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arXiv 2024年

作者： Wang, Yijie Hanasusanto, Grani A. Ho, Chin Pang Graduate Program in Operations Research and Industrial Engineering The University of Texas at Austin AustinTX78712-1591 United States Department of Industrial and Enterprise Systems Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States School of Data Science City University of Hong Kong Hong Kong

In this paper, we consider contextual stochastic optimization using Nadaraya-Watson kernel regression, which is one of the most common approaches in nonparametric regression. Recent studies have explored the asymptotic convergence behavior of using Nadaraya-Watson kernel regression in contextual stochastic optimization;however, the performance guarantee under finite samples remains an open question. This paper derives a finite-sample generalization bound of the Nadaraya-Watson estimator with a spherical kernel under a generic loss function. Based on the generalization bound, we further establish a suboptimality bound for the solution of the Nadaraya-Watson approximation problem relative to the optimal solution. Finally, we derive the optimal kernel bandwidth and provide a sample complexity analysis of the Nadaraya-Watson approximation problem. © 2024, CC BY.

关键词： Optimization

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Analysis of Non-fatal Occupational Accidents in a Ready-mixed Concrete Company

Analysis of Non-fatal Occupational Accidents in a Ready-mixe...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Sario, Maria Cristina Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Sittiwatethanasiri, Thaninrat Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

The production and delivery of ready-mixed concrete consist of several activities which expose employees to considerable safety hazards that lead to occupational accidents. The study aimed to determine the significant factors contributing to non-fatal occupational accidents in the ready-mixed concrete industry in Qatar. A total of 84 non-fatal occupational accidents were collected from a ready-mixed concrete company in Qatar. A coding scheme was developed based on available literature and the company's existing classification to analyze each accident in terms of employee, injury, and accident characteristics. Cross-tabulation analysis was used to determine the associations between categorical factors. Frequency distribution showed that regular employees aged between 25 and 44 years old were mostly involved in accidents, with hand/arm and leg/foot being mostly injured. The most common accident types were struck by an object (44%), slip, trip or fall on the same level (23.8%), and caught in between objects (17.9%). The majority of work-related accidents (71%) could be attributed to unsafe behavior, particularly due to improper use of PPE. Strong significant associations were observed between activity type and accident root cause, as well as injured body type and accident root cause. The significant factors contributing to non-fatal occupational accidents were determined through the study. The insights obtained from this study could be used by the company in designing a comprehensive safety management program with the goal of driving occupational injuries to a lower level. © 2022 IEEE.

关键词： Accidents

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The Impact of COVID-19 Pandemic on Airport: An Empirical Study of Service Quality, Customer Satisfaction, and Travel Intention for Sustainable Airport Operations

The Impact of COVID-19 Pandemic on Airport: An Empirical Stu...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Fuente, Darlene Gayle D. Dela Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Entrepreneurship Department Binus Business School Undergraduate Program Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Delivering a high service quality under the safety protocol of COVID-19 is very essential for the sustainable airport operations. The study was intended to determine the impact of COVID-19 on Airport Service Quality (ASQ), customer satisfaction, and travel intention by utilizing a structural equation modeling (SEM) approach. A total of 517 Filipinos voluntarily answered an online questionnaire that consists of 92 questions. SEM indicated that the security check, terminal facilities, and services had significant effects on perceived value which subsequently led to customer satisfaction. In addition, travel safety measures had direct effects on Filipinos' travel intention and customer satisfaction. Interestingly, service innovations had no significant impact on customer satisfaction but directly affected travel intention. By understanding the relationship between these factors, airport management could have better decision-making while efficiently and effectively utilizing the resources in these times of uncertainty. © 2022 IEEE.

关键词： COVID-19

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The Impact of the COVID-19 Pandemic on the Organizational Commitment in Semiconductor Industry: The Mediator Effect of the Job Satisfaction

The Impact of the COVID-19 Pandemic on the Organizational Co...

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2022 IEEE International Conference on industrial engineering and engineering Management, IEEM 2022

作者： Prasetyo, Yogi Tri Ong, Ardvin Kester S. Cagubcob, Alaissa Marie A. Chuenyindee, Thanatorn Nadlifatin, Reny Persada, Satria Fadil Mapúa University School of Industrial Engineering and Engineering Management Manila Philippines Yuan Ze University Department of Industrial Engineering and Management Chung-Li Taiwan Mapúa University School of Graduate Studies Manila Philippines Navaminda Kasatriyadhiraj Royal Air Force Academy Department of Industrial Engineering and Aviation Management Bangkok Thailand Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Due to the COVID-19 pandemic, employees are required to respond to changes and conform to stringent safety laws and regulations. This study aimed to analyze the impact of the COVID-19 pandemic on the organizational commitment among employees in the semiconductor industry. A total of 272 employees working in the semiconductor industry answered a self-administered questionnaire that considered 51 questions, distributed online. Utilizing Structural equation modeling (SEM), the results showed that the COVID-19 was found to have a significant direct effect on employees' perceived job outcomes (PO) and a negative direct effect on job demands (JD). Moreover, PO had a positive effect on job motivation (JM) and job satisfaction (JS). Subsequently, JM presented a significant positive direct effect on JS, while JD showed a negative effect on JS. Lastly, JS showed a significant positive direct effect on organizational commitment (OC). Intriguingly, an indirect effect of PO on JS was seen. This study is one of the first studies that analyzed the organization commitment among semiconductor workers during the COVID-19 pandemic. This paper could be utilized as a foundation to enhance organizational commitment, particularly in the semiconductor industry worldwide. © 2022 IEEE.

关键词： COVID-19

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A TWO-STAGE FEDERATED LEARNING APPROACH FOR industrial PROGNOSTICS USING LARGE-SCALE HIGH-DIMENSIONAL SIGNALS

arXiv

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arXiv 2024年

作者： Su, Yuqi Fang, Xiaolei Operations Research Graduate Program North Carolina State University RaleighNC United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University RaleighNC United States

industrial prognostics aims to develop data-driven methods that leverage high-dimensional degradation signals from assets to predict their failure times. The success of these models largely depends on the availability of substantial historical data for training. However, in practice, individual organizations often lack sufficient data to independently train reliable prognostic models, and privacy concerns prevent data sharing between organizations for collaborative model training. To overcome these challenges, this article proposes a statistical learning-based federated model that enables multiple organizations to jointly train a prognostic model while keeping their data local and secure. The proposed approach involves two key stages: federated dimension reduction and federated (log)-location-scale regression. In the first stage, we develop a federated randomized singular value decomposition algorithm for multivariate functional principal component analysis, which efficiently reduces the dimensionality of degradation signals while maintaining data privacy. The second stage proposes a federated parameter estimation algorithm for (log)-location-scale regression, allowing organizations to collaboratively estimate failure time distributions without sharing raw data. The proposed approach addresses the limitations of existing federated prognostic methods by using statistical learning techniques that perform well with smaller datasets and provide comprehensive failure time distributions. The effectiveness and practicality of the proposed model are validated using simulated data and a dataset from the NASA repository. © 2024, CC BY.

关键词： Principal component analysis

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Artificial Intelligence-Based Recommendation System for Detecting and Diagnosing Broken Bars in Induction Motors Under Transient Operation

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IFAC-PapersOnLine 2024年第19期58卷 1156-1161页

作者： Narco A.R. Maciejewski Roberto Z. Freire Anderson L. Szejka Thiago P.M. Bazzo Sofia M.A. Lopes Rogério A. Flauzino Department of Electrical Engineering - DAELT-CT Universidade Tecnológica Federal do Paraná - UTFPR Curitiba PR 80230-90 Brazil Production and Systems Engineering - PPGEPS-PB Energy Systems - PPGSE-CT Industrial and Systems Engineering Graduate Program - PPGEPS Pontifical Catholic University of Parana - PUCPR Curitiba PR 80215-901 Brazil Electrical Engineering - PPGEE-SC São Carlos School of Engineering - EESC Universidade de São Paulo - USP São Carlos SP 13566-590 Brazil

Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

关键词： condition monitoring fault detection fault classification machine learning maintenance engineering smart systems three-phase induction motor

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US Army Aviation air movement operations assignment, utilization and routing

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Journal of Defense Analytics and Logistics 2023年第1期7卷 2-28页

作者： Nelson, Russell King, Russell McConnell, Brandon M. Thoney-Barletta, Kristin Operations Research Graduate Program North Carolina State University Raleigh NC United States Department of Industrial and Systems Engineering North Carolina State University Raleigh NC United States Center for Additive Manufacturing and Logistics North Carolina State University Raleigh NC United States Department of Textile and Apparel Technology and Management North Carolina State University at Raleigh Raleigh NC United States

Purpose: The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost. Design/methodology/approach: In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period. Findings: The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time. Research limitations/implications: Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight. Originality/value: This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits. © 2022, In accordance with section 105 of the US Copyright Act, this work has been produced by a US government employee and shall be considered a public domain work, as copyright protection is not available.

关键词： Aircraft Demand priority Dial-a-ride problem Helicopter routing Heuristic Military aviation Multiple refuel nodes Vehicle utilization

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Multi-objective Machine Learning for control performance assessment in PID control loops ⁎

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IFAC-PapersOnLine 2024年第7期58卷 168-173页

作者： Gilberto Reynoso-Meza Jesús Carrillo-Ahumada Tainara Marques Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifícia Universidade Católica do Paraná (PUCPR) Rua Imaculada Conceição 1155 Curitiba Brazil Control Systems Optimization Laboratory (LOSC) Pontifícia Universidade Católica do Paraná (PUCPR) Ingeniería en Alimentos. Universidad del Papaloapan. Circuito Central 200 Col. Parque Industrial 68301 Tuxtepec Oaxaca Mex

Control Performance Assessment (CPA) is a critical endeavor in industrial processes, ensuring optimal functioning of control systems. Traditionally, CPA has been addressed through solutions using some control performance indicators. Nowadays, the integration of data science and machine learning has emerged as a viable alternative, particularly in classification tasks related to CPA. That is, in a binary classification scheme, the goal is to predict whether incoming data from the control loop belongs to class 0 or 1, representing the absence or presence of an anomaly (performance degradation). In such a case, a trade-of between false positives and false negatives should be obtained, via the training phase of a given supervised machine learning structure for example. Usually, this is a conflicting trade-of, where multi-objective optimization techniques in the training phase of such learners could bring interesting results. In this paper, we explore the usability of multi-objective optimization training in machine learning, for control performance assessment classification. A database describing 30 control performance indicators (features) in a PID control loop is used. The obtained results indicate that the proposed approach could bring interesting applications to improve the performance of CPA classification systems.

关键词： Control performance assessment machine learning multi-objective optimization PID control multi-criteria analysis

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Agent-Based Modeling for Forest Fire Simulation in Chiang Mai, Thailand

Agent-Based Modeling for Forest Fire Simulation in Chiang Ma...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Teerapat Tappanom Natt Leelawat Kumpol Saengtabtim Jing Tang Risk and Disaster Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Industrial Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan Disaster and Risk Management Information Systems Research Unit Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Forest fires have a significant impact on ecosystems and human life, especially in northern Thailand, where Chiang Mai is severely affected. Traditional methods for detecting forest fires have limitations, so there is a need for advanced simulation models. This research uses Agent-Based Modeling (ABM) to develop a forest fire simulation for Chiang Mai. The methodology involves collecting historical data, performing Multiple Regression Analysis, designing the simulation, and testing it. Data from 1998 to 2021, including temperature, relative humidity, wind speed/direction, burn area, and slope, were collected from various sources. Multiple regression analysis identified wind speed as the most significant factor affecting burn area. The forest fire simulation, designed using ZF Wang's spread model and tested with AnyLogic, showed that wind direction and speed are crucial in fire spread. The simulation accurately predicted high-risk areas, helping in proactive planning and response. This study confirms that wind is a critical factor in forest fire spread, providing a valuable tool for fire districts to improve preparedness and management. Future research should focus on refining the model with localized data and integrating real-time detection to improve its accuracy and applicability.

关键词： Temperature distribution Biological system modeling Wind speed Refining Forestry Agent-based modeling Mathematical models Regression analysis Sensors Testing

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Customer Preferences for Online Games: A Conjoint Analysis Approach with Market Segmentation 22

Customer Preferences for Online Games: A Conjoint Analysis A...

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Proceedings of the 2022 6th International Conference on Software and e-Business

作者： Rogel Angelo Rebualos Yogi Tri Prasetyo Irene Dyah Ayuwati Satria Fadil Persada School of Industrial Engineering and Engineering Management Mapua University Philippines and School of Graduate Studies Mapua University Philippines School of Industrial Engineering and Engineering Management Mapua University Philippines and International Program in Engineering for Bachelor Yuan Ze University Taiwan Department of Information Systems Institut Teknologi Telkom Surabaya Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Indonesia

ISBN: (纸本)9798400700095

Online games display a unique market with a promising economy; hence many developer companies are exploring and have been utilizing this technology because of good possibility in recent years. This study aimed to determine the composition of online game attribute that is most preferred by customers using a Conjoint Analysis Approach. This study utilized different attributes such as graphics, player-to-player interaction, character creation freedom, game accessibility, monetization, storyline, and business model. An orthogonal design of conjoint analysis was utilized to assess customer preference for online games. A survey was proposed to a community of online game players and segmented among their gender, age, and income. The results showed that business model was the most considered attribute while accessibility and monetization were the least preferred. The findings of this study could be beneficial to both technology corporations developing products that fit their target customers and customers making most of their investment.

关键词： market segmentation preference conjoint online games

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