Geoffrey De Smet

Some scientific research problems inherently suffer from an NP-complete problem. This session will explain several meta-heuristic algorithms which can handle such problems in reasonable time.

This session will also do lightning introduction of OptaPlanner, an open source Apache licensed Java library, which implements those algorithms.

Specifically, these algorithms will be explained:

• First Fit
• First Fit Decreasing
• Hill Climbing
• Tabu Search
• Simulated Annealing
• Late Acceptance

The jBPM workflow engine and the Drools rule engine and the help you extract your business logic in workflows and rules, so you don’t need to hard code it in Java. For example, suppose your application handles mortgage approval and you have a team of business experts at your disposal that can’t program. What happens if your business experts want to add a workflow action to send a mail upon each mortgage approval above 100 000 € with an applicant below age 30? How long does it take to hit production? Can the business experts easily read the changes before they hit production? With Guvnor and the jBPM designer, those business experts can write and edit the workflows and business rules themselves. They can verify them and put them in poduction.

The jBPM workflow engine and the Drools rule engine and the help you extract your business logic in workflows and rules, so you don’t need to hard code it in Java. For example, suppose your application handles mortgage approval and you have a team of business experts at your disposal that can’t program. What happens if your business experts want to add a workflow action to send a mail upon each mortgage approval above 100 000 € with an applicant below age 30? How long does it take to hit production? Can the business experts easily read the changes before they hit production? With Guvnor and the jBPM designer, those business experts can write and edit the workflows and business rules themselves. They can verify them and put them in production. Ref : http://www.jboss.org/jbpm Ref : http://www.jboss.org/guvnor

Other people: Marco Rietveld

Better planning algorithms can help save the environment, reduce costs and improve service quality. All organisations have planning problems, such as employee rostering, task scheduling, vehicle routing or bin packing. Yet, they hardly optimize those problems. Why? Because those problems are “NP-complete”: computationally very difficult and humanly impossible to optimize. Drools Planner optimizes such planning problems for normal Java programmers.

It implements a series of optimization algorithms which can optimize any planning problem: heuristics and metaheuristics with fancy names such as “First Fit Decreasing”, “Tabu Search”, “Simulated Annealing”, … The input and output are plain old java beans (POJO’s), so it’s easy to integrate with JPA, JDBC, JSF, ... The constraints are expressed as Drools Expert rules, so adding extra constraints is object-orientated and scalable. And it’s easy to get a Planner project started with the detailed reference manual and many examples. In this presentation, Geoffrey De Smet (Drools Planner lead) will explain the nature of planning problems, go through an entire Drools Planner example, show some demo’s and compare the results of different optimization algorithms. Ref : http://www.jboss.org/drools/drools-planner.html