Modeling the Traveling Salesman Problem from first principles

This article continues right where <a href="https://medium.com/@carlosjuribe/plan-an-optimal-trip-for-your-next-holidays-with-the-help-of-operations-research-and-python-481b1ea38fef" rel="noopener">the article for sprint 1</a> left off. You don’t need to have read it to understand what we’ll do here, but let me give you a quick recap (feel free to jump to section 2 if you did read the previous article). In a nutshell, we laid out the common problems that tourists face when planning a trip, and we set out to build a system that can help us plan trips more effectively, speeding up decision-making, or even fully automating the schedule for any given trip. We observed that stated like that, the problem is too complex, so we decomposed it and arrived at its essential version, and we called it the minimum valuable problem. In the end, we concluded that it took the form of the <a href="https://en.wikipedia.org/wiki/Travelling_salesman_problem" rel="noopener ugc nofollow" target="_blank">Traveling Salesman Problem</a> (TSP), where the “cities” that the proverbial salesman must visit correspond, in our version, to the “sites of interest” in a city that a tourist desires to visit. <a href="https://towardsdatascience.com/modeling-the-traveling-salesman-problem-from-first-principles-bd6530c9c07">Visit Now</a>