Gopal Saha | Operations Research | Best Researcher Award

Mr. Gopal Saha | Operations Research | Best Researcher Award

PhD student at IIT Roorkee | India

Mr. Gopal Saha is a dedicated researcher and Ph.D. candidate in Management Studies at the Indian Institute of Technology Roorkee, with primary research interests in Integer Programming, Optimization, Transportation and Routing Problems, Machine Learning, and Game Theory. His scholarly work focuses on integrating operations research and data analytics to address real-world decision-making and sustainability challenges. He has presented influential research papers at renowned international conferences, including the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) and the Transportation Infrastructure Projects: Conception to Execution (TIPCE), earning accolades such as a special mention award and selection for the Best Paper Award shortlist. Mr. Saha has actively participated in specialized academic workshops, including the ACM India Winter School, GIAN courses, and Large Scale Optimization programs at IITs, reflecting his deep engagement with cutting-edge research. His technical proficiency spans Python, C++, CPLEX, Gurobi, Pyomo, Sklearn, and Google OR-Tools, allowing him to combine computational models with theoretical insights in optimization and management science. As a Teaching Assistant, he has contributed to prestigious online programs on Artificial Intelligence, Machine Learning, and Strategic Supply Chain Management offered by IIT Roorkee in collaboration with Deloitte USI and Coursera. His growing academic footprint includes 1 citation, an h-index of 1, (all since 2020), highlighting his emerging impact in the field. Mr. Saha’s work exemplifies a strong blend of analytical rigor, innovation, and interdisciplinary research in optimization and management.

Profile: Google Scholar

Featured Publications

  1. Saha, G., & Gupta, M. K. (2023). Fair cost-savings allocation in transportation game. In Proceedings of the 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE.

  2. Saha, G., & Gupta, M. K. (2025). Fair cost savings allocation in two-stage fixed-cost transportation problem. INFOR: Information Systems and Operational Research, 1–39.

 

 

Fei Wang | Management Science | Best Researcher Award

Dr. Fei Wang | Management Science | Best Researcher Award

Lecturer at Hebei University of Economics and Business, China

Wang Fei is a lecturer at the School of Management Science and Engineering, Hebei University of Economics and Business. His research focuses on decision theory and methods, with expertise in fuzzy decision-making, multi-criteria decision-making (MCDM), and uncertainty modeling. He has published several high-impact papers in international journals and has led and participated in multiple research projects.

Publication Profile 

Orcid

Educational Background 🎓

  • Ph.D. in Economics and Management (September 2019 – January 2023)
    Yanshan University, School of Economics and Management
  • Master’s Degree in Economics and Management (September 2016 – June 2019)
    Yanshan University, School of Economics and Management

Professional Experience 💼

  • Lecturer (January 2023 – Present)
    School of Management Science and Engineering, Hebei University of Economics and Business

Research Interests 🔬

  • Decision Theory and Methods
  • Multi-Criteria Decision-Making (MCDM)
  • Fuzzy Systems and Uncertainty Modeling
  • Artificial Intelligence in Decision-Making
  • Data Science Applications in Management

Publications & Research Contributions

Wang Fei has authored several journal papers in high-impact publications, including Information Sciences and International Journal of Intelligent Systems. His research contributions include:

  • Development of novel multi-criteria decision-making (MCDM) models using fuzzy logic and prospect theory.
  • Applications of cubic fuzzy sets and similarity measurements in decision-making problems.
  • Exploration of neutrosophic and hesitant fuzzy systems in intelligent decision analysis.

Research Projects

Lead Investigator:

  • Hebei Education Department Science Research Project: “Research on Cubic Fuzzy Multi-Criteria Decision-Making Method and Its Applications” (Project No. BJK2024132)

Co-Investigator:

  • Public Opinion and Threat Analysis in Social Networks (Project No. 2019041201003)
  • Spatiotemporal Fuzzy Grading for Service Group Recommendation (Project No. HB19GL009)
  • Fuzzy Situation Analysis in Public Crisis Management (Project No. SQ201031)

Awards and Honors🏆✨

  • Recognized for contributions to fuzzy multi-criteria decision-making in academic research.
  • Published in top-tier international journals in the field of decision science and artificial intelligence.
  • Secured research funding for fuzzy logic applications in decision-making.

Conclusion🌟

Wang Fei is an emerging researcher in decision science and fuzzy logic-based multi-criteria decision-making. His work integrates mathematical modeling, uncertainty theory, and artificial intelligence, contributing to the advancement of intelligent decision support systems. As a lecturer and active researcher, he continues to drive innovations in fuzzy MCDM and its real-world applications.

Publications 📚

1️⃣ Novel score function and standard coefficient-based single-valued neutrosophic MCDM for live streaming sales
🏛 Journal: Information Sciences
📅 Date: January 2024
🔗 DOI: 10.1016/j.ins.2023.119836
Contributor(s): Fei Wang


2️⃣ Similarity and Pythagorean reliability measures of multivalued neutrosophic cubic set and its application to multiple‐criteria decision‐making
🏛 Journal: International Journal of Intelligent Systems
📅 Date: January 2022
🔗 DOI: 10.1002/int.22618
📖 ISSN: 0884-8173 / 1098-111X
Contributor(s): Fei Wang, Xiaodong Zhao


3️⃣ Prospect‐theory and geometric distance measure‐based Pythagorean cubic fuzzy multicriteria decision‐making
🏛 Journal: International Journal of Intelligent Systems
📅 Date: August 26, 2021
🔗 DOI: 10.1002/int.22453
Contributor(s): Fei Wang, Xiaodong Zhao