Rasman M Swargo

I am working as a Graduate Research Assistant under the supervision of Dr. Md Arifuzzaman at Missouri University of Science and Technology, one of the top engineering schools in the Midwest. My primary goal is to conduct research on file transfer optimization and develop relevant systems. My work so far includes the following accomplishments:

• Fine-tuned the IBM Granite Time Series Foundation model to create a scalable solution for active probing in high-speed networks, achieving approximately 2x lower error rates compared to the state-of-the-art FastProb. Poster accepted at BigData 2024.
• Working on a network file-transfer simulator aimed at training reinforcement learning agents in a controlled environment prior to real-world deployment.
• Exploring Proximal Policy Optimization (PPO) models to predict the optimal number of threads for real-time file transfer, focusing on reducing idle resource allocation and improving system efficiency.

I worked as a Software Engineer - Level 2 in the Logistics and Dispatcher team at Chaldal, a startup based in Bangladesh & California, and the biggest online grocery platform in Bangladesh. My primary goal was to achieve full automation of routing operations, eliminating the need for manual interventions. The system uses a Distributed architecture with .NET (C#/F#) for backend, React for frontend and Python for algorithm and ML services, all managed on our in-house infrastructure. I was involved in designing and building backend systems, research and development of algorithms to optimize delivery processes and also some frontend development for web and mobile applications. Below I will describe some of the projects that I have worked on.

Logistics Routing Algorithm

• Goal: The goal of this project is to assign routes of multiple shipments to a delivery person so that the delivery person can deliver all the orders in the same go. In the previous system, the routes were assigned manually by the dispatcher in charge. The manual assignment is effective for low order volumes, but as the order volume increases, the manual assignment becomes inefficient.
  
• Challenge: The main challenge of this project is that the problem is an NP-Hard problem, and the number of possible routes grows exponentially with the number of shipments. The off-the-shelf algorithms like Google OR-Tools and Gurobi Optimizer were not able to handle the problem. Also, some constraints were very specific to our business and could not be modeled using the off-the-shelf algorithms.
  
• Our Work:
  • Algorithm: We developed a new routing algorithm to fully automate the route assignment process. The algorithm takes all the shipments that need to be delivered, the location of the shipments, the time window of the shipments, the weights and volumes of the shipments, the time when each of the delivery persons will be available at the hub, the vehicles that are available, and the vehicle capacity, and outputs a list of routes. A route consists of an ordered list of shipments and the delivery person who will deliver the shipments. The main objective of the algorithm is to deliver the shipments on time and to minimize the total travel time of all the delivery persons. We used Clustering, TSP, Simulated Annealing, Tabu Search, and various neighbor-generating heuristics to achieve this.
    
  • Backend: The backend system is responsible for calling the algorithm, tracking the shipment states, such as whether it's routed or not, and the estimated delivery time if it's routed, etc. It also tracks the delivery person states, like their location and the time when they will be available at the hub. The backend system keeps track of the routes assigned to the delivery persons and the shipments assigned to the routes. It updates the states of the shipments, delivery persons, and vehicles as the delivery persons deliver the shipments.
    
• My Contribution: I was responsible for designing the algorithm, implementing it, and create a simulator for testing the performance.
• Demonstration: Here is a simplified demonstration of the algorithm. All the things shown here are dummy data. The images are from the simulator that I developed to test and visualize the algorithm results, and also to simulate a total warehouse environment.

  

  

  

  

  
  In the Fig 1, there are 10 shipments shown in black and the warehouse/hub shown in blue. There are 3 vehicles available: a cycle, a bike, and a van. The corresponding routes for the vehicles are shown in the next 3 images. The colors in the shipments represent whether the shipment is on time or not. Shipments colored in yellow are early, shipments colored in green are on time, and shipments colored in red are late.

Others

• Driver Incentive System: Developed an algorithm that takes the performance of the drivers, the predicted duration of all the deliveries in a day, and the actual duration of the deliveries that the driver completed in that day, and assigns a score to each driver. The incentive system uses the score to give incentives to the drivers.
• Driver Idle Time Analysis: Used Superset and Snowflake to analyze the idle time of the drivers and find the reasons for the idle time. Suggested new shift times for each driver to reduce the idle time.
• Dish Rating and Review System: I worked for two months on the food delivery team, where I implemented a dish rating and review system. This system allows customers to rate and review the dishes they have ordered.

Judicious Parallelism For File Transfers using Reinforcement Learning

Co-authors: Dr. Md Arifuzzaman (Supervisor)

Efficiently transferring large datasets in high-performance networks remains a challenging task due to system overheads and unfair resource allocation. To address this, we are exploring reinforcement learning techniques to dynamically optimize file transfer operations. Using reinforcement learning, the system learns optimal concurrency levels for reading, transferring, and writing operations by interacting with the environment.

Deploy-Efficient and Fast Network Probing with Time-Series Foundation Models

Co-authors: Dr. Md Arifuzzaman (Supervisor)

Monitoring network performance efficiently is crucial for high-speed networks used in modern scientific research. Traditional methods for estimating data transfer speeds are either slow or require extensive data for training, making them inefficient for dynamic networks. To address these challenges, we developed npLLM, a framework that uses time-series foundation models to predict network throughput more accurately and with minimal data.

Attention-Driven Multi-Modal Fusion: Enhancing Sign Language Recognition and Translation

Co-authors: Zaber Ibn Abdul Hakim, Dr. Muhammad Abdullah Adnan (Supervisor)

Sign language recognition and translation are challenging tasks due to the complexity of body movements and expressions involved. To address these, we explored lightweight cross-attention mechanisms that enhance the integration of visual (RGB) and motion (optical flow) data. Unlike traditional methods that are computationally heavy or merge features inefficiently, our approach uses a simple plugin module that improves the understanding of movement without adding significant computational cost.

Published at: International Conference on Image Processing, ICIP 2024 [Link]