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Tushar Shankar Walunj

Welcome

Tushar Shankar Walunj
PhD Candidate, IIT Bombay
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About Me

I am a passionate researcher specializing in game theory, optimization, and operations research. My work focuses on solving real-world problems in service systems and platform-based economies.

Research Scholar

Industrial Engineering and Operations Research,

Indian Institute of Technology Bombay.

📧 tusharwalunj@iitb.ac.in | twalunj9@gmail.com

Supervisors:

Research Interest:

Operations Research | Optimization | Modeling | Supply Chains
Reinforcement Learning | Game Theory | Queueing Theory

Academic Background:

  • M.Sc - Ph.D. in Operations Research (2019- present), IE&OR, IIT Bombay, Mumbai
  • B.Sc Mathematics (2016-2019), S.P. College, Pune University, Pune

Academic Publications:

  • Tushar Shankar Walunj, Shiksha Singhal, Jayakrishnan Nair, and Veeraruna Kavitha, 2024. Equilibrium Cycle: A "Dynamic" Equilibrium. Submitted at Econometrica 2024. (arXiv preprint arXiv:2411.08471.)
  • Tushar Shankar Walunj, Shiksha Singhal, Veeraruna Kavitha, and Jayakrishnan Nair, 2022, September. Pricing, competition and market segmentation in ride hailing. In 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (pp. 1-8). IEEE.
  • Tushar Shankar Walunj, Shiksha Singhal, Jayakrishnan Nair, and Veeraruna Kavitha, 2024. On the interplay between pricing, competition and QoS in ride-hailing. Under review at ANOR, 2023. (arXiv preprint arXiv:2308.14496.)
  • Gurkirat Wadhwa, Tushar Shankar Walunj, and Veeraruna Kavitha. ``Partition-form Cooperative Games in Two-Echelon Supply Chains.'' ICORES 2024: 13th International Conference on Operations Research and Enterprise Systems, 2024.

Key Projects

Jaltantra: Optimizing Rural Water Networks for Cost Reduction

Advisor: Prof. Ashutosh Mahajan

  • Developed a non-convex optimization model to minimize construction costs in rural water systems.
  • Optimized pipe diameters to maintain required water pressure while reducing overall costs.
  • Outperformed existing heuristic algorithms on real-world data, achieving significant cost reductions.
Strategic Facility Location Optimization with MILP and Heuristics

Advisor: Prof. Ashutosh Mahajan

  • Designed a Mixed-Integer Linear Program (MILP) to optimize facility locations, considering transport capacities and coverage limits.
  • Developed a two-phase solution combining clustering and facility optimization for improved cost efficiency.
  • Analyzed customer assignments to minimize distribution costs over multi-period horizons.
Dynamic Pricing in Competitive Two-Sided Ride-Hailing Platforms

Advisors: Prof. Veeraruna Kavitha, Prof. Jayakrishnan Nair

  • Applied dynamic pricing strategies based on driver availability to optimize supply and demand balance.
  • Analyzed the impact of market dynamics on pricing strategies and overall platform revenue.
Optimizing COVID-19 Vaccine Distribution with Logistics and OR Models

Advisor: Prof. Narayan Rangaraj

  • Modeled P-median and P-center problems to optimize facility locations for enhanced accessibility.
  • Identified bottlenecks in cold chain infrastructure and proposed strategies for improved efficiency.
  • Developed a cost-effective solution ensuring reliable and equitable vaccine delivery.
Strategic Coalition Formation and Resource Sharing for Profit Optimization

Advisors: Prof. Veeraruna Kavitha, Prof. Jayakrishnan Nair

  • Designed a cooperative queuing model enabling partial resource sharing among service providers.
  • Optimized profitability through strategic coalition formation and resource allocation.

Revenue Optimization in Two-Sided Queuing Platforms

Advisors: Prof. Veeraruna Kavitha, Prof. Jayakrishnan Nair

  • Created a mathematical model to maximize platform revenue with customers on both sides.
  • Evaluated the impact of customer volume on optimal pricing and platform revenue strategies.

 Mentoring Experience:

Teaching Assistant:

Courses:

Core Courses: Optimization Models, Operations Analysis, Computer Programming and Algorithms, Engineering Statistics, Modeling and Computation Lab (IE 507), ​Probabilistic Models, Service and Infrastructure Systems, Linear Systems, Decision Analysis and Game Theory, Optimization Techniques, Economics, Probability and Stochastic Processes II, IEOR Lab.

Electives:  Foundations of Machine Learning, Networks, Games and Algorithms, Applied Mathematical Analysis in Engineering, Matrix Computation, Quantitative Models for Supply Chain Management, Integer Programming: Theory and Computations,  Networks, Games and Algorithms, Simulation Modeling and Analysis, Markov decision processes, Topics in Industrial Engineering and Operations Research, Markov Chains and Queuing Systems. 

Position of Responsibility:

Student Companion, Institute Student Companion Programme (ISCP), 2020-21