The Optimization Edge

Following is the first of two lightly edited excerpts from Patricia’s recent presentation at the Gurobi Decision Intelligence Summit in Las Vegas.

In most corporate IT departments you see Agile everywhere, but many of us remember when that was not the case. Agile Software Development began, or at least coalesced, in 2001 at a meeting in Snowbird, UT and the creation of the Agile Manifesto (see this short history https://www.agilealliance.org/a-short-history-of-agile/). But it took years to become popular and broadly used; when I started as an optimization practitioner and technology consultant in 2007, the dominant methodology was Waterfall, which had taken root during the 1970s (see this interactive history on LinkedIn .)

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.