Optimal Planning for Large Scale Logistics – (2024-present)

Debanjan Gangopadhyay (IEOR), under the supervision of Ashutosh Mahajan Abstract:- The aim is to build mathematical models, especially optimization models, to actually capture the complexities of a trucking network. The goal is to look for optimal solutions, which are hard to obtain, considering the huge size of the problem, and this endeavor may involve the use of heuristics or other techniques to solve the data driven problem instances in polynomial time.

Empirical Analysis of Resource Utilization in Indian Aviation: Mathematical Models for Enhancing Capacity and Network Optimization under the UDAN Regional Connectivity Scheme for Sustainable Operations – (2024-present)

Shashank Jain (IEOR), under the supervision of Priyank Sinha Abstract:- This study focuses on reducing these operational costs and developing dynamic decision-making tools for managing passenger load factor. We aim to ascertain the most efficient distribution of routes and flight scheduling, guaranteeing minimal expenses and maximum utilization of capacity. Our approach involves creating a model to enhance the efficiency of the aviation network.

Development of a Cyber-Physical Environment for Connected Autonomous Vehicles – (2024-present)

Angshuman Baruah (Civil), under the supervision of Archak Mittal Abstract:- Connected and Autonomous Vehicles (CAVs) promises safe and efficient mobility. Realizing this potential in complex, non-lane-based traffic like India requires context-specific testing. This research presents a small-scale Cyber-Physical Testbed to rigorously design, test, and validate CAV prototypes against these challenging, real-world dynamics, accelerating development for the Indian market.

Cognitive Workload Assessment and Rating system (CogWAR Sys) for Freight Vehicle Drivers and Warehouse workers – (2024-present)

Kanjariya Karshanbhai Mavjibhai (Civil), under the supervision of Avijit Maji Abstract:- The primary aim of this project is to develop a suitable system for assessing the cognitive workload from physiological measurements and subjective ratings (i.e., CogWAR Sys) of freight vehicle drivers and warehouse workers.

Multimodal Generative AI for Immersive Technology – (2025-present)

Rohit Nair (IEOR), under the supervision of P. Balamurugan Abstract:- We will aim to focus on a specific operations task and utilize generative AI tools to create all the required components and functionalities for that particular task.

Statistical arbitrage in freight markets – (2025-present)

Bimal H M (SJMSOM), under the supervision of Sudeep R. Bapat Abstract:- Freight markets are characterized by high price volatility due to seasonality, demand fluctuations, geopolitical news and events, supply chain disruptions etc. Traditional pricing models rely on historical trends and deterministic forecasts, which fail to capture short-term arbitrage opportunities. The idea is to develop a statistical arbitrage framework for freight markets, using advanced time-series modeling, cointegration analysis, and machine learning to identify mispriced freight rates and optimize contract structures.

Developing Omnidirectional Mobile Robots for Narrow Aisle Spaces of Warehouses and Manufacturing Facilities – (2025-present)

Aritra Das (BSBE), under the supervision of Ambarish Kunwar Abstract:- The constrained spaces in narrow aisles of warehouses and manufacturing facilities limit the use of mobile robots. Utilizing Mecanum wheels allows mobile robots to move smoothly in any direction without needing to turn, enhancing manoeuvrability and efficiency in constrained environments. However, the sizeable Mecanum wheel and the individual motor assembly required for independent control restrict its use in mobile robot designs for maneuvering in narrow spaces. The aim of this project is to develop different designs of omnidirectional robots and analyses their kinematic requirements. Numerical simulation and experimental tests will be conducted to established the developed omnidirectional robotic platform’s operational efficiency to navigate tight spaces, make sharp turns, and carry various payloads. The proposed project offers a practical solution for enhancing the performance of automated guided vehicles (AGVs) and robotic systems in space-limited settings.

Data-Driven Logistics: A Predictive Analytics and Multi- Modal Sensing Approach – (2025-present)

Tisha Ghosh (IEOR), under the supervision of Saurabh Jain Abstract:- This project aims to develop an advanced Decision Support System (DSS) to address the growing challenges in logistics sector, including operational inefficiencies, cost optimization, and the integration of diverse real-time data. Existing solutions struggle to integrate diverse data sources, manage real-time information effectively, and offer scalable, adaptable solutions for both strategic and tactical decisions. This project will utilize indigenous digitalization strategies and a minimalistic, high-impact sensory framework to streamline data collection and enhance decision-making. By employing a multi-level, multi-scale simulation approach, the DSS will model aspects of both high-level logistics operations such as network flows, hub-and-spoke dynamics, and vehicle routing optimization, as well as micro-level activities like package sorting, last-mile delivery, and processing times at distribution centers. The system will integrate real-time multimodal data from sources including GPS, RFID, load sensors, accelerometers, external data such as traffic and weather information, and others. Leveraging AI-driven prescriptive analytics and adaptive machine learning, the DSS will offer proactive decision-making capabilities, fleet optimization, warehouse efficiency, and predictive risk mitigation. An interactive and comprehensive dashboard will provide actionable insights, predictive alerts, and cost-optimized logistics strategies, ensuring resilient and adaptive supply chain operations.

Modelling Muli-modal Multi-commodity Freight Logistics for Sustainable and Efficient E-commerce Operations – (2025-present)

Deepanshu Soni (Civil), under the supervision of Tom V Mathew & Sangram Nirmale Abstract:- The rise of globalization and economic growth has resulted significant increase in the demand of transportation services. Multi-modal transportation is a natural evolution of the classical uni-modal road transportation and is a mandatory choice for long distant shipments. Optimization techniques are always the underlying tool in developing operational solutions. In addition, the increase in the number of diverse commodities that are transported every year all around the globe enhanced the interest and the usefulness of operational research methodologies, which are needed to properly manage complex transportation systems. Though there are significant research in this area, new emerging data sources and technologies generate new challenges. The goal of this research is to provide a state-of-the-art multi-modal freight transportation planning and operational framework combining different type of commodities. FedEx or similar eCommerce system is proposed as the case study.