Jacob Feldman
OpenRules - CTO

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Dr. Jacob Feldman is a founder and CTO of OpenRules, Inc., a NJ corporation that created and maintains the popular Open Source Business Decision Management System commonly known as "OpenRules".  He has extensive experience in
development of decision support software using Business Rules, Optimization, and
Machine Learning technologies for real-world mission-critical applications.  He has 5 granted patents in the area of Business Rules and Constraint Programming.
 
Jacob is a frequent presenter at the major decision management
events. He is also a specification lead for the JSR-331 standard.

@Jacob_OpenRules
Decision modeling practitioners frequently face situations when a decision model has to analyze multiple alternative decisions to come up with an optimal one. Traditional decision modeling approaches mainly rely on business rules and/or scorecards to determine one “good” decision and in most cases do not backtrack to consider potentially better decisions. The reason is simple:  even for relatively small decision models with 20-40 decision variables it is physically impossible to manually create rules that cover all (!) possible combinations of decision variables and to find a decision that minimizes a total cost or maximizes resource utilization.

In this presentation we describe how “Decision Optimization” utilizes off-the-shelf optimization tools to help a business user to create smarter decision models that are capable to find decisions that minimize/maximize certain optimization objectives. Instead of trying to specify all possible rules, a decision model creator uses decision tables to represent only major business constraints and relationships between different decision variables. An optimization objective may be represented as a special decision variable defined on other key variables.  A decision optimization component does the rest of work by automatically considering multiple alternatives and selecting the best one within a time limit defined by a user.  Contrary to traditional rule engines, the optimization component is specifically designed to solve constraint satisfaction and optimization problems using proven optimization techniques such as constraint and/or linear programming.  We will demonstrate decision optimization at work using two real-world use cases.