Category Archives: Decision Optimization

Decision Optimization Technologies

Success Stories of Decision Optimization

Posted on November 29, 2019 by decisionmanagementcommunity

From lifesaving discoveries, to increased efficiencies, to billions in savings realized, Operations Research (OR) and Analytics are making significant impact every day by optimizing real-world decisions. See  success stories described at the INFORMS website and IBM.

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Chaining Machine Learning and Optimization Models

Posted on October 24, 2019 by decisionmanagementcommunity

In this article Nathan Brixius describes three relationships between optimization and machine learning: Optimization as a means for doing machine learning Machine learning as a means for doing optimization Using the results of machine learning as input data for an … Continue reading →

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Bringing Analytics to the Soccer Transfer Season

Posted on June 18, 2019 by decisionmanagementcommunity

SAS posted an article that shares how they applied analytics to optimize players movements between clubs during the soccer transfer season. Every team needs to answer the ultimate question: “How should we allocate our transfer budget to maximize the benefit … Continue reading →

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2019 Survey of Software for Linear Programming

Posted on May 26, 2019 by decisionmanagementcommunity

Prof. Robert Fourer, who will be our presenter at DecisionCAMP-2019, just published the results of the 2019 Linear Programming (LP) Survey.  LP software can be very effectively used for Decision Optimization problems which deal with minimizing or maximizing business objectives subject … Continue reading →

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Optimization and Machine Learning with Excel Online

Posted on May 22, 2019 by decisionmanagementcommunity

Frontline Systems has released Analytic Solver® V2019, a SaaS (software as a service) cloud and desktop platform that offers Excel-savvy business analysts point-and-click tools to create predictive and prescriptive analytics models themselves, without needing expert data scientists or programmers. Both … Continue reading →

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CP-2019 Workshop on Progress Towards the Holy Grail

Posted on May 21, 2019 by jacobfeldman

In 1997 in the paper “In Pursuit of the Holy Grail” Prof. Gene Freuder proposed that Constraint Programming was well-positioned to pursue the Holy Grail of computer science: the user simply states the problem and the computer solves it.  Twenty … Continue reading →

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Java Solver: Modeling and Solving Decision Optimization Problems

Posted on May 16, 2019 by decisionmanagementcommunity

Dr. Jacob Feldman announced availability of a new open source product “Java Solver” that provides a very simple API for modeling and solving optimization problems in Java. This is probably the simplest API for adding optimization components to business decisioning … Continue reading →

Posted in Decision Modeling, Decision Optimization, Products, Scheduling and Resource Allocation | Leave a comment

Similarity in Machine and Human Decision Making

Posted on April 25, 2019 by decisionmanagementcommunity

In his new post “Artificial and Human Intelligence” Alain Chabrier from IBM describes decisions the members of his own  family make while packing for vacation. In simple terms Alain nicely explains how we use 3 major Decision Management techniques (Rules, … Continue reading →

Posted in Business Rules, Case Studies, Decision Making, Decision Optimization, Machine Learning | Leave a comment

Explaining Decision Optimization Recommendations

Posted on March 30, 2019 by decisionmanagementcommunity

Explainability is a hot topic. Decision models are used to trigger recommendations such as “accept” or “refuse” a loan, but they also need to explain WHY they recommended certain decisions. Alain Chabrier from IBM DecisionOptimization team uses Portfolion Allocation problem … Continue reading →

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Modeling and Solving Scheduling Problems with CP Optimizer

Posted on March 22, 2019 by decisionmanagementcommunity

The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical problem among the myriad scheduling problems studied both in academia and in industry. Philippe Laborie, a principal scientist at IBM, describes how it can easily be modeled and efficiently solved using … Continue reading →

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