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OR based multi-objective decision-making in e-commerce improvement

SERVICE ORIENTED COMPUTING AND APPLICATIONS 2025年 1-11页

作者： Gollavilli, Venkata Surya Bhavana Harish Nagarajan, Harikumar Alagarsundaram, Poovendran Gattupalli, Kalyan Sitaraman, Surendar Rama Khalid, Haris M. Under Armour Baltimore MD 21202 USA Global Data Mart Inc GDM South Plainfield NJ USA Humetis Technol Inc Kingston NJ USA Yash Tek Inc Mississauga ON Canada Intel Corp Santa Clara CA USA Univ Dubai Coll Engn & Informat Technol Dubai 14143 U Arab Emirates

The inefficiencies and high costs of traditional business process redesign techniques, which frequently rely on manual optimization and lack systematic approaches, are discussed in this study. Our suggestion to address these issues is a computational fuzzy model that combines multi-objective decision-making methods with operational research. This model analyses corporate goals, resources, and limitations in an organized manner to improve the efficacy of business redesign. Our approach makes finding the best redesign tactics easier while considering various goals and trade-offs. Test results show that the suggested methodology delivers an astounding 98.62% average accuracy in redesign results. Its efficiency is further demonstrated by the fact that the average time needed for the redesign process is reduced to 11.55 s, and related expenditures are kept to a minimum. The results show that using the computational fuzzy model makes decision-making more accessible and generally raises the standard of business operations. This model offers a valuable tool for businesses looking to improve operational effectiveness and save expenses in fast-paced e-commerce by providing an organized approach to company redesign. The outcomes show how sophisticated decision-making frameworks may be integrated into business process management to enable more efficient and flexible organizational procedures.

关键词： Decision-making optimization algorithm e-commerce Business process Multi-objectives

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A model for speed and fuel refueling strategy of methanol dual-fuel liners with emission control areas

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TRANSPORT POLICY 2025年 161卷 1-16页

作者： Gao, Tianhang Tian, Jia Liu, Changjian Huang, Chuan Wu, Hongyu Yuan, Ziwen Minist Transport Div Waterway Planning Transport Planning & Res Inst Beijing 100028 Peoples R China

Methanol dual-fuel (DF) liners can simultaneously use traditional fuel oil and methanol as blended fuels, which can address environmental protection requirements while ensuring economy. This paper presents an optimization model for the speed and refueling strategy of methanol DF liners. A decision-making tool is introduced for shipping companies to develop liner operation plans. A mixed-integer 0-1 planning model is constructed with the objective of minimizing the operating cost of a single voyage of a methanol DF liner, and the optimal solutions for the voyage speed in each segment and the fuel oil and methanol refueling strategy at each port are computed considering the dual-fuel mixing of fuel oil and methanol. In this paper, the validity of the model and algorithm are verified with the AEU3 route of COSCO Shipping as an example, and the results show that installing a scrubber on liners and mixing heavy fuel oil (HFO) and methanol is more economical than mixing very low-sulfur fuel oil (VLSFO) and methanol, considering the existing emission standards. The studied liner should increase its speed in emission control areas (ECAs) and refuel at ports with low prices for fuel oil and methanol. Although the above conclusions are not influenced by changes in the price of methanol, the price difference between HFO and VLSFO, or the carbon allowance price, changes in sulfur emission standards will have a significant effect on the speed and refueling strategy of liners and carbon emissions. This paper provides a theoretical reference for operational decision-making for shipping companies operating methanol DF liners and is of practical value for improving the scientific management of methanol DF liners, enhancing the energy efficiency of ships, and reducing the emission of pollutants from ships.

关键词： Methanol dual-fuel liners Speed and refueling strategy optimization algorithm Emission control areas

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Cost-based model for optimal waste-load allocation and pollution loading losses in river system: simulation-optimization approach

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2022年第12期19卷 12103-12118页

作者： Fakouri, B. Samani, J. Mohamad Vali Samani, H. Mohamad Vali Mazaheri, M. Tarbiat Modares Univ Dept Water Engn & Management Tehran Iran Shahid Chamran Univ Dept Civil Engn Ahvaz Iran

The development of agricultural, industrial, and urban activities creates an increase in pollution loading in rivers that may violate water quality standards which in its turn results in damages to the river systems. An efficient model of waste-load allocation plays a significant role in improving the water quality of the rivers. In this paper, a cost-based waste-load allocation model (C-WLA) is applied to guarantee the optimal management of both costs and river water quality. To determine the optimal loading pattern and threshold limits, the trade-off between treatment cost and pollution loss from the river is considered. In this regard, the MIKE11 model is coupled with the Particle Swarm optimization (PSO) algorithm and applied to the Karoon River in Iran to demonstrate its practicality and efficiency. The sum of water treatment cost and pollution loading loss is minimized and the monthly optimal treatment percentages and threshold limit were determined. Then, different strategies under various operations of the river system are given with insights into the impacts of the trade-off policy between costs and losses for the discharger's TDS removal. The results demonstrated that the C-WLA model can achieve optimal management of pollution load in various operating of the river system. In addition, it is also shown that the proposed method is expected to offer better decision support to reasonable waste-load allocation where it can encourage dischargers to improve loading performance. [GRAPHICS] .

关键词： River quality Pollution damage Optimal load MIKE11 model optimization algorithm Karoon river

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Differential evolution algorithm with a complementary mutation strategy and data Fusion-Based parameter adaptation

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INFORMATION SCIENCES 2024年 668卷

作者： Chen, Bozhen Ouyang, Haibin Li, Steven Zou, Dexuan Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Grad Sch Business & Law Melbourne 3000 Australia Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Peoples R China

As an excellent optimization algorithm widely used to solve various practical problems, differential evolution (DE) algorithm has few parameters, yet its performance is significantly affected by these parameters. To address this issue, this paper presents a novel variant of DE called DACDE, which utilizes data fusion-based parameter adaptation and a complementary mutation strategy. Most parameter adaptation methods based on successful history only analyze the mean of parameters and ignore their degree of dispersion. In DACDE, the successful parameter distribution is recorded and described by both the mean and variance. Data fusion is then used to combine records and generate an estimated distribution, which is applied to a Gaussian distribution to generate new parameters. Inspired by opposition-based learning, we introduce a complementary mutation strategy. This strategy employs a symmetric selection mechanism to adapt to the varying search abilities required by the algorithm at different stages. The new variant is verified on 32 single-objective functions from CEC 2011 and 2014 benchmark suites, and the results show that DACDE is competitive compared to other 29 evolutionary algorithms.

关键词： Differential evolution optimization algorithm Data fusion Novel mutation strategy Gaussian distribution

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Dependency-Aware Dynamic Task Offloading Based on Deep Reinforcement Learning in Mobile-Edge Computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第2期21卷 1403-1415页

作者： Fang, Juan Qu, Dezheng Chen, Huijie Liu, Yaqi Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

The rapid advancement of mobile edge computing (MEC) networks has enabled the augmentation of the computational power of mobile devices (MDs) by offloading computationally intensive tasks to resource-rich edge nodes. This paper discusses the decision-making process for task offloading and resource allocation among multiple mobile devices connected to a base station. The primary objective is to minimize the time taken to complete tasks while simultaneously reducing energy consumption on the device under a time-varying wireless fading channel. This objective is formulated as an energy-efficiency cost (EEC) minimization problem, which cannot be solved by conventional methods. To address this challenge, we propose a dynamic offloading decision algorithm of dependent tasks (DODA-DT) that adjusts local task execution based on edge node status. The proposed algorithm facilitates fair competition among all devices for edge resources. Additionally, we use a deep reinforcement learning (DRL) algorithm based on an actor-critic learning structure to train the system to quickly identify near-optimal solutions. Numerical simulations demonstrate that the proposed algorithm effectively reduces the total cost of the task in comparison to previous algorithms.

关键词： Task analysis Servers Mobile handsets Heuristic algorithms Reinforcement learning Costs Deep learning Mobile edge computing task offloading optimization algorithm deep reinforcement learning

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Optimized multi-variable coupling can improve synchronization in complex networks

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NONLINEAR DYNAMICS 2024年第20期112卷 18491-18500页

作者： Ansarinasab, Sheida Parastesh, Fatemeh Ghassemi, Farnaz Rajagopal, Karthikeyan Jafari, Sajad Kurths, Juergen Amirkabir Univ Technol Tehran Polytech Dept Biomed Engn Tehran Iran Chennai Inst Technol Ctr Nonlinear Syst Chennai India Amirkabir Univ Technol Tehran Polytech Hlth Technol Res Inst Tehran Iran Potsdam Inst Climate Impact Res Telegrafenbery 31 D-14473 Potsdam Germany Humboldt Univ Dept Phys D-12489 Berlin Germany

Investigating synchronization in networks of oscillators elucidates the intricate interplay between individual dynamics and emergent collective behavior. This study introduces an optimization algorithm to achieve multi-variable coupling for enhancing synchronization in networks of chaotic systems with diffusive couplings. Our results demonstrate that the optimized multi-variable coupling surpasses single-variable and diagonal couplings in achieving synchronization under equivalent total coupling strength. Employing the optimization algorithm, based on the master stability function (MSF), across networks of chaotic R & ouml;ssler, Hindmarsh-Rose, Lorenz, and Chen reveals that the optimized multi-variable coupling requires lower coupling strength for synchronization than other coupling schemes. Furthermore, the resulting MSF with optimized multi-variable coupling results in more negative values, indicating a higher degree of stability in synchronization.

关键词： Synchronization Complex network Multi-variable coupling optimization algorithm

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Research on Rotating Machinery Fault Diagnosis Based on Multi-Strategy Feature Extraction

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TRIBOLOGY TRANSACTIONS 2024年第6期67卷 1117-1131页

作者： Song, Yadi Wang, Haibo Zhao, Chuanzhe Wang, Ronglin Li, Pengtao Li, Zhifeng Jilin Inst Chem Technol Coll Mech & Elect Engn Jilin Peoples R China Jilin Inst Chem Technol Coll Informat & Control Engn Jilin Peoples R China

This study proposes a novel feature extraction method that leverages multi-strategy optimization algorithms to enhance the accuracy and efficiency of rotating machinery fault diagnosis. By introducing an improved slime mold algorithm (LTSSMA), it optimizes the penalty factor and decomposition layers in variational mode decomposition (VMD), achieving more precise signal decomposition. The sample entropy generated by VMD forms the basis of the feature vector, and the nonlinear dimensionality reduction algorithm (t-SNE) is applied to reduce dimensions. Finally, the optimized features are classified using a random forest (RF) model, resulting in an 11.3% improvement in fault diagnosis accuracy compared to traditional methods. This method not only accelerates the diagnostic process but also significantly improves fault identification reliability.

关键词： optimization algorithm variational mode decomposition t-distributed stochastic neighbor embedding random forest

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Multi-step wind speed forecasting model using a compound forecasting architecture and an improved QPSO-based synchronous optimization

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ENERGY REPORTS 2022年 8卷 9899-9918页

作者： Sun, Sizhou Wang, Yu Meng, Ying Wang, Chenxi Zhu, Xuehua Anhui Polytech Univ Energy Internet Engn Res Ctr Anhui Prov Dept Educ Wuhu 241000 Peoples R China Anhui Polytech Univ Anhui Key Lab Elect Drive & Control Wuhu 241000 Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

The intermittency and randomness of wind speed time series influence the forecasting accuracy. To figure out this problem and enhance the forecasting performance, a novel compound structure is developed for short-term wind speed forecasting. The developed compound method firstly eliminates the inherent noise from the original empirical wind speed time series using wavelet packet decom-position (WPD) and subsequently constructs appropriate input matrix by phase space reconstruction (PSR) for multi-kernel least square support vector machine (MKLSSVM). To take advantage of different kernel function, MKLSSVM with optimal combination of radial basis kernel function, polynomial kernel function and linear kernel function is constructed to make wind speed forecasting. Then, the proposed improved quantum particle swarm optimization algorithm (QPSO) based on the chaos initialization, Gaussian distribution local attraction points, precocity judgment, and disturbance operator, namely ADQPSO, is employed to optimize the decomposition level of WPD, reconstruction parameters of PSR, kernel parameters and weighted coefficients in MKLSSVM synchronously. To evaluate the forecasting performance of the proposed hybrid model, four sets of historical wind speed data samples from Weihai wind farm in China are utilized to make multi-step short-term wind speed forecasting tests. The experimental results illustrate that the proposed hybrid model outperforms the compared single and new recently developed forecasting models, thus, the proposed WPD-PSR-ADQPSO-MKLSSVM is an effective method for short-term wind speed forecasting.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Wind speed forecasting Phase space reconstruction Least square support vector machine Improved quantum particle swarm optimization algorithm Wavelet packet decomposition

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Interconnected Microgrids Load-Frequency Control Using Stage-by-Stage Optimized TIDA+1 Error Signal Regulator

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第1期7卷

作者： Shayeghi, Hossein Rahnama, Alireza Bizon, Nicu Szumny, Antoni Univ Mohaghegh Ardabili Energy Management Res Ctr Ardebil Iran Natl Univ Sci & Technol Politehn Bucharest Pitesti Univ Ctr Pitesti Romania Wroclaw Univ Environm & Life Sci Dept Food Chem & Biocatalysis Wroclaw Poland

Load-frequency control (LFC) is essential for maintaining system stability and ensuring high power quality in microgrids (MGs), particularly those heavily reliant on renewable energy sources (RES) and operating independently of the main grid. This paper introduces a novel control strategy aimed at improving LFC performance in interconnected MGs by correcting the error signal. The proposed controller, denoted as TIDA+1, combines tilt, integrator, derivative, and acceleration operators in a parallel configuration to refine the incoming error signal. The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time-varying acceleration coefficients (NTVAC). The controller's effectiveness is validated through four distinct scenarios, including sudden load variations, system modeling uncertainties, fluctuations in RES outputs, and the impact of nonlinearities. Additionally, a lab-scale evaluation of the controller has been conducted to further assess its practical applicability. Comparative results demonstrate that the TIDA+1 controller outperforms traditional controllers such as PID and FOPID, especially under complex operational conditions. The study highlights the TIDA+1 controller as a robust and viable solution for LFC in MGs, with potential for future scalability and application in larger systems.

关键词： interconnected microgrids load-frequency control optimization algorithm TIDA+1 controller

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Measurement of four main catechins content in green tea based on visible and near-infrared spectroscopy using optimized machine learning algorithm

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 138卷

作者： Luo, Wei Li, Wenyoujia Liu, Shuling Li, Qicheng Huang, Haihua Zhang, Hailiang East China Jiaotong Univ Coll Elect & Automat Engn Nanchang 330013 Peoples R China Jiangxi Inst Sci & Technol Informat Nanchang 330046 Peoples R China

The catechin content is critical for assessing the nutritional value and commercial worth of green tea, making detection methods valuable for its quality evaluation. This study utilized visible and near-infrared spectroscopy (Vis-NIRS, 400-2500 nm) combined with an optimized machine learning model, integrating the global search whale optimization algorithm (GSWOA) and kernel extreme learning machine (KELM), to predict the content of four main catechins (EC, ECG, EGC, and EGCG) in fresh tea leaves. Multiple preprocessing and effective wavelength selection methods were applied to improve prediction accuracy. The results showed that the distribution of effective wavelengths verified the importance of the visible spectrum. The GSWOA-KELM demonstrated superior performance, optimizing the modeling capabilities of KELM and outperforming other models, including partial least squares regression (PLSR) and random forest (RF), with R2P values exceeding 0.98 and RMSEP values below 3.3 for all catechins. Notably, even without additional data processing, the model maintained good effects, achieving a minimum R2p of 0.9751 and a maximum RMSEP of 3.3654. Therefore, the proposed approach proved to be both accurate and rapid for non-destructive, online catechin analysis and had practical value for tea appreciation.

关键词： Visible and near-infrared spectroscopy Green tea Catechin content Machine learning optimization algorithm

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