I’ve implemented a new example in drools-solver: patient admission scheduling. In this problem, we have to assign each patient (that will come to the hospital) a bed for each night that the patient will stay in the hospital. The problem is defined on this webpage by the Kaho Sint-Lieven IT research group and the testRead more →

# KIE Community

Knowledge is everything

## Benchmarking drools-solver configurations

One of the little known gems in Drools Solver is the Benchmarker utility. Until now, it wasn’t documented in the manual and few people knew about it. The Benchmarker allows you to play out different solver configurations against each other, so you can determine the best one for your problem domain. It’s pretty easy toRead more →

## Drools Solver article on DZone’s Javalobby

I’ve posted an overview article about Drools Solver on DZone’s Javalobby:Solving planning problems: Introducing Drools Solver

## Knowledge-based Locomotive Planning for the Swedish Railway

Knowledge-based Locomotive Planning for the Swedish Railway The link above is the master thesis of Dr. Volker Scholz written in 1998. It discusses the knowledge-based approach for locomotive planning. The algorithms could be adopted and applied to logistic planning in general. Drools Solver and the upcoming Drools Fusion are well suited for this problem domain.

## Solving the Examination problem part 2: score function

This is the second part in a blog series about drools-solver and the Examination problem. If you haven’t done so, read Solving the Examination problem: Domain diagram first. Each possible solution has a score. Before we try to find the best solution, we need a way to calculate the score of a solution. And that’sRead more →

## Solving the Examination problem part 1: Domain diagram

The examination problem is one of the examples in drools-solver. It has a number of exams, students, periods and rooms. A student is enrolled into multiple exams. Each exam needs to be scheduled into a period and into a room. Multiple exams can share the same room during the same period. There are a numberRead more →

## Drools solver Javapolis 2007 slides

The BOF was a success and as promised, I ‘ve posted the slides online: Javapolis 2007 slides and speaker notes (ODF) Javapolis 2007 slides and speaker notes (PDF) People asked a bunch of interesting questions afterwards.One question I felt I left a bit unanswered: how do you weigh your constraints in DRL? So, here’s aRead more →

## Drools solver @ Javapolis

I ‘ll hold a Drools Solver BOF at Javapolis 2007 on Tuesday December 11th at 20:00. You’re all invited 🙂Take a look at the schedule here.Take a look the contents of the BOF here. The drools solver manual has also been expanded with more info about the examples:Take a look at the updated manual here.Read more →

## Drools solver in a nutshell

Drools-solver combines a search algorithm with the power of the drools rule engine to solve planning problems, such as: Employee shift rostering Freight routing Supply sorting Lesson scheduling Exam scheduling The traveling salesmen problem The traveling tournament problem Drools-solver supports several search algorithms, such as simple local search, tabu search and simulated annealing. You canRead more →

## Drools Solver

Geoffrey De Smet has been busy working on the Drools Solver module, which will hopefully be part of the next major Drools release. Drools solver aims to efficiently solve search based problems finding a valid solution from large search areas. It currently provides implementations for Tabu, simulated annealing and Local search. I personally hope toRead more →