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Smart Service Systems, Operations Management, and Analytics

Proceedings of the 2019 INFORMS International Conference on Service Science

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Hui Yang

    1. Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, USA

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  2. Robin Qiu

    1. Division of Engineering and Information Science, Pennsylvania State University, Malvern, USA

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  3. Weiwei Chen

    1. Department of Supply Chain Management, Rutgers, The State University of New Jersey, Piscataway, USA

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  • Presents recent advances in using smart service systems, operations management, and/or analytics in service science research
  • Highlights emerging technology and state-of-the-art applications for service science
  • Includes service case studies written by scholars and practitioners worldwide

Part of the book series: Springer Proceedings in Business and Economics (SPBE)

Included in the following conference series:

Conference proceedings info: INFORMS-CSS 2019.

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Table of contents (35 papers)

  1. Front Matter

    Pages i-viii
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  2. Cleaning and Processing on the Electric Vehicle Telematics Data

    • Shuai Sun, Jun Bi, Cong Ding
    Pages 1-6

  3. Performance Analysis of a Security-Check System with Four Types of Inspection Channels for High-Speed Rail Stations in China

    • Chia-Hung Wang, Xiaojing Wu
    Pages 7-16

  4. LSTM-Based Neural Network Model for Semantic Search

    • Xiaoyu Guo, Jing Ma, Xiaofeng Li
    Pages 17-25

  5. Research on the Evaluation of Electric Power Companies’ Safety Capabilities Based on Grey Fixed Weight Clustering

    • Yijing Wang, Jie Xu, Chuanmin Mi, Zhipeng Zhou
    Pages 27-39

  6. Analysis of Crude Oil Price Fluctuation and Transition Characteristics at Different Timescales Based on Complex Networks

    • Jiao Yan, Jing Ma
    Pages 41-53

  7. Understanding of Servicification Trends in China Through Analysis of Interindustry Network Structure

    • Yunhan Liu, Dohoon Kim
    Pages 55-64

  8. Machine Learning Methods for Revenue Prediction in Google Merchandise Store

    • Vahid Azizi, Guiping Hu
    Pages 65-75

  9. Predicting Metropolitan Crime Rates Using Machine Learning Techniques

    • Saba Moeinizade, Guiping Hu
    Pages 77-86

  10. Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction

    • Mohsen Shahhosseini, Guiping Hu, Hieu Pham
    Pages 87-97

  11. How Do Pricing Power and Service Strategy Affect the Decisions of a Dual-Channel Supply Chain?

    • Houping Tian, Chaomei Wu
    Pages 99-111

  12. Designing Value Co-creation for a Free-Floating e-Bike-Sharing System

    • Christoph Heitz, Marc Blume, Corinne Scherrer, Raoul Stöckle, Thomas Bachmann
    Pages 113-125

  13. Research on Electricity Falling Accident Based on Improved Bode Accident Causation Model

    • Qian Yuanyuan, Xu Jie, Mi Chuanmin, Peng Qiwei
    Pages 127-137

  14. Crop Yield Prediction Using Deep Neural Networks

    • Saeed Khaki, Lizhi Wang
    Pages 139-147

  15. Cloud-Based Life Sciences Manufacturing System: Integrated Experiment Management and Data Analysis via Amazon Web Services

    • Pei Guo, Raymond Peterson, Paul Paukstelis, Jianwu Wang
    Pages 149-159

  16. Matching Anonymized Individuals with Errors for Service Systems

    • Wai Kin (Victor) Chan
    Pages 161-168

  17. Developing a Production Structure Model Using Service-Dominant Logic—A Hypergraph-Based Modeling Approach

    • Mahei Manhai Li, Christoph Peters, Jan Marco Leimeister
    Pages 169-182

  18. Airworthiness Evaluation Model Based on Fuzzy Neural Network

    • Jie-Ru Jin, Peng Wang, Yang Shen, Kai-Xi Zhang
    Pages 183-194

  19. Two-Level Trip Selection and Price Incentive Scheduling in Electric Vehicle-Sharing System

    • Zihao Jiao, Xin Liu, Lun Ran, Yuli Zhang
    Pages 195-207

  20. Research on the Method of Identifying Opinion Leaders Based on Online Word-of-Mouth

    • Chenglin He, Shan Li, Yehui Yao, Yu Ding
    Pages 209-222
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Other volumes

  1. Smart Service Systems, Operations Management, and Analytics

Keywords

About this book

This volume offers state-of-the-art research in service science and its related research, education and practice areas. It showcases recent developments in smart service systems, operations management and analytics and their impact in complex service systems. The papers included in this volume highlight emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users‘ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. The papers in this volume present methods to approach such technical challenges in service science and are based on top papers from the 2019 INFORMS International Conference on Service Science.

Editors and Affiliations

  • Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, USA

    Hui Yang

  • Division of Engineering and Information Science, Pennsylvania State University, Malvern, USA

    Robin Qiu

  • Department of Supply Chain Management, Rutgers, The State University of New Jersey, Piscataway, USA

    Weiwei Chen

About the editors

Hui Yang is an Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. Dr. Yang's research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization.
Robin Qiu is a tenured full Professor of Information Science, teaches a variety of courses including Predictive Analytics, Management Science, Business Process Management, Decision Support Systems, Project Management, Enterprise Integration, Enterprise Service Computing, Software Engineering, Web-based Systems, Distributed Systems, Computer Architecture/SOA, Computer Security, Web Security, Operations Research, and System Engineering. Dr. Qiu’s research interests include Big Data, Data/Business Analytics, Smart Service Systems, Service Science, Service Operationsand Management, Information Systems, and Manufacturing and Supply Chain Management.
Weiwei Chen is an Associate Professor of Supply Chain Management in Rutgers Business School – Newark and New Brunswick at Rutgers University. Dr. Chen’s current research interest lies in operations and finance interface, as well as supply chain operations planning and scheduling. He also works on simulation and randomized global optimization methodologies. He has extensive experience working with businesses and public sectors to improve strategic decisions and operational efficiencies using data analytics. He has taught courses in optimization modeling, operations analysis, and lean six sigma.

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