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A Symbiosis of Multi-Criteria Decision Making and Electroencephalography: A Review of Techniques, Applications, and Future Directions

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IEEE Access 2025年 13卷 75067-75084页

作者： Adem, Aylin Çakıt, Erman Dağdevíren, Metin Szopa, Anna Karwowski, Waldemar University of Central Florida Department of Industrial Engineering and Management Systems OrlandoFL32816 United States Gazi University Department of Industrial Engineering Ankara06570 Turkey

Multi-criteria decision-making (MCDM) problems are generally oriented toward strategic decisions and have a significant economic aspect, holding a special place in decision-making processes. To address these problems, specialized techniques have been developed and utilized. Electroencephalography (EEG) is a technique that measures brain activity, and the results can be used both to diagnose neurological disorders and to create more productive and healthier working conditions in a wide variety of areas. This paper discusses the collaborative nature between MCDM and EEG, focusing on how EEG and MCDM processes can enhance each other. Based on the database search results, thirty-five out of 149 papers were selected for the review process. By examining the literature on both macro and micro scales, the following were mainly retrieved: a) the MCDM techniques applied and their use in analyzing EEG data, b) EEG devices and wave types used in the MCDM process, c) the attributes of MCDM or EEG that were focused on, and d) current trends in knowledge and research opportunities. The results of this study will help identify potential future research areas, as well as provide a comprehensive overview of the existing literature. To summarize the findings, it can be concluded that EEG measurements of decision-makers’ cognitive states during the application of MCDM techniques improve the MCDM process and the presentation of these techniques, or they enhance the results obtained by MCDM based on the cognitive states of decision-makers when evaluating alternatives. Additionally, MCDM techniques contribute to improvements in the classification or feature extraction stages of data obtained through EEG. © 2013 IEEE.

关键词： Neurons

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Throughput and Quality Optimized Down-Selection of Overlay Measurement Markers for Robust Control of the Maximum Overlay Error in a Pattern Layer in Photolithography Processes

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IEEE Transactions on Semiconductor Manufacturing 2025年第2期38卷 251-262页

作者： Graff, Noah Djurdjanovic, Dragan The University of Texas at Austin Graduate Program in Dynamic Systems & Controls Walker Department of Mechanical Engineering AustinTX78712 United States The University of Texas at Austin Walker Department of Mechanical Engineering AustinTX78712 United States

This paper presents a metaheuristic optimization-based approach for selecting a pre-determined number of measurement markers from the set of available markers that optimizes the performance of the recently introduced robust L∞ norm overlay control algorithm, which robustly minimizes the worst overlay error across a given pattern layer. This optimization is then used in a Design of Experiments (DOE) setting to build a tractable regression model of a customizable objective function encompassing cost effects of quality losses and throughput benefits resulting from the down-selection of markers selected for robust overlay control. Using this model, one can rapidly determine the optimal proportion of markers for any set of cost parameters, and the optimal subset forming this proportion of available markers can be down-selected to maximize performance of the resulting robust overlay controller. Overlay data and models from a semiconductor manufacturing fab were used to evaluate the newly proposed inspection and control strategy. Results clearly indicate that the novel strategic down-selection of measurement markers coupled with robust overlay control could lead to vastly improved throughputs without decreasing quality relative to what can be achieved using traditional Run-to-Run (R2R) control. Feasibility of the novel DOE-based optimization was demonstrated for two scenarios of cost-effect parameters. © 1988-2012 IEEE.

关键词： Throughput

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Effect of Wet−dry Cycles and Water-to-cement Ratios on Cement Paste Carbonation

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Journal of Advanced Concrete Technology 2025年第4期23卷 205-222页

作者： Zhao, Zhiwei Oh, Dayoung Kitagaki, Ryoma Zheng, Tianlong Maruyama, Ippei Division of Human Environmental Systems Graduate School of Engineering Hokkaido University Hokkaido Sapporo060-8628 Japan Department of Architecture Graduate School of Engineering The University of Tokyo 113-8685 Japan Department of Environmental Engineering and Architecture Graduate School of Environmental Studies Nagoya University Nagoya464-8603 Japan

Cement production is associated with significant energy consumption and CO2 emissions, whereas concrete waste from demolition and renovation has the potential for CO2 absorption. This study investigated the effects of relative humidity and water-to-cement (w/c) ratio on the carbonation of hardened cement pastes with w/c ratios of 0.4, 0.5, and 0.6. Thermogravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy were used to analyze the carbonated samples, which were exposed to five different constant humidity conditions and underwent five types of wet–dry cycles. The results indicated that higher w/c ratios increased the hydration degree and pore formation, facilitating CO2 diffusion and promoting carbonation. Wet–dry cycles enhanced pore generation and calcium silicate hydrates (C-S-H) decomposition through shrinkage and deformation during the drying process. In addition, the minimum and maximum humidity of the wet–dry cycles influenced the formation of vaterite and amount of vaterite converted to calcite. The highest CO2 uptake after 14 days for WC06-40100 with a w/c ratio of 0.6 exhibited twice that of RH40 and 22% higher than that of RH80 on day 14. Moreover, the amount of CO2 uptake under RH40-100 for 28 days was approximately 17% of the annual CO2 emissions from cement production. © 2025 Japan Concrete Institute. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Optimizing CdS thin films as optical windows in solar cells: A comparative study of cryogenic and classical techniques

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Applied Physics A: Materials Science and Processing 2025年第6期131卷 1-17页

作者： Manir, Melih Genç, Gamze Nevruzoglu, Vagif Tomakin, Murat Department of Energy Systems Engineering Erciyes University Kayseri38039 Turkey Energy Systems Engineering Program Graduate School of Natural and Applied Sciences Erciyes University Kayseri38039 Turkey Department of Energy Systems Engineering Recep Tayyip Erdogan University Rize53100 Turkey Research Group Erciyes University Kayseri38039 Turkey Department of Physics Recep Tayyip Erdogan University Rize53100 Turkey

In this study, CdS thin films were produced in a quasi-closed volume using two different techniques (classical and cryogenic thermal evaporation techniques) between the 100–573 K substrate temperature, and their characteristic properties (structural, electrical, and optical properties) were investigated. While CdS thin films were produced at 373 K, 473 K, and 573 K substrate temperatures in the classical technique (hot), they were produced at 100–300 K substrate temperature range with 50 K steps in the cryogenic technique (cold). The X-Ray Diffraction (XRD) analysis showed that the CdS thin films grew in a hexagonal structure in the (002) plane at all substrate temperatures. According to the field emission scanning electron microscope (FESEM) images, the thin films produced at 200 K substrate temperature consisted of equally sized spherical grains. This situation shows that the soliton growth mechanism occurs at a substrate temperature of 200 K during the film production process with the cryogenic technique. Due to the characteristic properties of the soliton waves occurring on the substrate surface in the soliton growth mechanism (mass transport), the films grow in a tight-packed form. Therefore, the produced films consist of clusters of equal size, providing a homogeneous surface and a uniform thickness. In addition, Atomic Force Microscope (AFM) and optical analyses showed that the CdS thin film produced at 200 K substrate temperature had the smallest average surface roughness value (Ra) and the highest optical transmittance value. It was found that the energy band gap (2.37–2.47 eV) and resistivity (1.25 × 103–5.39 × 103 Ω-cm) values of CdS thin films increased with decreasing substrate temperature. The carrier density increased with decreasing substrate temperature (3.91 × 1017–1.73 × 1016 cm−3). Energy Dispersive Spectroscopy (EDS) analysis showed that the films grew stoichiometrically at substrate temperatures of 473 K and 200 K. The results brought out tha

关键词： Density (optical)

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Optimization of Stiffness and Damping Coefficient Using Genetic Algorithm in an Electric Bus with Air Suspension System 6th

Optimization of Stiffness and Damping Coefficient Using Gene...

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6th International Conference on Mechanical engineering, ICOME 2023

作者： Hendrowati, Wiwiek Sutantra, INyoman Yuniarto, M. Nur Solichin, Moch. Nurlita, Cony Mechanical Engineering Department Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl AriefRahmanHakim Surabaya60111 Indonesia Graduate of the Department of Mechanical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9789819778973

Riding comfort is the minimum vibration transferred by the road surface to a passenger, where it is characterized by maximum sprung mass acceleration. International standards determine riding comfort, specifically ISO 2631. This research utilizes a genetic algorithm to obtain optimal stiffness and damping coefficients for the vehicle suspension system. The system modeling is simulated using MATLAB software. Based on the genetic algorithm optimization performed, the optimal stiffness and damping coefficients obtained for the system are 203,800 N/s and 12,771 Ns/m for the front suspension stiffness and damping coefficient, respectively. The rear suspension stiffness and damping coefficient are 310,750 N/s and 10,702 Ns/m, respectively. With the optimized parameters, the dynamic responses of acceleration and displacement in vertical and pitch motions yield more comfortable results. The conclusions are drawn based on the root mean square (RMS) acceleration value for drivers who meet ISO 2631 comfort standards. The driver's system operates comfortably without complaints at low speeds, with only minor complaints at high rates. With the optimized suspension, the driver can sustain over 16 h of operation, whether subjected to sinusoidal road. The optimized parameters result in an improved transient vehicle response, characterized by reduced impact forces and quicker attainment of settling time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Damping

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EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

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IEEE Access 2025年 13卷 70849-70871页

作者： Akram, Muhammad Ullah Bazai, Sibghat Imran Ghafoor, Muhammad Akram, Saira Ilyas, Qazi Mudassar Mehmood, Abid Iqbal, Sajid Asim Rafique, Muhammad Department of Software Engineering Quetta87300 Pakistan Department of Computer Engineering Quetta87300 Pakistan Pakistan Television Corporation Department of Engineering Lahore44000 Pakistan King Faisal University College of Computer Sciences and Information Technology Department of Information Systems Al Hofuf31982 Saudi Arabia King Faisal University College of Business Administration Department of Management Information Systems Al Hofuf31982 Saudi Arabia

Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for dat

关键词： Sensor nodes

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Container Pre-marshalling Problem Minimizing CV@R under Uncertainty of Ship Arrival Times

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Journal of Japan Industrial management Association 2025年第4 E期75卷 172-182页

作者： Ikuma, Daiki Ikeda, Shunnosuke Sukegawa, Noriyoshi Takano, Yuichi Graduate School of Science and Technology University of Tsukuba Japan Department of Advanced Sciences Hosei University Japan Institute of Systems and Information Engineering University of Tsukuba Japan

This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships without reshuffles. In reality, however, ship arrival times are affected by various external factors, which can cause the order of container retrieval to be different from the initial plan. To represent such uncertainty, we generate multiple scenarios from a multivariate probability distribution of ship arrival times. We derive a mixed-integer linear optimization model to find an optimal container layout such that the conditional value-at-risk is minimized for the number of misplaced containers responsible for reshuffles. Moreover, we devise an exact algorithm based on the cutting-plane method to handle large-scale problems. Numerical experiments using synthetic datasets demonstrate that our method can produce high-quality container layouts compared with the conventional robust optimization model. Additionally, our algorithm can speed up computations for solving large-scale problems. © 2025 Japan Industrial management Association. All rights reserved.

关键词： Mixed-integer linear programming

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Understanding preferences for autonomous trucks functions in China: Insights from drivers and organizational buyers

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Transport Policy 2025年 170卷 110-119页

作者： Fu, Hanlong Fu, Xiaowen Ye, Shi Wang, Kun Chen, Tiantian Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Behaviour and Knowledge Engineering Research Centre Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology South Korea Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

The transportation industry is undergoing a significant transformation as it integrates advanced driver assistance systems (ADAS) technologies. This shift is particularly important in the trucking industry. Driver assistance technologies offer a promising solution for improving safety and reducing traffic accidents. However, the trucking industry lags significantly behind passenger vehicles in the maturity and penetration rate of such technologies. This study uses a stated preference survey to explore the purchasing preferences for ADAS functions among truck drivers and organization buyers in China. Our findings show that truck drivers with a safe driving history prioritize reliability and assistance features such as automatic emergency braking, adaptive cruise control, and lane-centering control. In contrast, drivers with a record of unsafe driving favor more advanced ADAS functions, such as city or highway navigation on autopilot, owing to their ability to alleviate driving stress. Buyers from organizations, compared with individual truck drivers, are more averse to the additional costs of ADAS technologies, while larger companies seem more willing to invest in autonomous trucks than are smaller businesses and individuals. However, top management teams remain cautious, reflecting a lack of confidence in the operational and safety benefits of the current technology at Level 2 autonomy. Resistance to adopting autonomous trucks is also stronger among male (vs. female) drivers and older drivers, who comprise a large segment of the domestic market. The study recommends that autonomous vehicle system providers and governments prioritize active safety functions to further improve safety. Furthermore, it is suggested that extensive training and trials be provided to increase trust and confidence in autonomous truck technologies among industry stakeholders. © 2025 Elsevier Ltd

关键词： ADAS Autonomous truck Chinese market Function preference Stated preference experiment

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Technical Note—Conic Mixed-Binary Sets: Convex Hull Characterizations and Applications

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Operations Research 2025年第1期73卷 251-269页

作者： Kılınç-Karzan, Fatma Küçükyavuz, Simge Lee, Dabeen Shafieezadeh-Abadeh, Soroosh Tepper School of Business Carnegie Mellon University PittsburghPA15213 United States Department of Industrial Engineering and Management Sciences Northwestern University EvanstonIL60208 United States Department of Industrial and Systems Engineering KAIST Daejeon34141 Korea Republic of

We consider a general conic mixed-binary set where each homogeneous conic constraint j involves an affine function of independent continuous variables and an epigraph variable associated with a nonnegative function, fj, of common binary variables. Sets of this form naturally arise as substructures in a number of applications, including mean-risk optimization, chance-constrained problems, portfolio optimization, lot sizing and scheduling, fractional programming, variants of the best subset selection problem, a class of sparse semidefinite programs, and distributionally robust chance-constrained programs. We give a convex hull description of this set that relies on simultaneous characterization of the epigraphs of fj’s, which is easy to do when all functions fj’s are submodular. Our result unifies and generalizes an existing result in two important directions. First, it considers multiple general convex cone constraints instead of a single second-order cone type constraint. Second, it takes arbitrary nonnegative functions instead of a specific submodular function obtained from the square root of an affine function. We close by demonstrating the applicability of our results in the context of a number of problem classes. © 2023 INFORMS.

关键词： Constrained optimization

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A digital twin calibration for an automated material handling system in a semiconductor fab

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Journal of Manufacturing systems 2025年 80卷 1013-1028页

作者： Kang, Bonggwon Park, Chiwoo Kim, Haejoong Hong, Soondo Department of Industrial Engineering Pusan National University Busan46241 Korea Republic of Department of Industrial and Systems Engineering University of Washington SeattleWA98195 United States Department of Industrial and Management Engineering Kyonggi University Gyeonggi 16227 Korea Republic of

To address the complex, dynamic, and stochastic nature of an automated material handling system (AMHS) in a semiconductor fabrication facility (fab), practitioners have used a high-fidelity discrete-event simulation as its digital twin model for decision-making over several decades. Previous studies have focused on fast digital twin-based decision-making in AMHSs under the assumption that their digital twin models are credible enough to prescribe decisions. However, parameter uncertainty and intrinsic bias in an AMHS digital twin model can lead to an inaccurate representation of its field system. To address the challenge, this paper introduces the Bayesian calibration, which modularly estimates a digital twin outcome and its discrepancy using Gaussian process priors. A calibration framework for digital twin-based decision-making is also presented using an AMHS example. Our experimental results with various AMHS operating scenarios demonstrate that: (1) a sophisticated digital twin calibration is necessary, especially when AMHSs operate under heavy-workload scenarios;and (2) exploring model bias considerably decreases the prediction error of an AMHS digital twin within a limited number of field observations. Moreover, we discuss the applicability of the approach to digital twins in various fields. © 2025 The Society of Manufacturing Engineers

关键词： Gaussian distribution

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