IoTs

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第4期24卷 2935-2948页

作者： Wang, Suhong Qin, Tuanfa Chen, Tingting Guo, Wenhao Hu, Yongle Sun, Hongmin Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China Guangxi Univ Sch Comp Elect & Informat Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Guangxi Univ Sch Elect Engn Guangxi Key Lab Multimedia Commun & Network Techno Nanning 530004 Peoples R China Runjian Co Ltd Nanning 530007 Peoples R China Guangxi Vocat Tech Inst Ind Nanning 530001 Peoples R China

In order to solve the problem that high-priority tasks can not be processed timely and reliably due to the disorder of multi-task and dynamicity in Power Internet of Things(PIoTs), a high-reliability small-area task offloading mechanism with trust evaluation and fuzzy logic(HRSATF) is proposed. First, considering task priority, preemptive priority queue is introduced to ensure high-priority tasks processed preferentially, and minimum resource allocation coefficients(MRACs) of tasks are solved to ensure the effectiveness of offloading. Second, the trust model between smart device(SD) and edge server(ES) is established, and ESs are divided into three priorities based on trust value and computing power by fast non-dominated sorting. Thirdly, fuzzy logic is applied to select target ES when the priorities of task and ES do not match or the ES is offline, and MRAC is used to schedule tasks between SD and ES. Finally, NSGA2 is modified (MNSGA2) to verify the effectiveness of HRSATF in terms of success rate, time, power consumption and load balancing, where success rate is increased by 102.3%, and time and power consumption are decreased by 90.7%, 89.3% at most, respectively.

关键词： Fuzzy logic Costs Reliability Blockchains Resource management Mobile computing Computational modeling Cloud computing Smart grids Delays multi-objective genetic algorithm preemptive priority resource allocation task offloading trust evaluation

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A novel multi-objective optimization model for designing a reverse supply chain network

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JOURNAL OF CLEANER PRODUCTION 2025年 499卷

作者： Momeni, Mojtaba Arab Mostofi, Amirhossein Jain, Vipul Chan, Felix T. S. Persian Gulf Univ Jam Fac Engn Bushehr Iran Auckland Univ Technol Dept Management Org & Technol Auckland New Zealand RMIT Univ Melbourne Coll Business & Law Sch Accounting Informat & Supply Chain Dept Logist & Supply Chain Management Melbourne Australia Macau Univ Sci & Technol Sch Business Dept Decis Sci Taipa Macao Peoples R China

Reverse supply chain management is one of the most effective ways to protect the environment and conserve natural resources while generating significant economic benefits for manufacturing firms. This paper examines the design of a reverse supply chain consisting of various facilities, including collection, inspection, and recycling centers, internal and external remanufacturing plants, suppliers, and secondary markets. The supply chain aims to minimize costs and carbon emissions. Additionally, the proposed model is extended to a multi-objective robust stochastic-based model to consider the uncertainty in the amount and quality of return products. Furthermore, to derive the Pareto solutions, two methods, epsilon- constraint, and the Metaheuristic algorithm of the multi-objective genetic algorithm are employed. In the multi-objective genetic algorithm, a new representation of solutions is provided, considering all dependencies between the variables and embodying the concept of robustness in the solutions of the Metaheuristic algorithms. According to the research findings, the Metaheuristic algorithm could compete with the exact method of epsilon- constraint in small-size problems, while the multi-objective genetic algorithm outperforms it in large-size problems. On the other hand, a sensitivity analysis reveals that revenue and emission-related parameters influence the level of expansion in the reverse supply chain, while the standard deviation of uncertain parameters mostly affects the robustness of the solutions. A balanced capacity throughout the supply chain is also recognized as essential for improving profitability and flexibility. Additionally, the model results showed that reducing emissions related to facilities can create opportunities for all the identified objectives within the supply chain.

关键词： Reverse supply chain Robust optimization epsilon- constraint method multi-objective genetic algorithm

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Sustainable Water Optimization Tool (SUWO): An Optimization Framework for the Water-Energy-Food-Ecosystem Nexus

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WATER 2025年第9期17卷 1280-1280页

作者： Yaykiran, Salim Ekdal, Alpaslan Istanbul Tech Univ Environm Engn Dept TR-34469 Maslak Turkiye

Sustainable water management requires integrated approaches balancing competing demands and environmental sustainability. This study introduces the Sustainable Water Optimization Tool (SUWO), an open-source, Python-based simulation-optimization framework for basin-scale surface-water-resources management. SUWO employs the water-energy-food-ecosystem (WEF-E) nexus approach, utilizing a multi-objective genetic algorithm (MOGA) to generate Pareto-optimal solutions and facilitate a trade-off analysis among water uses through simulations of reservoir operations, hydro-energy production, irrigation, and flow regulation. SUWO integrates scenario analysis with multi-criteria decision making (MCDM), enabling the evaluation of various management, climate, and environmental scenarios. The framework was applied to the Sakarya River Basin (SRB) in T & uuml;rkiye, a rapidly developing region pressured by water infrastructure development, hydroelectric power plants (HEPPs), and irrigation expansion. The SUWO-SRB model showed that while Non-dominated Sorting genetic algorithm II (NSGA-II) generally exhibited superior performance, NSGA-III presented a competitive alternative. The optimization results were analyzed across four management scenarios under varying hydrological conditions and environmental management classes (EMCs) for the near future. The model results highlight WEF-E nexus trade-offs. Maximizing energy production often impacts irrigation and the ecosystem, while prioritizing sustainable irrigation can reduce energy output. Dry conditions reduce hydropower and irrigation capacity, emphasizing water scarcity vulnerabilities. Ecological deviation negatively correlates with anthropogenic factors.

关键词： water resources management multi-objective optimization multi-objective genetic algorithm WEF-E nexus trade-off analysis environmental flows reservoir operation irrigation Sakarya Basin Python

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Energy, exergy, economy analysis and multi-objective optimization of SOFC/GT/SCO2 hybrid power system for ships based on zero-carbon emissions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1430-1449页

作者： Xia, Minjie Li, Chen Yao, Shouguang Yan, Xuan Wang, Chuang Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212000 Peoples R China Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China Wuhan Univ Technol Sch Traff & Transportat Engn Wuhan 430063 Peoples R China

LNG-powered ships have gradually become the mainstream of green shipping because of the high efficiency and emissions reduction, but cannot realize zero-carbon emissions operation. Therefore, a SOFC/GT/SCO2 hybrid power system for ships based on oxygen-enriched combustion carbon capture technology is proposed to achieve efficient and zero-carbon emissions operation for ships. The system is evaluated based on the thermoeconomic method, the effects of system operating parameters on system performance are investigated, and the system is optimized based on parametric analysis utilizing a multi-objective genetic algorithm. The results demonstrate that the higher operating temperature of the reformer increases the system net output power and reduces the system electricity production cost. The system net output power is maximized and the electricity production cost is minimized when the operating temperature of SOFC is 800 degrees C. For the SCO2 cycle, the selection of appropriate operating parameters is essential for the efficient operation of the system. The system achieves 7276.182 kW net power generation, 63.00% exergy efficiency, and 0.0774$/kWh electricity production cost after multi-objective optimization.

关键词： multi-objective genetic algorithm Thermoeconomic method Oxygen-enriched combustion carbon capture technology LNG-Powered ship SOFC/GT/SCO2 hybrid power system

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A Fault-Tolerant Scheduling algorithm Based on a multi-objective genetic algorithm for Precedence-Constrained Tasks in Real-Time Heterogeneous Distributed Systems

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第5期10卷 1091-1096页

作者： Chen Yuanlong Ma Peijun Li Dong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

In this paper, we investigate an efficient off-line fault-tolerant scheduling algorithm that generates schedules in which real-time tasks with precedence constraints can tolerate more than one fault at a time in a heterogeneous system with a fully connected network. We assume tasks to be non-preemptable, and each task has two copies scheduled on different processors and mutually excluded. Our fault-tolerant scheduling algorithm based on non-domination sort genetic algorithm (NSGA) employs performance improving techniques, including (1) a dynamic distance concept that decides the relative position of two task copies in the task graph, (2) a multi objectives genetic algorithm to optimise multiple targets. We performed experiments using synthetic workloads and a real-world application. Simulation results show that compared with existing scheduling algorithms, our scheduling algorithm improves guarantee ratio, reliability and overhead.

关键词： Heterogeneous Distributed System Dynamical Distance Fault Tolerant Scheduling multi-objective genetic algorithm

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