Practice and Research Optimization Environment in Python (PyPROE)

Abstract

Practice and Research Optimization Environment in Python (PyPROE) is a GUI-based, integrated framework designed to improve the user experience in both learning and research on engineering design optimization. Traditional optimization programs require either coding or creating complex input files, and often involve a variety of applications in sequence to arrive at the solution, which presents a steep learning curve. PyPROE addresses these challenges by providing an intuitive, user-friendly Graphical User Interface (GUI) that integrates key steps in design optimization into a seamless workflow through a single application. This integration reduces the potential for user error, lowers the barriers to entry for learners, and allows students and researchers to focus on core concepts rather than software intricacies. PyPROE’s human-centered design simplifies the learning experience and enhances productivity by automating data transfers between function modules. This automation allows users to dedicate more time to solving engineering problems rather than dealing with disjointed tools. Benchmarking and user surveys demonstrate that PyPROE offers significant usability improvements, making complex engineering optimization accessible to a broader audience.