Applications of Management Science
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- Description
- Contents
- About
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.
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
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.