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Bi-objective overlapped links vehicle routing problem for risk minimizing valuables transportation

COMPUTERS & OPERATIONS RESEARCH 2023年第1期153卷

作者： Mazdarani, Fatemeh Ghannadpour, Seyed Farid Zandieh, Fatemeh Iran Univ Sci & Technol Dept Ind Engn Tehran *** Iran

This paper presents a novel variant of the vehicle routing problem (VRP) that considers overlapped links for the transportation of valuables. Maintaining security and minimizing route risks are crucial challenges in the planning and routing of valuable goods whose transport is threatened by risks such as armed robbery and assault. Keeping the transit process secure necessitates, therefore, special planning and routing. A more realistic exam-ination of real-world situations reveals that network routes are not entirely independent and share sections that influence one another. In other words, changing the status of one street section simultaneously affects multiple links. Consequently, a bi-objective overlapped links vehicle routing problem (OLVRP) for valuable goods transportation is developed in this study, where the total cost and risk of routing are minimized. Moreover, a hybrid genetic algorithm is developed to optimize the problem, and its efficiency and effectiveness for small-to -medium and large-scale problems are evaluated. The results demonstrate that the proposed algorithm is superior and highly efficient compared to The non-dominated sorting genetic algorithm II (NSGA-II). Finally, the pro-posed model is used to conduct a real-world case study on cash distribution in Tehran province, Iran, one of the most widely used types of valuable goods transportation.

关键词： Vehicle routing problem Risk Valuable goods Overlapped link Cash-in-transit multi-objective genetic algorithm

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Optimal Design of Intermittent Water Distribution Network Considering Network Resilience and Equity in Water Supply

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WATER 2023年第18期15卷 3265页

作者： Ramani, Katineni Rudraswamy, G. K. Umamahesh, Nanduri V. Natl Inst Technol Warangal Dept Civil Engn Hanamkonda 506004 Telangana India

In urban areas of developing countries, due to industrialization and population growth, water demand has been increasing significantly, thereby increasing stress on the existing water distribution systems (WDSs). Under these circumstances, maintaining equity in the allocation of water becomes a significant challenge. When building an intermittent water distribution system, it is important to provide a minimum level of supply that is acceptable as well as water supply equity. A non-dominated sorting genetic algorithm (NSGA-II) is employed for the optimal design of an intermittent water distribution network (WDN). Network resilience is taken as a measure of reliability (In), while the uniformity coefficient (CU) is taken as a measure of equity in the water supply. Maximizing network resilience, uniformity coefficient, and minimization of cost of the network are considered as the objectives in the multi-objective optimization model. Pressure-driven analysis (PDA) is used for the hydraulic simulation of the network. The NSGA-II model is applied and demonstrated over two water distribution networks taken from the literature. The results indicate that reliability and equity in WDNs can be accomplished to a reasonable extent with minimal cost.

关键词： water distribution networks intermittent supply uniformity coefficient network resilience reliability pressure driven analysis multi-objective genetic algorithm

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Estimation of absolute distance and high-frequency vibration from the modulated SM-OFI signal using compound mutated genetic algorithm

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OPTICS AND LASERS IN ENGINEERING 2020年 134卷 106271-106271页

作者： Bhardwaj, Vibhor Kumar Maini, Surita St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India Bhagwant Inst Technol Dept Elect & Commun Engn Muzaffarnagar 251315 Uttar Pradesh India

Contactless measurement of absolute distance or high-frequency vibration is a prominent factor for many industrial & scientific applications like predictive maintenance, non-destructive testing, and reverse engineering, etc. In recent years, self-mixing optical feedback interferometry (SM-OFI) has proven its superiority over other conventional optical methods because of its simple, compact and less expensive structure. Typically, the extraction of the sensing parameters from the modulated SM-OFI signal relies on the analysis of interferometric fringes that often leads to a non-optimal solution for weak feedback conditions. In this paper, the authors introduce a novel method to process the SM-OFI signal that employs a multi-objective compound mutated genetic algorithm to search for optimum solutions. To the author's knowledge, this is the first attempt to measure the vibrating frequency and the absolute distance between the laser-diode front facet and the vibrating target, simultaneously using a multi-objective global optimization method instead of frequency analysis. The validity of the proposed method was experimentally tested on a vibrating object located 2 to 20 cm away from the laser diode and having a vibration frequency between 2 and 10 kHz. The proposed method exhibits an excellent accuracy of 1% & 0.98% with an SNR of 42 dB & 19 dB for absolute distance & vibration-frequency measurement, respectively. The method also possesses a resolution of 0.6 mm during absolute distance measurement and a percentage of error ranging between 2.3 to 8% during high-frequency vibration measurement, both under the weak feedback conditions. The comparison with other methods also supports the potential of the proposed method for the applications where absolute distance and frequency of the vibrations are required to measure simultaneously.

关键词： Self-mixing effect Optical feedback multi-objective genetic algorithm Compound mutation Vibration frequency estimation Distance estimation

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Day surgery appointment scheduling with patient preferences and stochastic operation duration

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TECHNOLOGY AND HEALTH CARE 2021年第4期29卷 697-708页

作者： Wu, Qianyun Xie, Naiming Shao, Yuting Nanjing Univ Aeronaut & Astronaut Coll Econ & Management 29 Jiangjun Ave Nanjing 211106 Jiangsu Peoples R China

BACKGROUND: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. objective: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. METHODS: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. RESULTS: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. CONCLUSIONS: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

关键词： Patient preferences stochastic operation duration surgery scheduling stochastic programming multi-objective genetic algorithm

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A new approach to portfolio selection based on forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Corberan-Vallet, Ana Vercher, Enriqueta Segura, Jose, V Bermudez, Jose D. Univ Valencia Dept Stat & Operat Res Dr Moliner 50 Burjassot 46100 Spain Univ Miguel Hernandez Elche Ctr Operat Res Av Ferrocarril S-N Elche 03202 Spain

In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio's value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, which is solved using a multi-objective genetic algorithm. To show the performance of this procedure, we use a data set of asset prices from the New York Stock Market.

关键词： Portfolio optimization Finance Time series analysis Forecasting multi-objective genetic algorithm

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Fuzzy optimized conditioned-barrier nonlinear control of electric vehicle for grid to vehicle & vehicle to grid applications

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JOURNAL OF ENERGY STORAGE 2023年 64卷

作者： Zar, Abu Ahmad, Iftikhar Nazir, Muhammad Saqib Ahmed, Ijaz Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci H-12 Islamabad Pakistan

Plug-in electric vehicle (PEV) is the long-term eco-friendly solution to conventional fossil fuel based vehicles. In this study, a novel nonlinear barrier conditioned super-twisting sliding mode controller (BC ST-SMC) is proposed for the control of the PEV. The conventional super-twisting sliding mode control (ST-SMC) can only cater for the disturbances with known bounds and exhibits wind-up effects in the presence of saturated inputs which deteriorates the closed-loop performance of the system. The proposed BC-ST SMC solves these problems by providing anti-windup and un-bounded disturbance rejection. Lyapunov stability criterion is used to prove the asymptotic stability of the system. The PEV comprises of two subsystems namely: bidirectional charging unit (BCU) and hybrid energy storage system (HESS). The BCU comprises of a totem pole power converter between the PEV and grid which operates in two modes of grid to vehicle (G2V) and vehicle to grid (V2G). The components of the HESS are the battery and regenerative fuel-cell (RFC) (i.e. fuel-cell (FC) with an electrolyzer). The electrolyzer generates hydrogen which is required for the FC. Fuzzy-logic based energy management system (EMS) is adopted to maintain the power balance using state of charge (SoC) of the battery and electrolyzer as inputs. Furthermore, the controller gains are tuned using multi-objective genetic algorithm (MOGA) and multi-objective grey wolf optimizer (MOGWO) using integral absolute derivative of control input (IADU) with integral square of the error (ISE) as a fitness function. The simulations are performed on MATLAB (R) Simulink (2022b) which proves the superior performance of BC ST-SMC over conventional nonlinear controllers in the presence of saturated inputs. Finally, hardware-in-loop (HIL) based experimental results verify the real-time control of the proposed PEV.

关键词： Nonlinear barrier function Conditioned super-twisting multi-objective genetic algorithm multi-objective grey wolf optimizer Bidirectional charging unit G2V V2G Hybrid energy storage system Fuzzy-logic Hardware-in-loop

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Stress Analysis and Optimization Design of Bridge Crane Main Beam 23

Stress Analysis and Optimization Design of Bridge Crane Main...

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Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

作者： Xin Ning Tianji Shi Shaoru Shang Zhihua Zhu Mengyao Qin School of Mechanical and Electrical Engineering Henan Institute of Science and Technology China

ISBN: (纸本)9798400716362

To further improve the structural performance of the 32/5t-10.5m double girder bridge crane, the optimal size design of the main beam is sought. The main beam of the bridge crane is taken as the optimization object, and stress analysis is conducted on multiple load cases to identify critical points. The goal is to achieve the best structural quality of the bridge crane while ensuring its stiffness performance. Topology optimization design is performed using a multi-objective genetic algorithm and response surface analysis is employed to optimize the thickness of the left and right web plates of the main beam. By comparing the stress analysis results before and after the reconstruction of the main beam, it is found that the reconstructed main beam has a 19.8% reduction in weight while meeting the strength and stiffness requirements. This achieves the objective of simplifying the structure, reducing weight, and lowering manufacturing costs.

关键词： Bridge Crane Main Beam Finite Element Analysis multi-objective genetic algorithm Optimization Design

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multi-Physics multi-objective Optimal Design of 5-DOF Magnetic Bearing System with High-speed Permanent Magnet Synchronous Motor 24

Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnet...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Xiaoyuan Xu, Yuhao Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9788986510218

In view of the high-precision design requirements of suspension system under high-speed conditions, this paper used multi-objective genetic algorithm (MOGA) to carry out pareto optimization design of five-degree-of-freedom magnetic bearing system, and used finite element method to analyze the structure before and after optimization based on electromagnetic field, stress field and rotor dynamics theory respectively.

关键词： five-degree-of-freedom magnetic bearing system multi-objective genetic algorithm non-dominated sorting genetic algorithm-II

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Thermally regenerative electrochemical refrigerators decision-making process and multi-objective optimization

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ENERGY CONVERSION AND MANAGEMENT 2022年 252卷 115060-115060页

作者： Kamali, Hamed Mehrpooya, Mehdi Mousavi, Seyed Hamed Ganjali, Mohammad Reza Univ Tehran Caspian Fac Engn Coll Engn Tehran Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehran Iran Univ Tehran Coll Sci Ctr Excellence Electrochem Sch Chem Tehran Iran Univ Tehran Med Sci Biosensor Res Ctr Endocrinol & Metab Mol Cellular Sci Inst Tehran Iran

Thermally regenerative electrochemical refrigerators have drawn tremendous attention due to their reliability, quietness, eco-friendliness, non-emission of CFC gases, and potential for various applications. This work was conducted to model a thermally regenerative electrochemical refrigerator based on finite-time analysis. The proposed system is analyzed in four diverse temperature ranges, and all losses are considered for more accurate modeling by Python. Moreover, sensitivity analysis was performed for these temperature ranges. The thermodynamic analysis of these states has been demonstrated in detail for the first time. A multi-objective genetic algorithm in MATLAB software was used to achieve the maximum cooling capacity and COP and minimum input power. The optimal values, including system temperature, cell materials, and parameters related to heat exchangers and output results of the genetic algorithm, were prioritized using the weighted aggregated sum product assessment method. The results revealed that in the temperature ranges, 263K < TL < 283K, 297K < TH < 301K, which are the temperature ranges of cold and hot cells, respectively, the system indicated better performance. Meanwhile, selecting materials with higher specific charging/discharging capacity, isothermal coefficient, and smaller specific heat and internal resistance improves the system's performance. The optimum values of cooling capacity and system coefficient of performance were acquired as 367.01 W and 0.7301. This paper is expected to pave the way for the lab-scale design of thermally regenerative electrochemical refrigerators.

关键词： Coefficient of performance Cooling capacity Performance analysis multi-objective genetic algorithm multi-Criteria decision-making Thermally regenerative electrochemical refrigerator

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Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications by non-parametric regression-based metaheuristic technique

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2022年

作者： Ngoc Thoai Tran Minh Phung Dang Ngoc Le Chau Shankar, Subramaniam Buddhi, Dharam Thanh-Phong Dao Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Kongu Engn Coll Dept Mechatron Engn Erode India Uttaranchal Univ Div Res & Innovat Dehra Dun Uttarakhand India Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

With the advancement of bioengineering and robotic engineering, medical robots have been increasing concern about manipulating the microobject or cells. Although the rigid robots have a stable operation, they inherit many limitations such as the complex assembly process of joints-coupled rigid links and expensive costs. Especially, clearances between kinematic joints cause vibrations that damage microobject. To cope with such problems, a flexure-based polylactic acid (PLA) gripper is developed to realize precise motion in medical robots. The proposed gripper is 3D printed by fused deposition modeling technique with advantages of monolithic structure, jointless and cheap cost. Prior to the gripper fabrication, the optimization development of the gripper is conducted employing a combination of the non-parametric regression (NPR) and multi-objective genetic algorithm (MOGA). Numerical data samples are collected by the finite element method. The modeling results were well formulated utilizing the NPR method with the R-2 value greater than 0.9. The Pareto-optimum design results identified that the gripper can provide a high displacement of 2 mm and a small stress of 41 MPa via MOGA. Additionally, the proposed flexure-based compliant PLA gripper can work with a safety factor higher than 1.6. The experiment tests on the prototype of the gripper are close to the estimated values.

关键词： 3D printing additive manufacturing fused deposition modeling PLA gripper non-parametric regression multi-objective genetic algorithm

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