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Multi-objective optimization of the solar absorptivity distribution inside a cavity solar receiver for solar power towers

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SOLAR ENERGY 2017年 158卷 247-258页

作者： Wang, Kun He, Ya-Ling Li, Peiwen Li, Ming-Jia Tao, Wen-Quan Xi An Jiao Tong Univ Sch Energy & Power Engn Minist Educ Key Lab Thermofluid Sci & Engn Xian 710049 Shaanxi Peoples R China Univ Arizona Dept Aerosp & Mech Engn Tucson AZ 85721 USA

The solar flux distribution on the receiver of a solar power tower is usually not uniform, which can cause a number of problems for the energy efficiency and system safety, particularly, the local hot spot and the thereby caused thermal stress and thermal deformation. Therefore, homogenization of the solar flux distribution is critical and important. The objective of the present study is to homogenize the solar flux distribution while keeping the optical loss (reflection loss) as low as possible through optimization of the distribution of the solar absorptive coating. An integrated approach coupling the Monte-Carlo ray tracing method and the Gebhart method is applied to simulate the process of the solar radiation transfer in the solar power system. The multi objective optimization of the distribution of solar absorptivity is performed by using the non-dominated sorting genetic algorithm. The following conclusions are drawn from the study. (1) The improvement of the uniformity of the distribution of solar flux can lead to more reflection loss due to the fact that more solar energy is distributed on the position with greater view factor to the aperture;(2) The Pareto optimal front obtained from the multi-objective optimization provides the trade-off between the non-uniformity of the solar flux distribution and the reflection loss. (3) The optimal solar absorptivity distribution provided by the Pareto optimal front can significantly flatten the solar flux distribution at a minimum cost of optical loss. (4) The optimal distribution of the solar absorptivity is approximately opposite to the distribution of solar flux projected onto the active surfaces from the heliostat fields.

关键词： Solar power tower Optimal solar absorptivity distribution Solar flux distribution non-dominated sorting genetic algorithm

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A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design

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APPLIED SOFT COMPUTING 2024年 150卷

作者： Ala, Ali Goli, Alireza Mirjalili, Seyedali Simic, Vladimir Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China Univ Isfahan Fac Engn Dept Ind Engn & Future Studies Esfahan Iran Torrens Univ Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze Univ Coll Engn Dept Ind Engn & Management Taoyuan Taiwan Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Healthcare supply chains play a crucial role, which enables the implementation of optimization strategies that have rapidly emerged as highly effective means for improving the overall structure of pharmaceutical and healthcare supply chains.. In the healthcare industry, parameters such as increasing the quality of service, as well as optimizing costs, environmental, and social factors play a unique role in supply chain management. To improve the healthcare supply chain network, this study proposed a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental impacts, while maximizing the social factors by creating jobs simultaneously. To address the effects of uncertain parameters, a fuzzy optimization method alongside the multi-objective gray wolf optimizer (MOGWO), non-dominated sorting genetic algorithm II (NSGA-II), multi-objective differential evolution algorithm (MODEA), and epsilon-constraint are applied to optimize the model. Also, a case study of the pharmaceutical industry demonstrates the model's efficacy in a real-life context. The numerical results show the MOGWO manages to create high-quality Pareto solutions with a good spread at the Pareto boundary within a short time compared to the epsilon-constraint approach. Further, it has shown a more robust performance compared to MODEA and NSGA-II, indicating the efficiency of MOGWO, among other solution methods and other objective indicators.

关键词： Healthcare Supply Chain Sustainability Multi-objective Gray Wolf Optimizer non-dominated sorting genetic algorithm Fuzzy Mathematical Programming Uncertainty Optimization algorithm

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Efficient economic energy scheduling in smart cities using distributed energy resources

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION

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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2024年 79卷

作者： Manzoor, Awais Akram, Waseem Judge, Malik Ali Khan, Naveed Khattak, Hasan Ali COMSATS Univ Islamabad Dept Comp Sci Islamabad 44500 Pakistan Univ Palermo Dept Engn I-90133 Palermo Italy Ulster Univ Fac Comp Engn & Built Environm Newtownabbey BT37 0QB North Ireland King Fahd Univ Petr & Minerals KFUPM Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Natl Univ Sci & Technol NUST Islamabad Pakistan

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2-A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2-B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids' peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing

关键词： Micro grid Demand response Multiobjective optimization Home energy management Distributed energy resources non-dominated sorting genetic algorithm

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A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria

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APPLIED MATHEMATICAL MODELLING 2016年第4期40卷 2674-2691页

作者： Niakan, Farzad Baboli, Armand Moyaux, Thierry Botta-Genoulaz, Valerie Univ Lyon INSA Lyon DISP EA4570 F-69621 Villeurbanne France

This paper examines a new multi-objective mathematical model in a Dynamic Cell Formation Problem (DCFP), where social criteria and uncertainty conditions are considered. Although corporate social responsibility is one of the important issues that are increasingly considered by researchers and practitioners, it is largely overlooked in the literature on DCFP. In this paper the first objective function minimizes costs related to a machine (machine fixed and variable costs, machine procurement and relocation costs, intra-cell and inter-cell movement costs) and wages, while social issues are maximized (e.g. potential machine hazards are minimized, while job opportunities are maximized). A robust counterpart of the proposed model is then developed by applying the recent robust optimization theory. Afterward, due to the NP-hardness of DCFP, a non-dominated sorting genetic algorithm (NSGA-II) as a meta-heuristic method is designed. Finally, two deterministic and robust mathematical formulations are compared by a number of nominal realizations under randomly generated test problems. This serves to assess the robustness of the solution achieved by the proposed robust optimization model. The obtained results demonstrate the ability of the robust model to reach appropriate solutions at all levels of uncertainty, specifically when a feasible solution cannot be found with the deterministic model. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Dynamic cell formation Robust optimization Corporate social responsibility Multi-objective optimization non-dominated sorting genetic algorithm

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Parametric optimization of micro-tool fabrication through sheet-EDG using nature-inspired algorithms

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第2期46卷 1-17页

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

Sheet electrical discharge grinding (sheet-EDG) is a recently developed novel method for producing high aspect ratio micro-tools. However, due to the presence of varying nature of initial tool eccentricity for fabricating each micro-tool, determining the optimal process parameters for the mentioned process is a difficult task. To overcome the above issue, an alternative experimental investigation called the slit-cut process was conducted, in which tool rotation was not used. Face-centered central composite design-based response surface methodology (RSM) was conducted to produce 20 slit-cuts on sheet electrodes with varying input parameters like voltage, sheet-EDG parameter, and radial infeed. Later, these slit-cuts were assessed and responses like volume removal rate (VRR) and electrode wear rate (EWR) were evaluated. An analysis of variance (ANOVA) is conducted for both VRR and EWR and statistically validated regression equations were developed. Also, the effects of various input parameters on responses are discussed based on the main effect plots and surface plots. Both VRR and EWR were simultaneously optimized using desirability function approach and a variety of nature-inspired optimization algorithms, including the multi-objective bonobo optimizer (MOBO), multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm (NSGA II). Based on the quality of the Pareto front, MOBO performed better than the other techniques. MOBO suggested changing the design ranges of voltage and radial feed from (140-180 V) to (140-156.8 V) and from (2-6 mu m/pass) to (5.2-6 mu m/pass), respectively.

关键词： Sheet electrical discharge grinding Micro-tool non-dominated sorting genetic algorithm Multi-objective particle swarm optimization Multi-objective bonobo optimizer Multi-objective grey wolf optimizer

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A multi-objective stochastic programming model for post-disaster management

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TRANSPORTMETRICA A-TRANSPORT SCIENCE 2022年第3期18卷 1103-1126页

作者： Gharib, Mehrdad Fatemi Ghomi, Seyyed Mohammad Taghi Jolai, Fariborz Amirkabir Univ Technol Dept Ind Engn 424 Hafez Ave Tehran *** Iran Univ Tehran Coll Engn Dept Ind Engn Tehran Iran

This paper develops a mathematical model for post-disaster planning with human casualties, which can be considered as operational guidance for the proper use of emergency resources. For this purpose, a stochastic mixed-integer programming model is provided to formulate the problem. The objective functions of the model are (1) maximizing the survival probability of patients, (2) minimizing the maximum of completion time of treatment of all patients, and (3) minimizing the total cost of operations. The model is solved with the epsilon-constraint method. Due to the NP-hardness of the problem which is a significant challenge in the literature, two innovative meta-heuristic algorithms are proposed, i.e. a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective simulated annealing (MOSA). Finally, a comprehensive computational analysis is performed for evaluation purposes. Also, a case study is made on the earthquake in Iran, which illustrates the real-world application of the model.

关键词： Emergency medical services multi-objective stochastic programming epsilon-constraint method multi-objective simulated annealing non-dominated sorting genetic algorithm

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Multi-objective optimization for scheduling multi-load automated guided vehicles with consideration of energy consumption

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 161卷

作者： Huo, Xiang He, Xiaozheng Xiong, Zhongxia Wu, Xinkai Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Robint Technol Co Ltd Xueyuan Rd Beijing 100191 Peoples R China Rensselaer Polytech Inst Dept Civil & Environm Engn Troy NY 12180 USA

The widespread implementation of modern logistics has led to the extensive use of multi-load automatic guided vehicles (m-AGVs) in automated sorting centers. This paper tackles one of the most challenging problems in this context, i.e., the scheduling of m-AGVs for express package handling. We comprehensively consider the loading and unloading delay of packages and energy consumption of m-AGVs, formulating the problem as a multi-objective mixed integer program (MO-MIP). To solve the proposed optimal scheduling problem, we apply the non-dominated sorting genetic algorithm (NSGA-II). A series of simulation experiments on three sorting centers have been further conducted to demonstrate the effectiveness and efficiency of the proposed model and solution algorithm.

关键词： Multi-load automatic guided vehicles (m-AGVs) Multi-objective mixed integer program (MO-MIP) Scheduling non-dominated sorting genetic algorithm (NSGA-II)

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The Performance Indices Optimization of a Symmetrical Fully Spherical Parallel Mechanism for Dimensional Synthesis

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2018年第3-4期90卷 305-321页

作者： Enferadi, Javad Nikrooz, Reza Islamic Azad Univ Dept Mech Engn Mashhad Branch Mashhad Iran

The 3(UPS)-S fully spherical parallel manipulator is the most famous fully spherical parallel robot. Therefore, dimensional synthesis of the manipulator to optimize kinematics and dynamic performance indices is very important. In this paper, we proposed a new kinematics index that is called global workspace conditioning index (GWCI). This index is a suitable criteria to compare workspace of the spherical parallel manipulators. Using this index, and other performance indices such as;Global Conditioning Index (GCI), Global Gradient Index (GGI) and Dynamic Dexterity Index (DDI), we optimize dimensions of the manipulator based on single and multi-objective optimizations. Two dimensionless design parameters are advised to obtain an optimal solution. The manipulator is optimized with regard to the joints constraint and range of motion actuators as functions of the design parameters. For this purpose, we elaborate kinematics analyses and also obtain the mass matrix of the manipulator using its kinetic energy. Next, single-objective optimizations based on genetic algorithm (GA) and Pattern Search (PS) are presented. The comparison between GA and PS in the single-objective optimization shows that the accuracy of both methods is rather equal, but in elapsed time PS is better than GA. Finally, multi-objective evolutionary algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the true optimal solutions and Pareto fronts. The obtained solution will be a set of optimal geometric parameters to adjust the dynamic and the kinematic performance.

关键词： Dimensional synthesis Global workspace conditioning index Performance indices genetic algorithm Pattern search non-dominated sorting genetic algorithm

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Add-on integration module-based proportional-integration-derivative control for higher precision electro-optical tracking system

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第6期43卷 1347-1362页

作者： Duan, Qianwen Mao, Yao Zhang, Hanwen Xue, Wenchao Chinese Acad Sci Key Lab Opt Engn Beijing Peoples R China Chinese Acad Sci Inst Opt & Elect Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS LSC Zhongguancongdonglu Rd 55 Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

This paper concerns the improvement on proportional-integration-derivative (PID) control for the electro-optical tracking system for high-mobility targets. To achieve higher tracking precision and stronger disturbance rejection while fully utilizing the existing PID loop, the add-on integration module is proposed and seamlessly integrated into the conventional PID loop. It is proven that for any given conventional PID controller parameters, the add-on integration module based PID control can improve the ability of error attenuation at low frequency and keep the stability of resulting closed-loop system. More importantly, the feasible set of all parameters in the added module is explicitly given. Based on the feasible set, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to obtain the globally optimal controller's parameter under certain performance indices. The experiments are carried out for a typical electro-optical tracking test bed with several reference signals. It is shown that the proposed method has much smaller tracking errors than the existing PID method.

关键词： Proportional-integration-derivative control electro-optical tracking system motion control disturbance rejection non-dominated sorting genetic algorithm

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Combining local search into non-dominated sorting for multi-objective line-cell conversion problem

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2013年第4期26卷 316-326页

作者： Yu, Yang Tang, Jiafu Sun, Wei Yin, Yong Kaku, Ikou Northeastern Univ Inst Syst Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Liaoning Univ Coll Business Adm Shenyang 110136 Peoples R China Yamagata Univ Dept Econ & Business Management Yamagata 9908560 Japan Tokyo City Univ Fac Environm & Informat Studies Yokohama Kanagawa 2248551 Japan

Multi-objective decision-making is often used in line-cell conversion problem. It is the key issue of developing line-cell conversion successfully. In this article, a multi-objective optimisation model is proposed to investigate two line-cell conversion performances: the total throughput time and the total labour hours. It is proved that the model is a non-deterministic polynomial (NP)-hard problem and its Pareto-optimal front is non-convex. Owing to the properties of cell formation and cell loading in line-cell conversions, an approach of combining local search into non-dominated sorting genetic algorithm II is proposed to obtain better non-dominated solutions. Several numerical simulation experiments are performed to show the utility and performance of our approach.

关键词： line-cell conversion multi-objective optimisation local search non-dominated sorting genetic algorithm

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