Timothy

Timothy

Experienced AI/ML professional

Tutorials

Geospatial Optimization

June 17, 2025

This tutorial provides a Linear Programming (LP) approach to determine where stationary sensors should be placed to maximize coverage in a defined area while respecting constraints on sensor count and overlapping. The optimization problem assigns a binary variable to each potential location/configuration pairing. The objective minimizes the number of sensors while discouraging redundant overlap. Constraints ensure required coverage, limit overlap, and allow no more than one sensor per spot.

Textual Inversion for Stable Diffusion

February 24, 2023

This tutorial provides a comprehensive guide on using Textual Inversion with the Stable Diffusion model to create personalized embeddings. It covers the significance of preparing diverse and high-quality training data, the process of creating and training an embedding, and the intricacies of generating images that reflect the trained concept accurately. The author shares practical insights into overcoming challenges such as data preparation, the training process, and adjusting the weight of embeddings to achieve desired results. This resource is valuable for anyone looking to tailor generative models to recognize and generate images of specific objects, faces, or styles.

Using Linear Programming for Route Planning and Job Scheduling

February 16, 2022

Efficiently managing travel and job scheduling for multiple engineers across various locations presents a significant operational challenge. We use a Linear Programming (LP) model designed to optimise route planning and job allocation among engineering teams, aiming to minimise travel time and adhere to individual working hours constraints. Utilising variables such as travel costs, job durations, and resource capacities, we construct a mathematical framework that accommodates each engineer’s starting location and contractual obligations. Experimental results, visualised through Gantt charts and geographical plotting, demonstrate the model’s efficacy in reducing total travel time while ensuring equitable workload distribution. This approach not only enhances operational efficiency but also contributes to the broader field of operations research by providing a scalable solution for multi-location, multi-personnel scheduling problems.

Linear Programming for Optimal Resources Allocation

July 17, 2020

Using Linear Programming (LP) solver to allocate resources to geographical regions. This generalised example shows how to dynamically allocate engineers to local areas, while satisfying business constraints and minimising travel cost (e.g. travel time, fuel expenditurem, motor insurance, etc).