OptaPlanner

AI vs COVID-19: How Java helps nurses and doctors in this fight

AI vs COVID-19: How Java helps nurses and doctors in this fight

DevNation Tech Talks are hosted by the Red Hat technologists who create our products. These sessions include real solutions plus code and sample projects to help you get started. In this talk, you’ll learn how OptaPlanner has helped keep medical staff and patients safer with advanced planning algorithms, from Geoffrey De Smet and Edson Yanaga.

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Use these six simple steps to get started with Red Hat JBoss Business Resource Planner

Red Hat JBoss Resource Planner (part of Red Hat JBoss BRMS, the enterprise product based on the upstream OptaPlanner community project) is the leading open source constraint satisfaction solver. A constraint satisfaction solver is a solving engine build around sophisticated optimization algorithms that allows to plan for optimal use of a limited set of constrained resources.

Every organization faces scheduling problems: assign a limited set of resources, for example employees, assets, time and money, to build products or provide services. Resource Planner optimizes such planning problems to provide an optimal utilization of resources, resulting in higher productivity, less costs and higher customer satisfaction. Use cases include:

  • Vehicle Routing: What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?
  • Employee Rostering: Find an optimal way to assign employees to shifts with a set of hard and soft constraints.
  • Cloud Optimization: What is the optimal assignment of processes to cloud computing resources (CPU, memory, disk)
  • Job Scheduling: Optimise the scheduling of jobs of varying processing times on a set of machines with varying processing power, trying to minimize the makespan.
  • Bin Packing:  pack objects of different volumes into a finite number of bins or containers in a way that minimizes the number of bins used.
  • and many more.

All these problems are, so called, NP-hard problems, which implies that the time required to solve these problems using any currently known algorithm increases very quickly as the size of the problem grows (e.g. adding a destination to a vehicle routing problem, adding a shift to an employee rostering problem). This is one of the principal unsolved problems in computer science today.

As it is impossible to solve these problems, or find the best solution to these problems, in a limited timespan when scaling out, Business Resource Planner uses a set of sophisticated optimization heuristics and meta-heuristics (like Tabu Search, Simulated Annealing and Late Acceptance) to find an optimal solution to these problems.

As said, every organisation has these kind of scheduling problems, and there is a lot to gain from optimising these problems. In the remainder of this post we will walk you through a number of steps to get you started with Business Resource Planner/OptaPlanner to find an optimal solution to your business problem and start increasing productivity, reducing costs and increasing customer satisfaction.

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