Business optimization software helps organizations make the best use of available resources —people, equipment, time, etc.—by determining optimal solutions to planning problems.
Common organizational tasks like scheduling, rostering, vehicle routing, and packing can be complex. They need to be solved quickly under circumstances when time, money and other resources are limited.
Business optimization software can algorithmically resolve business planning problems with precision and speed. Because these problems are rarely static, solutions can change on the fly. When resource counts change and gaps appear, business optimization software adjusts to make the optimal decision, fast.
About constraints and solutions
Business optimization tackles problems by weighing various constraints and comparing the relative strengths of each possible solution.
A constraint is a restriction on the use of a resource (people, equipment, etc.) that a solution to a planning problem must or should satisfy. Some real-world constraints might include deadlines to be met, regulatory limits on working hours, availability of needed equipment or venues, safety requirements, and so on).
Constraints can be positive or negative, and hard or soft. A positive constraint is something that must or should happen, while a negative constraint is something that must not or should not happen. A hard constraint cannot be broken. For example, a truck can’t be in two places at the same time. A soft constraint is desirable, but not strictly required. A desire to make deliveries as fast as possible would be a soft constraint.
Based on the constraints, a business optimization system calculates a numerical score for each solution and compares a set of possible solutions to determine which one is the best.
Each solution can fall into one of three categories: Possible, feasible, and optimal. A possible solution is any solution, even one that breaks constraints. There can be an incredibly large number of possible solutions. A feasible solution is one that doesn’t break any negative hard constraints. Sometimes there aren’t any feasible solutions. Finally, the optimal solution is a solution or solutions with the highest score. (It’s possible to have an optimal solution that isn’t feasible.)
Examples of business optimization use cases
Scheduling: As the size of a workforce grows, so do the challenges in scheduling. A hospital, assembly plant, or call center can employ thousands of people with different skills. Business optimization systems can help determine who should work when, optimized for skill requirements, the number of employees required at a time, the number of hours in a day each employee can work, time off schedules, desired time slots, and fairness.
Vehicle routing: From package delivery to public transportation to repair companies, vehicle routing can come with a long list of constraints. Time windows, job locations, employee skills, and employee locations are a few of them. Business optimization can take these inputs and produce an optimal vehicle routing plan in seconds or minutes.
Agenda scheduling: Planning schedules can encompass many constraints, often coordinating both people and physical locations with limited availability. Business optimization can help find the optimal times for meetings, maintenance jobs, sports events, or school classes.
Any complex constrained problem: Organizations put business optimization to use for balancing investment portfolios, packing containers, minimizing waste while cutting materials and more.
Why choose Red Hat for optimization?
Red Hat® Decision Manager includes an optimization engine derived from the OptaPlanner open source project. The Red Hat build of OptaPlanner is a lightweight, embeddable planning engine that can quickly find good solutions to problems that are otherwise extremely difficult, time-consuming, and expensive to solve.
Teams can use a variety of out-of-the-box-provided algorithms, allowing them to experiment and choose the right algorithm to achieve optimal results, even with no specific knowledge of optimization techniques.
Red Hat’s business optimization solution benefits from the underlying capabilities of Decision Manager’s rules engine, which conveys scalability benefits compared to traditional planning solutions that do not use rules-based technology.