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Shared Mental Models and Boundary management Activities in New Product Development: A Perspective on Multiteam systems

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IEEE Transactions on engineering management 2024年 71卷 1073-1085页

作者： Lei, Yang Wu, Xiaosong Jiang, James The Department of Information Systems and Management Engineering College of Business Southern University of Science and Technology Shenzhen518055 China The Department of Innovation Management Tsinghua Shenzhen International Graduate School Shenzhen518071 China The Department of Business Administration College of Management National Taiwan University Taipei10617 Taiwan

—Multiteam systems (MTSs) have been increasingly adopted as an organizational form to manage large-scale projects. However, new product development (NPD) in the MTSs structure entails challenges with respect to achieving efficiency due to interteam coordination challenges. Shared mental models (SMMs) within a single team have been found to facilitate intrateam coordination;however, further exploration is needed as to whether SMMs within MTSs can improve interteam coordination among component teams and, if so, what mechanisms underlie this. The present article examines the theoretical underpinning of the impacts of SMMs among component team leaders on MTSs’ NPD efficiency, and further considers the impact of external conditions of requirement uncertainty, the most common external situation during NPD. Using survey data from 110 information technology development MTSs and drawing on boundary management perspective, the results reveal that SMMs among component team leaders positively affect NPD efficiency by managing the MTSs boundary, i.e., through boundary entrainment and boundary buffering activities at the system level. In addition, we find that requirement uncertainty plays a vital role in determining the nature of this relationship. Taken together, our findings highlight the importance of the role of SMMs among component team leaders in facilitating NPD efficiency in MTSs settings. © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Project management

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Synthetic damage effect assessment through evidential reasoning approach and neural fuzzy inference:Application in ship target

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Chinese Journal of Aeronautics 2022年第8期35卷 143-157页

作者： Tianle YAO Run MIAO Weili WANG Zhirong LI Jun DONG Yajuan GU Xuefei YAN Ordnance Engineering College Naval University of EngineeringWuhan 430000China Xi’an Modern Chemistry Research Institute Xi'an 710065China Department of Equipment Command and Management Army Engineering UniversityShijiazhuang 050003China Institute of Systems Engineering Academy of Military ScienceBeijing 100082China

The damage effect assessment of anti-ship missiles combines system science and weapon science,which can provide reference for the assessment of battlefield damage *** order to solve the difficulty of heterogeneous data aggregation and the difficulty in constructing the mapping between factors and damage effect,this paper analyzes the specific damage process of the anti-ship missile to the ship,and proposes a synthetic Evidential Reasoning(ER)–Adaptive Neural Fuzzy Inference System(ANFIS)to assess the damage *** solve the problem of fuzziness and uncertainty of criteria in the assessment process,the belief structure model is used to transform qualitative and quantitative information into a unified mathematical structure,and ER algorithm is used to fuse the information of lower-level *** order to solve the problem of fuzziness and uncertainty of the information contained in the first-level variables,and the strong non-linear characteristics of the mapping between first-level variables and damage effect,the ANFIS with selfadaptation and self-learning is *** map between the three first-level variables and damage effect is established,and the interaction process of the various factors in the damage effect assessment are *** analysis shows that assessment model has good *** result analysis and comparative analysis show that the process proposed in this paper can effectively assess the damage effect of anti-ship missiles,and all criteria data are objective and comparable.

关键词： ANFIS Belief structure model Damage effect assessment ER approach Ship target

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Condition-based maintenance via Markov decision processes: A review

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Frontiers of engineering management 2025年第2期12卷 330-342页

作者： Xiujie ZHAO Piao CHEN Loon Ching TANG College of Management and Economics Tianjin UniversityTianjin 300072China ZJU-UIUC Institute Zhejiang UniversityHaining 314400China Department of Industrial Systems Engineering and Management National University of SingaporeSingapore 117576Singapore

The optimization of condition-based maintenance (CBM) poses challenges due to the rapid advancement of monitoring technologies. Traditional CBM research has mainly relied on theory-driven approaches, which lead to the development of several effective maintenance models characterized by their wide applicability and attractiveness. However, when the system reliability model becomes complex, such methods may run into intractable cost models. The Markov decision process (MDP), a classic framework for sequential decision making, has drawn increasing attention for optimization of CBM optimization due to its appealing tractability and pragmatic applicability across different problems. This paper presents a review of research that optimizes CBM policies using MDP, with a focus on mathematical modeling and optimization methods to enable action. We have organized the review around several key components that are subject to similar mathematical modeling constraints, including system complexity, the availability of system conditions, and diverse criteria of decision-makers. An increase in interest has led to the optimization of CBM for systems possessing increasing numbers of components and sensors. Then, the review focuses on joint optimization problems with CBM. Finally, as an important extension to traditional MDPs, reinforcement learning (RL) based methods are also reviewed as ways to optimize CBM policies. This paper provides significant background research for researchers and practitioners working in reliability and maintenance management, and gives discussions on possible future research directions.

关键词： reliability degradation modeling dynamic programming reinforcement learning sequential decision problems

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Leveraging publicly available data to evaluate health reform: establishing a methodology to assess the future impact of over-the-counter hearing aids

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Discover Health systems 2024年第1期3卷 1-16页

作者： Kruger, Sarah E. Sarkani, Shahram Mazzuchi, Thomas A. Department of Engineering Management and Systems Engineering School of Engineering and Applied Science The George Washington University Washington USA

Health systems are complex and health reform is uncertain. With an aging global population, chronic health conditions are increasing and have become a priority for policymakers. Hearing loss is the third most common chronic condition among older adults and if left untreated, is associated with poor health and social outcomes. In the US, accessibility and affordability have been significant barriers to hearing aid utilization. Through new reform, over-the-counter (OTC) hearing aids are now available. This study demonstrates how publicly available health data was used to establish a methodology to define, characterize, and analyze the target population of this reform. In this retrospective cross-sectional cohort analysis, data from the National Health Interview Survey (NHIS) was utilized, with a sample that included respondents from the 2021 NHIS Sample Adult Interview. Three hearing aid groups (Active, Potential, General) were defined using questions from the NHIS annual core questionnaire and a baseline population health framework was developed. Binary logistic regression confirmed that the framework sufficiently described two group comparisons (Active-Potential, Active General) and also identified the key determinants of hearing aid utilization. Classification and regression tree (CART) classification amply predicted active hearing aid users and verified the key determinants as important predictors. These findings aligned nicely with literature published before OTC hearing aids were made available, confirming the NHIS to be consistent and representative. Our methodology can be repeated to examine the impact of OTC hearing aids in the post-implementation period and similar approaches should be taken to examine other emerging health reforms.

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PROMISE: preconditioned stochastic optimization methods by incorporating scalable curvature estimates

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 16974-17030页

作者： Zachary Frangella Pratik Rathore Shipu Zhao Madeleine Udell Department of Management Science and Engineering Stanford University Department of Electrical Engineering Stanford University Department of Systems Engineering Cornell University

Ill-conditioned problems are ubiquitous in large-scale machine learning: as a data set grows to include more and more features correlated with the labels, the condition number increases. Yet traditional stochastic gradient methods converge slowly on these ill-conditioned problems, even with careful hyperparameter tuning. This paper introduces PROMISE (Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates), a suite of sketching-based preconditioned stochastic gradient algorithms that deliver fast convergence on ill-conditioned large-scale convex optimization problems arising in machine learning. PROMISE includes preconditioned versions of SVRG, SAGA, and Katyusha; each algorithm comes with a strong theoretical analysis and effective default hyperparameter values. Empirically, we verify the superiority of the proposed algorithms by showing that, using default hyperparameter values, they outperform or match popular tuned stochastic gradient optimizers on a test bed of 51 ridge and logistic regression problems assembled from benchmark machine learning repositories. On the theoretical side, this paper introduces the notion of quadratic regularity in order to establish linear convergence of all proposed methods even when the preconditioner is updated infrequently. The speed of linear convergence is determined by the quadratic regularity ratio, which often provides a tighter bound on the convergence rate compared to the condition number, both in theory and in practice, and explains the fast global linear convergence of the proposed methods.

关键词： stochastic optimization preconditioning randomized low-rank approximation lazy Hessians

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Stochastic Scheduling for Batch Processes With Downstream Queue Time Constraints

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IEEE Transactions on Semiconductor Manufacturing 2023年第4期36卷 599-610页

作者： Chien, Wen-Chi Chou, Ywh-Leh Wu, Cheng-Hung Taiwan Semiconductor Manufacturing Company Ltd. Fab Operations Hsinchu30078 Taiwan Feng Chia University Department of Industrial Engineering and Systems Management Taichung City407102 Taiwan National Taiwan University Institute of Industrial Engineering The Department of Business Administration The Department of Mechanical Engineering Taipei City106 Taiwan

This research studies the problems of stochastic dynamic scheduling in production systems with batch processes and process queue time (PQT) constraints. The production systems consist of upstream batch processing machines and downstream single processing machines. Under the PQT constraint, waiting time in the downstream queue is constrained by an upper limit and violating this constraint causes scraps of jobs. The batch process increases the probability of PQT constraint violation because a batch of work-in-processes (WIPs) will move simultaneously into the downstream queue after the service completion of batch processes and suffer from higher waiting time variance. A batch process admission control (BPAC) model is developed using Markov decision processes to minimize the sum of long-run average waiting and scrap costs. The proposed BPAC model explicitly considers uncertain factors in production systems given that uncertainties are major reasons for PQT constraint violation. These uncertain factors include job arrival, processing time, and machine breakdown/repair. To cope with these uncertain factors, the BPAC control decisions change dynamically with the real-time machine health and WIP distribution. The performance of BPAC is validated using discrete event simulation, and the simulation results confirm the significant performance improvement in a wide range of batch production environments. © 1988-2012 IEEE.

关键词： Stochastic systems

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Identifying and addressing disruptions in healthcare supply chain management 13

Identifying and addressing disruptions in healthcare supply ...

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13th International Workshop on Innovative Simulation for Health Care, IWISH 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Patel, Puravkumar Shah, Smitesh Carpitella, Silvia Abolghasem, Sepideh Department of Manufacturing Systems Engineering and Management California State University 18111 Nordhoff St NorthridgeCA91330 United States

ISBN: (纸本)9791281988132

The COVID-19 pandemic has put a spotlight on the global supply chain for medical equipment and medicines, highlighting vulnerabilities and disruptions in the healthcare *** paper aims to contribute to the ongoing research on enhancing supply chain resilience in healthcare by exploring the critical issues surrounding supply chain management in this sector and presenting strategies aimed at identifying and mitigating ***, we propose a model using fuzzy logic to address the inherent uncertainty in analyzing key barriers in supply chain management for the healthcare *** findings emphasize the crucial need for robust and adaptable supply chains to ensure a consistent supply of essential medical resources, especially during times of crisis. © 2024 The Authors.

关键词： COVID-19

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GA-Stacking:A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

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Computers, Materials & Continua 2023年第2期74卷 3945-3976页

作者： Walaa N.Ismail Hessah A.Alsalamah Ebtesam Mohamed Department of Management Information Systems College of Business AdministrationAl Yamamah UniversityRiyadh11512Saudi Arabia Faculty of Computers and Information Minia UniversityMinia61519Egypt Information Systems Department College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Computer Engineering Department College of Engineering and ArchitectureAl Yamamah UniversityRiyadh11512Saudi Arabia

As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic *** ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble *** utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction ***,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly ***,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided ***-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first *** also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness *** the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final *** performance of the model was evaluated using a publicly available dataset from the Center for systems Science and engineering,Johns Hopkins University,which consisted of 10,722 data *** experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected ***,the proposed model achieved good performance when compared with existing baggingbased *** proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to pre

关键词： COVID-19 ensemble machine learning genetic algorithm machine learning stacking ensemble unbalanced dataset vaccine

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An Integrated Production Parameters Decision on Multi-Stage Sequential Manufacturing Through Experimental Design and Mathematical programming

An Integrated Production Parameters Decision on Multi-Stage ...

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2023 IEEE International Conference on Industrial engineering and engineering management, IEEM 2023

作者： Jeang, A. Chung, C.P. Feng Chia University Department of Industrial Engineering and Systems Management Taiwan National Taichung University of Science and Technology Department of Intelligent Production Engineering Taiwan

ISBN: (纸本)9798350323153

This study focuses on optimizing production parameters within a multi-stage sequential manufacturing system for the rayon fiber coagulating bath recycle process. It employs experimental design and mathematical programming techniques to address the interdependence and interactions among these stages. Unlike previous approaches that solely concentrated on single process optimization, often resulting in suboptimal designs and increased production costs due to idle time, our approach integrates the filtering, evaporating, and crystallizing processes as a cohesive system. The objective function, derived from experimental design, considers process and equipment limitations, input-output balance, and heat energy balance, with the overarching aim of minimizing production costs while meeting production constraints. To underscore the significance of our integrated approach, we provide a comparison between non-integration and our present approach, highlighting the crucial importance of this multi-stage sequential manufacturing process for enhanced efficiency and cost-effectiveness. © 2023 IEEE.

关键词： Integration production parameters optimization multi-stage sequential manufacturing Rayon fiber coagulating bath recycle system

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An Intelligent Data Analytics Approach for Crisis management in Healthcare systems

An Intelligent Data Analytics Approach for Crisis Management...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Madian, Fatma A. Ebrahim, Gamal A. Salah, Mostafa M. Abdelaal, Marwa A. Ain Shams University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Future University in Egypt Electrical Engineering Department Cairo Egypt Faculty of Computers and Information Sadat Academy for Management Sciences Computer Science Department Cairo Egypt

ISBN: (纸本)9798350367560

Crisis management is preparing for and managing possible crises that may impact organizations and individuals at different levels. It involves effective communication, quick decision-making, and strategic planning to minimize the negative impact of a crisis and ensure swift recovery. It plays a vital role in healthcare systems, especially in virus outbreaks. Its role is to monitor and manage the spread of viruses. Moreover, it can use Machine Learning (ML) techniques to achieve this goal. This paper discusses how to apply ML approaches to daily reports of infection cases. The adopted ML approaches are Auto Regressive Integrated Moving Average (ARIMA) model and the Long Short-Term Memory (LSTM) model. Furthermore, it uses Root Mean Square Error (RMSE) as a performance measure to evaluate the applied models. Simulation results show that ARIMA model performs better as compared to other models, which can provide a prediction accuracy of more than 99%. © 2024 IEEE.

关键词： Electronic health record

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