Sequential Optimization

Meinolf Sellmann: Sequential optimization problems are frequent in business. There are many ways to deal with them, but two methods are particularly prevalent: deterministic look-ahead and stochastic look-ahead optimization.

Watch our instant premiere on Tuesday, Feb 10, at Noon EST, when we present experimental results on six different sequential optimization problems:

1. Daily Task Assignment.
2. Weekly Production Scheduling
3. Weekly Pricing and Distribution
4. Weekly Pricing and Ordering
5. Online Routing
6. Weekly Replenishment

If you are serious about optimization that matters, you will not want to miss this. Calendar invite: https://lnkd.in/eByn7pri

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Building Business Capability 2026

April 20-23, 2026 – Toronto, Canada
Marriott Downtown CF Toronto Eaton Centre

Link

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IBM ODM Webinar on Jan 29

Webinar: Generating ODM Decision Services from the Decision Assistant in IBM Decision Intelligence on Jan 29, 2026 from 11:00 AM to 12:00 PM (ET)

Key IBM ODM developers Stephane Mery and Pierre Feillet will explain “how easy it is to come from a policy expressed in natural language to a well-formed ODM project in minutes, dramatically improving the time to value for customers.” Link

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Coming soon: “The Decision Factory”

“This book has the potential to do for automated decision-making in business that The Goal (Goldratt) did for supply chain management. The ease with which DeJans and Elam communicate these fundamentally new ideas for making decisions under uncertainty for complex business problems is likely to do more to democratize “stochastic optimization” than anything that has ever been written.” Warren B. Powell

https://www.bitbrosdata.com/resources/the-decision-factory

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MS OptiMind: From Problem Description to Solution

Microsoft Research has just released OptiMind, “a small language model designed to convert business problems described in natural language into the mathematical formulations needed by optimization software. Built on a 20-billion parameter model, OptiMind is compact by today’s standards yet matches the performance of larger, more complex systems. Its modest size means it can run locally on users’ devices, enabling fast iteration while keeping sensitive business data on users’ devices rather than transmitting it to external servers.

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Posted in Artificial Intelligence, Decision Optimization, solvers | 1 Comment

Gartner To Publish New Magic Quadrant for Decision Intelligence Platforms Next Week

David Pidsley just announced: “Last summer, I shared that Gartner Says AI Ready For Decision Intelligence Market. Today, I’m pleased to announce that we have a new Magic Quadrant scheduled (3 December 2025), which will be the evolution of our Market Guide for Decision Intelligence Platforms (client login required). This will supersede the Gartner Market Guide of 18 July 2024, which will not be refreshed.Link

Published on Jan 26, 2026

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Challenge Feb-2026 “Trivial Rule”

This challenge was inspired by a post from Ron Itelman. You need to use any rules-based platform or GenAI tool to create an AI Agent that implements the following rule:

Execute a buy order if the current price is lower than yesterday’s price.

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Decidability Problem

We often assume that if we have good data and a good model, a decision can be made. That assumption quietly fails more often than we think,” – wrote Ron Itelman. “By decidability, I mean a boundary condition: A decision is decidable if the minimum semantic prerequisites required by the decision rule are grounded.

“Take something trivial: a trading rule that says ‘Buy if price today is less than price yesterday.’ The logic is simple. The model works. The data exists. But the system can still halt. Why?

When a stock-market trader at a London Stock Exchange desk asks their AI agent, “What were my trades yesterday?” The system resolves ambiguity before it can answer. Which time zone? “Yesterday” from whose perspective: the headquarters in New York, U.S.A. the trader in London, UK or their client at Shenton Way, Singapore? The AI silently decides, then returns a confident answer without exposing those variables to the user.
Link

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“Show me why this loan was approved”

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Decision Timing Mismatch

Adan DeJans Jr. shared another experience from the trenches of real-world decision-making. “The model assumes decisions are made at a clean point in time, with clean information. But reality doesn’t work that way. Data arrives late, decisions are staggered, overrides happen mid-cycle, and yesterday’s “final” plan quietly becomes today’s suggestion. If your optimization assumes a single moment of truth, it will always feel brittle in production, no matter how optimal the solution looks on paper.Link More

Posted in Decision Making, Optimization, Scheduling and Resource Allocation, Supply Chain, Uncertainty | Leave a comment