An Integer-Programming Approach ( Research Publication )

PhD Candidate, University at Buffalo, USA

Niraj Kumar Pandey
Niraj Kumar Pandey

Niraj Kumar Pandey is a PhD candidate at the Industrial and Systems Engineering department of the University at Buffalo. He earned his Bachelor of Technology degree in Production Engineering from Veer Surendra Sai University of Technology ( 2004 – 2008 ) in India, and MS in Operations Research from the Industrial and Systems Engineering of the University at Buffalo. His doctoral dissertation develops sequential decision models and inverse optimization techniques to quantify the adverse affects of lipid abnormalities and cholesterol treatment to facilitate better decision making for diabetes patients. His research also spans computational aspects of optimization by exploring efficient algorithms to tackle large-scale applied problems. He has presented his research in various major conferences organized by INFORMS, IIE and Society of Medical Decision Making and at a workshop sponsored by the National Science Foundation. While majored as an engineer, he has a great interest in literature. With research interests in scheduling sports leagues, his other hobbies include playing and watching soccer, football and basketball.

PAPER – Alleviating Competitive Imbalance in NFL Schedules: An Integer-Programming Approach

Abstract: The NFL uses numerous complex rules in scheduling regular season games to maintain fairness, attractiveness and its wide appeal to all fans and franchises. While these rules balance a majority of the features, they are not robust in spacing games to avoid competitive imbalance. We consider the scheduling of NFL regular season games and formulate a mixed-integer linear program (MILP) to alleviate competitive disadvantages originating from the assignment of bye weeks, Thursday games and streaks of home-away games among various other sources. We propose a two-phase heuristic approach to seek solutions to the resulting large-scale MILP and conduct computational experiments to illustrate how past NFL schedules could have been improved for fairness. We also demonstrate the efficiency and stability of our approach by creating balanced schedules on an extensive set of simulated possible future NFL seasons. Our experiments show that the heuristic can quickly create a large pool of schedules that are completely free of disadvantages due to scheduling of bye-weeks and well-balanced in preparation time differences due to Thursday games.


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