#31 Meinolf Sellmann: Decision-Making Under Uncertainty

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠.Meinolf Sellmann, computer scientist, entrepreneur, and founder/CEO of InsideOpt, joins the Decision Intelligence Lab podcast with hosts Mike Watson and Vijay Mehrotra. Meinolf built MIP solvers at Bell Labs, IBM, and GE before launching his startup, InsideOpt. The conversation starts with a 1998 story: George Nemhauser named three open challenges in operations research- timely decision support, multi-objective optimization, and decision-making under uncertainty. Nearly 30 years later, according to the 2025 NSF DECIDE workshop, the same problems are critical to national security and competitiveness.Meinolf explains why machine learning "hit a nerve" but couldn't deliver perfect forecasts, why optimization is seeing renewed interest, and how primal solvers attack highly combinatorial problems under uncertainty. He covers jettisoning dual bounds, scaling to 1,000 cores with ML-guided search operators, and beating Gurobi by a factor of 1,000 on quadratic assignment (Taillard instances). He shares competition wins (MaxSAT 2016, AI for TSP 2021 at IJCAI), a real coffee-roasting scheduling story, and three principles for better decision-making.What You'll Learn- Why George Nemhauser's 1998 challenges remain unsolved today.- The difference between primal solvers and dual solvers, and why dual bounds limit you.- Why perfect forecasts are impossible and what to do with residual uncertainty.- How machine learning guides search - Why a primal solver scales to 1,000 cores while MIP heuristics stall.- How Seeker beat Gurobi by 1,000x on quadratic assignment (Taillard instances).- Why the right tool beats raw algorithmic improvement.- Bridging the gap between a well-shaped technical problem and the business customer's real problem.- The coffee-roasting scheduling story — why MIP failed and a primal solver won.- Three rules for good decision-making- Why risk mitigation matters more than expected value (gambler's ruin, UPS fleet scenarios).Timestamps0:00 - Preview & Introduction0:52 - Meet Meinolf Sellmann, InsideOpt1:29 - The 1998 George Nemhauser story: Three OR challenges3:21 - Multi-objective optimization: the three canonical approaches and why they fail6:20 - Why renewed focus on decision-making after the AI/ML wave7:43 - Perfect forecasts are impossible: the sushi example9:50 - Solving combinatorially complex problems under uncertainty12:10 - What is a primal solver vs. a dual solver?14:16 - Technical problem vs. the business customer's real problem 16:45 - Jettisoning bounds; 1,000 cores; ML-guided search19:22 - Machine learning as counting cards in blackjack22:05 - Hardware vs. algorithms; beating Gurobi 1,000x on quadratic assignment23:43 - Why leave the big labs and start a company25:27 - MaxSAT 2016 win; the self-learning solver27:29 - Evolving view of good decision-making: three principles31:20 - Where to find InsideOpt and Seeker35:40 - The Coffee-Roasting Scheduling StoryFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestMeinolf Sellmann: ⁠https://www.linkedin.com/in/meinolf-sellmann-a349636/InsideOpt: https://insideopt.com/Connect with hostsProf. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠About the podcastThe Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.For business inquiries, email at ⁠[email protected]⁠