This book showcases state-of-the-art advances in service science and related fields of research, education, and practice. It presents emerging technologies and applications in contexts ranging from healthcare, energy, finance, and information technology to transportation, sports, logistics, and public services. Regardless of its size and service, every service organization is a service system. Due to the socio-technical nature of service systems, a systems approach must be adopted in order to design, develop and deliver services aimed at meeting end users’ utilitarian and socio-psychological needs alike. Understanding services and service systems often requires combining multiple methods to consider how interactions between people, technologies, organizations and information create value under various conditions. The papers in this volume highlight a host of ways to approach these challenges in service science and are based on submissions to the 2021 INFORMS Conference on Service Science.
Inhaltsverzeichnis
Deep Learning and Prediction of Survival Period for Breast Cancer Patients.- Should Managers Care About Intra-Household Heterogeneity?.- Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications.- Prediction of Gasoline Octane Loss Based on t-SNE and Random Forest.- Introducing AI General Practitioners to Improve Healthcare Services.- A U-Net Architecture Based Model for Precise Air Pollution Concentration Monitoring.- An Interpretable Ensemble Model of Acute Kidney Disease Risk.- Algorithm for Predicting Bitterness of Children’s Medication.- Intelligent Identification of High Emission Road Segment Based on Large-Scale Traffic Datasets.- Construction Cost Prediction for Multi-family Housing Projects based on Support Vector Regression.- Evolution of Intellectual Structure of Data Mining Research Based on Keywords.- Development of a Cost Optimization Algorithm for Food and Flora Waste to Fleet Fuel (F4).
Über den Autor
Robin Qiu, a full professor of information science, teaches a variety of courses on e.g. 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. His research interests include big data, data/business analytics, smart service systems, service science, service operations and management, information systems, and manufacturing and supply chain management.
Kelly Lyons is a professor at the Faculty of Information, University of Toronto with a cross appointment to the Department of Computer Science. Prior to joining the Faculty of Information, she was the program director of the IBM Toronto Lab Centre for Advanced Studies (CAS). Her current research interests include service science, knowledge mobilization, data science, social media, and collaborative work. From 2015 to 2020, she served as an associate dean, Academic at the Faculty of Information. From 2020 to 2021, she is serving as the dean’s advisor on Pandemic Planning and Response. She has co‐authored several papers, served on program committees for conferences, given many keynote and invited presentations, and co‐chaired several workshops. She has received an NSERC Strategic Partnership Grant, NSERC Discovery Grants, an NSERC Collaborative Research and Development Grant with SAP, two NSERC Engage Grants (with Science Scape and Dell), MITACS Accelerate Grants (with CA, IBM, and Cerebri AI), an SSHRC Knowledge Synthesis Grant, and an IBM Smarter Planet Faculty Innovation Grant, as well as funding from the GRAND Networks of Centers of Excellence (NCE). She is an IBM faculty fellow and a faculty affiliate of the Schwartz Reisman Institute for Technology and Society. She is currently on the Board of CS-Can/Info-Can and on the Board of the Informs Service Science Section. From 2008 to 2012, she was a member‐at‐large of the ACM Council and a member of the Executive Council of ACM‐W.
Weiwei Chen is an associate professor at Rutgers University. His research interests lie 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 the public sector, especially in energy and healthcare, to improve strategic decisions and operational efficiency using data analytics.
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