About Geoffrey De Smet

Geoffrey De Smet (Red Hat) is the lead and founder of OptaPlanner. Before joining Red Hat in 2010, he was formerly employed as a Java consultant, an A.I. researcher and an enterprise application project lead. He has contributed to many open source projects (such as drools, jbpm, pressgang, spring-richclient, several maven plugins, weld, arquillian, ...). Since he started OptaPlanner in 2006, he’s been passionately addicted to planning optimization.

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Red Hat JBoss BRMS and BPMS Rich Client Framework demonstrating Polyglot Integration with GWT/Errai/UberFire and AngularJS

Last week I published a blog highlighting a presentation I gave showing our rich client platform that has resulted from the work we have done within the BRMS and BPMS platforms, the ...
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Prototyping an enterprise webapp at Devoxx Hackergarten

For the 10th year in a row, I attended DevoxxBe. It’s my favorite Java conference, but the talk schedule isn’t always optimal: sometimes I want to see 2 great talks at the same ...
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OptaPlanner – Open benchmarks for the win

Recently, there was some commotion on Twitter because a competitor heavily restricts publicising benchmarks of their Solver as part of their license. That might seem harsh, but I can ...
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OptaPlanner – Vehicle routing with real road distances

In the real world, vehicles in a Vehicle Routing Problem (VRP) have to follow the roads: they can’t travel in a straight line from customer to customer. Most VRP research papers and ...
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Pluggable Knowledge with Custom Assemblers, Weavers and Runtimes

As part of the Bayesian work I’ve refactored much of Kie to have clean extension points. I wanted to make sure that all the working parts for a Bayesian system could be done, ...
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Cheating on the N Queens benchmark

Many Solver distributions include an N Queens example, in which n queens need to be placed on a n*n sized chessboard, with no attack opportunities. So when you’re looking for the ...
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How much faster is Java 8?

Java SE 8 was released yesterday. Traditionally, every new major JRE version comes with a free performance boost. Do we get another free lunch? And how big is the gain this time? Let’s ...
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Can MapReduce solve planning problems?

To solve a planning or optimization problem, some solvers tend to scale out poorly: As the problem has more variables and more constraints, they use a lot more RAM memory and CPU power. ...
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Drools 6 Performance with the PHREAK Algorithm

Drools 6 introduces a new lazy matching algorithm. The details of that algorithm have been covered in two previous blogs:R.I.P. RETE time to get PHREAKY PHREAK Stack Based Evaluations ...
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Drools : PHREAK Stack Based Evaluations and Backward Chaining

A while back I wrote a blog on our new algorithm: http://blog.athico.com/2013/11/rip-rete-time-to-get-phreaky.html Someone asked me about the new stack based system, and how backward ...
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