Current Fellows Projects
| Novel Reinforcement Learning based Algorithms and Generative AI based Mixed Reality Toolkit for Online Convex and Non-convex 3d Packing Problem – (2024-present) |
| Rahul Kumar Gop (IEOR), under the supervision of P. Balamurugan |
| Digitalization of Supply Chains and Operations – (2024-present) |
| Saimadhav S (IEOR), under the supervision of Jayendran Venkateswaran |
| Smart Indoor Logistics – Enhancing Efficiency, Safety, and Well-Being – (2025-present) |
| Siddhesh Madkaikar (IEOR), under the supervision of Saurabh Jain |
Past Fellows Projects
| Pricing Contracts in Air Cargo Revenue Management – (2024-2025) |
| Pulkit Khandelwal (IEOR), under the supervision of Ashutosh Mahajan Abstract:- This report investigates pricing and capacity allocation strategies in air cargo logistics, focusing on interactions between carriers and freight forwarders under uncertainty of demand. We analyze three models: centralized decision making, decentralized coordination without buy-back, and decentralized coordination with a buy-back mechanism. Closed-form expressions for expected revenues are derived and optimal capacity allocation strategies are explored using numerical simulations. The results show that centralized coordination produces the highest revenue, while the introduction of a buyback contract significantly reduces inefficiencies in decentralized settings, with a revenue loss below 1%. These findings offer actionable insights for contract design in logistics networks with asymmetric information. |
| Replenishment Scheduling for Combat Logistic Forces – (2024-2025) |
| Shubham Joshi (IEOR), under the supervision of Manjesh Hanawal Abstract:- The Fleet Management services conducts missions using combat ships, aircraft and submarines that operate together in mission-driven groups. These units require regular fuel replenishment, but returning to port frequently is not practical. To address this need, shuttle ships are deployed to deliver fuel at sea. This project presents a Mixed Integer Programming (MIP) model designed to generate efficient refueling schedules for these operations. The primary objectives of the model are to minimize operational costs, maintain inventory levels above safety thresholds and ensure uninterrupted mission readiness. To improve the model’s flexibility and scalability, two operational scenarios were introduced. In the first scenario, basic calculations are used to check whether combat ships can reach their assigned group using only their internal fuel. If they are found incapable, the optimization model is then applied to determine whether a feasible plan can be made using one or two shuttle ships. The second scenario assumes that combat groups remain stationary and the model is used to generate a full refueling schedule over the entire planning horizon. A user-friendly software interface has been developed to allow Fleet Management services planners to input mission data, configure ports and assets and visualize results through interactive maps and animations. The system successfully runs on the data and generates feasible, cost-effective and interpretable schedules, demonstrating its strength as a practical and reliable tool for mission planning and Fleet Management services logistics. |
| Digital Twin Simulation – (2024-2025) |
| Arun Singh (IEOR), under the supervision of Jayendran Venkateswaran Abstract:- The simulation and control of distributed systems in real-time environments are increasingly essential in domains such as transportation, automation, and logistics. This thesis, titled Distributed Simulation Modeling and Analysis for Complex Systems presents the implementation of federated communication and synchronization between simulation models using the High-Level Architecture (HLA) standard. A digital twin serves as a virtual representation of a physical system, enabling simulation, monitoring, and optimization of real-world operations.To explore the integration of digital twins in distributed environments, two distinct simulation setups were developed. The first setup connects a simulation software (AnyLogic) with an external Java-based application via PITCH pRTI, enabling real-time interaction and dynamic parameter control through HLA-compliant messaging. The second setup models end-to-end supply chain behavior using two AnyLogic simulations running as separate federates over a local area network. These federates represent different layers of the supply chain and coordinate through time-synchronized interaction over RTI. In both configurations, the HLA standard facilitates modularity, interoperability, and synchronized time advancement. The first setup demonstrates external control of simulation parameters and event-triggered communication, while the second setup models inter-tier logistics coordination between retailers, distributors, and manufacturers. Each federate executes autonomously yet progresses in logical time steps, ensuring accurate message ordering and causality preservation. A practical application is demonstrated through a supply chain case study where manufacturers, distributors, and retailers are each modeled as autonomous simulation agents. This approach supports real-time monitoring, predictive analytics, and distributed decision-making. Moreover, the federated architecture ensures data confidentiality and stakeholder independence while enabling collaborative simulation. The results validate the feasibility of HLA-based distributed simulation as a scalable framework for building digital twin systems in supply chain environments. |
| Deep Learning for Real Time Visual Inspection of Pharma Pills on Conveyor Belt using Computer Vision – (2024-2025) |
| Mayur Dhanawade (IEOR), under the supervision of P. Balamurugan Abstract:- Defect detection in pharmaceutical pills is critical to ensure product quality and patient safety, as flaws in color, shape, surface texture, or size may signal manufacturing issues. Although existing defect detection systems are available, they are often expensive customizable, and efficient computer vision-based tool for detecting and localizing defects in pills using deep learning techniques. The proposed solution processes images of pills to distinguish defective units from non-defective ones, with a strong emphasis on minimizing inference time. The approach is divided nto two parts: defect detection in the side view and in the top/bottom view of pills. For the side view, the initial segmentation strategy involves a two-stage pipeline using the Segment Anything Model (SAM) followed by a custom-trained U-Net model. To reduce computational overhead, this is later replaced by a single-stage segmentation approach using a YOLOv8-seg model fine-tuned on a custom-labeled dataset. This modification leads to a significant reduction in processing time—by approximately 65%. After segmenting the region of interest, defect detection is carried out using a series of image processing operations, with Gabor filters playing a central role. The system also includes adjustable parameters, allowing users to control the sensitivity or scale of defect detection as per their inspection requirements. The result is a fast, flexible, and affordable defect detection pipeline suitable for real-time pill inspection in pharmaceutical settings. For defect detection in the top/bottom view of pills, traditional image processing methods are inadequate due to the presence of engravings, which makes it difficult to isolate actual defects. To overcome this, supervised learning models are explored, but they require a labeled dataset, which is not readily available to us. A semi-automatic annotation tool is therefore developed. This tool first segments and removes the engraving and edge regions using a custom U-Net model trained on a dataset generated via rule-based techniques. Defects in the remaining pill surface are then detected using a Gabor filter-based method, similar to the side view approach. The annotated dataset generated through this pipeline is then used to train a YOLO model, which effectively detects defects of various sizes and shows promising generalization. Additionally, a contextual comparison-based method is introduced to enhance robustness across different pill types by comparing each test image with a defect-free reference of the same pill type. We have developed a fully functional tool with a user interface, where the user can upload pill images from any view (side or top/bottom). If defects are present, the tool detects and localizes them using bounding boxes; if not, it indicates that the pill is defect-free. The user also has control over customizable parameters to adjust the scale of defect detection. All functionalities are seamlessly integrated into the interface. |
| Developing Optimal Warehouse Policies and Planned Interventions for Enhanced Sustainability using PetriNets – (2024-2025) |
| Deepak Naik (IEOR), under the supervision of Priyank Sinha |
| Opportunity of Small Batch Movement of Freight on Indian Rail Networks – (2024-2025) |
| Rohit Kumar (IEOR), under the supervision of Narayan Rangaraj |