Java Solver is an open source product that provides a minimalistic, simple-to-use Java API for modeling and solving optimization problems. It’s freely available from JavaSolver.com. Download the product, try examples, and use it for your decision optimization problems. Continue reading
DMCommunity Sep-2018 Challenge “Balanced Assignment” gives an example of a complex business problem with a serious optimization component. This problem deals with the assignment of people to different project groups. Usually, such problems require deep knowledge of optimization techniques. My interest was to build a decision model for this problem and to investigate what can be done by business people and where the involvement of optimization experts is necessary. So, I attempted to use a business-friendly approach to represent and to solve this complex problem. It was not a simple journey, and this article describes what I did successfully and where I failed. Link
I provided two solutions for DMCommunity.org Challenge “Vacation Days Advanced”. So far, all decision models submitted as solutions for the old Jan-2016 Challenge and the new challenge were “method-based” meaning they describe exactly how to assign extra days while avoiding possible conflicts. I tried to apply a new “model-based” approach that concentrates on “what” instead of “how” and finds not just one of many possible solutions but the optimal one. The first decision model consists of clearly separated two parts: 1) Business Part presented in Excel decision tables; 2) Technical Part presented in Java using the JSR-331 standard. The second decision model is an attempt to present both parts completely in DMN-like decision tables using Excel only (the supporting Java code is hidden in new Excel templates). You may analyze both solutions here. I’d appreciate comments and suggestions for improvement.
In Aug-2018 Prof. Robert Fourer gave a tutorial “Model-based Optimization“, in which he compares two essentially different approaches to modeling optimization problem: “model-based” vs. “method-based”. He is using a relatively complex “Balanced Assignment” problem to demonstrate his points. While Fourer’s tutorial deals with optimization, I believe the same arguments are directly related to Decision Modeling that so far mainly remains method-based. During DecisionCAMP-2018 we had interesting (and sometimes hot) discussions about these two approaches and in my closing remarks I described the major differences between them as follows: Continue reading
The July’s challenge “Zoo, Buses, and Kids” deals with a very simple optimization problem:
“300 kids need to travel to the London zoo. The school may rent 40 seats and 30 seats buses for 500£ and 400£. How many buses of each to minimize cost?”
Naturally, a constraint solver nicely and easily solves this and more complex constraint satisfaction problems as shown in Philippe Laborie’s solution. When today I saw a pure SQL solution provided by Damir Sudarevic, I thought that it’s time to model this problem as a business decision model. It would not be as compact as provided solutions, but it should be oriented to business users. Continue reading
When I learned that the famous Prof. Raymond Smullyan passed away this February at the age of 97, I felt grateful to the man whose books and puzzles my friends and I enjoyed reading as young programmers many years ago. Later on we shared them with our children. I wanted somehow to mark this event and decided to buy his book “The Chess Mysteries of Sherlock Holmes” to read on vacation. Ten days ago I started to read the book during my flight from Newark to Jamaica and… haven’t even noticed as we landed. Continue reading
One of the largest Japanese telecom company is successfully using OpenRules being integrated with a BPM product produced by our partner Intra-Mart. Last month this customer asked us to help with development of a custom scheduler capable to schedule thousands of cable construction operations subject to various precedence and exclusivity constraints. Continue reading
People frequently assume that a good, consistent decision model should produce one and only one decision (solution) for any valid input. However, in real-world applications we frequently deal with situations when a decision model may produce multiple feasible solutions for the same input. And it does not mean that the decision model is incomplete – simply a user may choose the most appropriate solution among all produced decisions. It can be done interactively or by adding more rules. I’ve already provided an example “Monkey Business Analyzed” that describes how to deal with multiple decisions using OpenRules What-If Analyzer. In this post I will describe another decision model that also shows how to choose an optimal decision among multiple feasible decisions. Continue reading
“The Sleep of Reason Produces Monsters”, Francisco Goya
Defeasible Logic and Business Rules with Probabilities
Modern rules and decisions management systems provide effective mechanisms for development of good decision models. However, building real-world decision models people always face complex issues related to diagnostic and resolution of rule conflicts. Some systems can effectively verify decision model consistency and diagnose rule conflicts. But there are no practically used Business Rules (BR) products that claim that they may automatically resolve rule conflicts.
In the Part 1 of this series I described how end users can represent their rules in single-hit and multi-hit decision tables while avoiding rule conflicts. But is it possible to automatically resolve rule conflicts? I will discuss this problem below. Continue reading
The integration of different decision making techniques finally is finding its home under the roof of the Decision Management movement. I am glad that an integrated Constraint Programming (CP), Business Rules (BR), and Machine Learning (ML) approach is gaining in popularity as well. An interesting workshop “CoCoMiLe 2013 – Continue reading
Practical people know that one of the most important property of a business rule is its ability to be violated. Here are a few examples: Continue reading
On a long flight back to the US I had a few hours to kill. So, I decided to implement one of my favorite modeling tests that I used to give to my students and they always enjoyed it. This time I wanted to try it myself with the newest OpenRules Decision Modeling facilities (see Rule Solver).
Virtual Chess Tournament
Three world champions Fischer, Kasparov, and Karpov played in a virtual chess tournament. Each player played 7 games against two other opponents. Each player received 2 points for a victory, 1 for a draw, and 0 for a loss. We know that Kasparov, known as the most aggressive player, won the most games. Karpov, known as the best defensive player, lost the least games. And Fischer, of course, won the tournament. Continue reading
The famous RETE algorithm was invented by Dr. Charles Forgy more than 30 years ago and it still remains the foundation for most implementations of inferential rule engines. Recently Carole-Ann asked the question: why after all these years there were no practical alternatives to RETE? Continue reading
“Reality is built in wonderful simplicity”, Eliyahu Goldratt “The Choice”
Scheduling and Resource Allocation are traditionally considered as very complex business problems. They are usually out of reach for the most rule engines. I personally learned how to deal with these complex problems during my real-world consulting practice by applying a great product called “ILOG Scheduler” written by Claude LePape and Jean-Francois Puget 20 years ago. I’ve just googled the product name and got this User Manual that has over 600 pages with a lot of C++ code. I used to teach ILOG Solver/Scheduler courses and will reuse some examples borrowed from them. Continue reading
The latest rules conferences RulesFest-2011, BBC-2011, and RuleML-2011 were really great events in general and for OpenRules in particular. We announced a new constraint-based Rule Engine that is the first alternative to Rete-based implementations of a real inferencial rule engine. Continue reading
OpenRules has announced that its redesigned Rule Solver™ is now JSR-331 compliant. Rule Solver™ is a long time available optimization component of OpenRules® BRMS that allows users to define and solve constraint satisfaction and optimization problems within a business rules environment. Continue reading