The Optimization Edge

Leaders of a software platform for structured finance sought assistance in significantly upgrading its optimization features. An important component of the technology portfolio of a global financial analytics provider, the platform is used by issuers, investors, underwriters and other entities to administer, monitor and value collections of securities.

In a Gurobi webinar (view the recording here) on Data-First Optimization Development, Irv discussed the transformative benefits of pandas and best practices using the gurobipy-pandas library, and walked through an example. Following is a lightly edited excerpt from Irv’s presentation and the Q&A with practitioners from around the world.

Scheduling has for decades been a prime process to optimize. Recently at Princeton Consultants, we are seeing an increasing number of highly complex scheduling problems across industries, perhaps because costs are rising and critical resources are becoming more limited. Executives are looking to maximize efficient utilization of their workforce, equipment, vehicles, and other high-value assets.

This post was not generated by AI. If it was, you likely wouldn’t be able to tell, so you’ll just have to trust me.

Following are edited excerpts from Irv’s April 11 webinar with Taipy, https://taipy.io/, a tool that eases web development with drag-and-drop Python integration.

We created a solution that optimizes a complex scheduling process for a client organization. The tasks range in duration from several days to more than a year. There are teams of varying sizes, comprised of managers and tiers of personnel. The critical tasks in question are conducted routinely but scheduling them in a timely fashion can be very challenging for limited personnel. We developed an optimization model application to help generate schedules for available ongoing and future tasks.

Steve has recently conducted webinars for Gurobi about best practices in applied optimization. Following are excerpts from the Q&A sessions.

Following are excerpts from Steve's Jan. 25 webinar hosted by Gurobi Optimization.

The AI optimization success lifecycle—from the charter to the harvest—represents a wonderful world. However, today we will get away from the “wonderful” and talk about what really happens in the field. Often, colleagues and I see that a chasm forms between the early win and the scale-up. In the early win, a Proof of Concept (PoC) is in production at a pilot site, but the organization has been unable or unwilling to scale it across the enterprise.

Steve recently gave a guest lecture to Professor Shruti Sharma’s introductory class on optimization for graduate students at the NYU Tandon School of Engineering, in the Technology Management and Innovation Department. Following are lightly edited excerpts.

What optimization solvers, programming and modeling languages do professors, students and industry practitioners use? Do they align? At the INFORMS Annual Meeting in October in Phoenix, Princeton Consultants asked attendees to see what has changed since 2017, the last time we conducted the survey.

Following are lightly edited excerpts from Steve’s recent presentation in a Gurobi Optimization webinar. Watch the recording here.

If optimization is so powerful, why do top executives need convincing to invest in it? Well, they are constantly pitched, by internal colleagues and external advisors, to spend money in many different areas. They learn to say no to anything that isn’t focused on accomplishing their business strategy.