Category Archives: Decision Optimization

Decision Optimization Technologies

AI is not equal to ML

Alain Chabrier from IBM Decision Optimization team in his Medium.com article argues that not only “AI != ML” pointing to the difference between Data-driven and Knowledge-driven decision models. He even claims that “Decision Optimization has more impact on the real … Continue reading

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Pragmatic Approach to Predictive Decision Automation

In this webinar Red Hat specialists explain how explainable Predictive Decisioning can help us trust AI. Their  approach combines AI/ML, decision optimization, and traditional business rules to better understand the factors that contribute to an automated decision. They use the … Continue reading

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Solving Assembly Line Balancing Problem

Philippe Laborie from IBM CPLEX team shared several models for representation and solving an optimization problem in assembly line balancing research. His solutions are shown in Python CP Optimizer and OPL: they are not only very compact but highly efficient. … Continue reading

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Covid-19 and Decision Optimization

Alex Fleischer from IBM ILOG team wrote: “First, decision optimization can help reduce inbalance and help us live with Covid-19. Second, Optimization can help us look for a vaccine or a medicine. Third, Optimization will once we have some vaccine … Continue reading

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Time to deliver a decision optimization model to the business user

Alain Chabrier and Alex Fleischer from IBM Decision Optimization team published the article “Decision Optimization: Speed is not enough” “Many tend to think that performance is the holy grail, and the choice of an optimization engine is mostly based on … Continue reading

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The AI Evolution and the Challenge of Decision Automation

In his latest post Prof. Warren Powell contrasts “using AI to automate decisions to Tesla’s aggressive use of robotics to automate their assembly lines. It is important to understand the most common use of AI today (machine learning) with the … Continue reading

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Decision analytics: What is AI?

Prof. Warren B Powell from Princeton University: “The most common types of AI come in three flavors: rules, machine learning, and making decisions (“optimization”). Rules are used for guiding computers to identify patterns or make recommendations, and have to be … Continue reading

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Consuming Optimization models for Operational Decisions

During DecisionCAMP-2020, we had several presentations devoted to incorporation of Optimization Engines in Business Decision Models: 1) Developing Decision Optimization Microservices for Real-World Decision-Making Applications by Jacob Feldman; 2)  cDMN: Combining DMN with Constraint Reasoning by a KU Leuven’s team.

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Solving Previously Unsolved Optimization Problems

Mixed-integer programming (MIP) problem is arguably among the hardest classes of optimization problems. This paper describes how  21 previously unsolved MIP instances were solved using up to 80,000 cores in parallel on the Titan supercomputer. Link

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Making Decision Optimization Actionable for Business Users

IBM Decision Optimization Center (DOC) is a a brand-new platform, based on modern and open-source technologies. It embeds the “secret sauce” of DecisionBrain. The end goal of DOC 4.0 is to facilitate the process of making optimization actionable in the … Continue reading

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