Applications of Management Science
Published on 8. August 2022
Book
Hardback
184 pages
978-1-80071-552-3 (ISBN)
Description
Applications of Management Science showcases current studies in the application of management science, contributing to the solution of significant managerial decision-making problems. To those involved in the applications of multi-criteria decision making, data envelopment analysis, and decision making, in a realistic managerial problem-solving environment through the use of state-of-the-art management science modeling, this is a must read.
The research presented by academics in Volume 13 significantly aids in the deconstruction of managerial decision-making problems with management science methodologies. Specifically focusing on the applications of management science methodologies data envelopment analysis and multi-criteria decision making, this collection is split into three sections: Data Envelopment Analysis, Optimization Modeling, Business Analytical Modeling.
Applications of Management Science is core for those academics, researchers, and practitioners of management science in mitigating significant managerial decision-making problems, for both the public and the private sectors.
The research presented by academics in Volume 13 significantly aids in the deconstruction of managerial decision-making problems with management science methodologies. Specifically focusing on the applications of management science methodologies data envelopment analysis and multi-criteria decision making, this collection is split into three sections: Data Envelopment Analysis, Optimization Modeling, Business Analytical Modeling.
Applications of Management Science is core for those academics, researchers, and practitioners of management science in mitigating significant managerial decision-making problems, for both the public and the private sectors.
More details
Series
Language
English
Place of publication
Bingley
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 15 mm
Weight
426 gr
ISBN-13
978-1-80071-552-3 (9781800715523)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kenneth D. Lawrence | Dinesh R. Pai
Applications of Management Science
E-Book
08/2022
1st Edition
Emerald Publishing Limited
€92.49
Available for download
Persons
Kenneth D. Lawrence is a Professor of Management Science and Business Analytics at the Tuchman School of Management at the New Jersey Institute of Technology, U.S.A. His research centers on forecasting, multi-criteria decision making, data envelopment analysis, and data mining.
Dinesh R. Pai is an Associate Professor of Supply Chain Management in the School of Business Administration, Penn State Harrisburg, U.S.A. His research interests are in the area of supply chain management, business analytics, and performance evolution.
Dinesh R. Pai is an Associate Professor of Supply Chain Management in the School of Business Administration, Penn State Harrisburg, U.S.A. His research interests are in the area of supply chain management, business analytics, and performance evolution.
Content
Part A. Data Envelopment Analysis
Chapter 1. Insurance Company Efficiency Analysis through Data Envelopment Analysis (DEA) during COVID-19 Pandemic; Gao Niu, John Quinn, and Alan Olinsky
Chapter 2. Are All Gold Medal Mutual Funds Equally Efficient?; D.K Malholtra, Rashmi Malholtra, and Robert Nydick
Chapter 3. Covid-19 and Performance of Cosmetics Companies; Rashmi Malhotra, D. K Malhotra, and Shubha Bennur
Chapter 4. Productivity in the New Jersey PPO Health Insurance Industry, 2018; Kenneth D. Lawrence and Sheila M. Lawrence
Chapter 5. Negative Data in Data Envelopment Analysis (DEA); Kenneth D. Lawrence, Sheila M. Lawrence, and Dinesh R. Pai
Part B. Optimization Modeling
Chapter 6. Optimization of the Geothermal Energy for District Heating in the Polish Tatras Region: A Case Study; Bartosz Sawik
Chapter 7. Profitability, Sustainability, and Product Development; Nand K. Jha
Part C. Business Analytical Modeling
Chapter 8. Comprehensive Business Diagnostics of a Company's Activities; Naila Aliyeva, Sergei Chernov, Liudmila Babaskina, and Mariya Zakharova
Chapter 9. Optimal Bundling of Platform Memberships under Horizontal Cooperation; Xingping Zhang, Feng Yang, Yaqin Hu, and Zhimin Huang
Chapter 10. Mitigating Administrative Fraud in Warranty Service for Remanufactured Products; Aditya Pandit, and Surendra M. Gupta
Chapter 11. Experiential Learning Exercise in an Analytics Class; Ronald Klimberg, Michael Marzano, and Michael Bruening
Chapter 1. Insurance Company Efficiency Analysis through Data Envelopment Analysis (DEA) during COVID-19 Pandemic; Gao Niu, John Quinn, and Alan Olinsky
Chapter 2. Are All Gold Medal Mutual Funds Equally Efficient?; D.K Malholtra, Rashmi Malholtra, and Robert Nydick
Chapter 3. Covid-19 and Performance of Cosmetics Companies; Rashmi Malhotra, D. K Malhotra, and Shubha Bennur
Chapter 4. Productivity in the New Jersey PPO Health Insurance Industry, 2018; Kenneth D. Lawrence and Sheila M. Lawrence
Chapter 5. Negative Data in Data Envelopment Analysis (DEA); Kenneth D. Lawrence, Sheila M. Lawrence, and Dinesh R. Pai
Part B. Optimization Modeling
Chapter 6. Optimization of the Geothermal Energy for District Heating in the Polish Tatras Region: A Case Study; Bartosz Sawik
Chapter 7. Profitability, Sustainability, and Product Development; Nand K. Jha
Part C. Business Analytical Modeling
Chapter 8. Comprehensive Business Diagnostics of a Company's Activities; Naila Aliyeva, Sergei Chernov, Liudmila Babaskina, and Mariya Zakharova
Chapter 9. Optimal Bundling of Platform Memberships under Horizontal Cooperation; Xingping Zhang, Feng Yang, Yaqin Hu, and Zhimin Huang
Chapter 10. Mitigating Administrative Fraud in Warranty Service for Remanufactured Products; Aditya Pandit, and Surendra M. Gupta
Chapter 11. Experiential Learning Exercise in an Analytics Class; Ronald Klimberg, Michael Marzano, and Michael Bruening