![]() ![]() ![]() $cpuPower - $requiredCpuPowerTotal.intValue(), InsertLogical(new IntConstraintOccurrence("requiredCpuPowerTotal", ConstraintType.HARD, $requiredCpuPowerTotal : Number(intValue > $cpuPower) from accumulate( $computer : CloudComputer($cpuPower : cpuPower) To fix that, either use an incremental Java score function or a Drools scoreįunction. ProcessList only once, it is still slow because it doesn'tĭo incremental score calculation. implements Solution Įven if we optimize the code above to use Maps to iterate through the Let's examine the domain model classes and the score configuration. You can tweak it to get even better results. Will already surpass human planners and most in-house implementations. Optimization algorithms configuration: How should Planner optimize it?ĭon't worry about this for now: this is a good default configuration that works on most planning problems. 'll look into 2 alternatives to calculate the score: using a simple Java implementation or using Drools Need to tell Planner how to calculate such the score, depending on our business requirements. Variables? Since we have hard and soft constraints, we use a HardSoftScore. Score configuration: How should Planner optimize the planning This solver configuration consists out of 3 parts:ĭomain model configuration: What can Planner change? We need to make FIRST_LAST_STEP_SCORE_EQUAL_OR_IMPROVING org/drools/planner/examples/cloudbalancing/solver/cloudBalancingScoreRules.drl-> With Planner, it is easy to switch the optimizationĪlgorithm, by changing the solver configuration in a few lines of XML or code. Depending on the use case, some optimization algorithms perform better than others,īut it's impossible to tell in advance. Implementation is forced to evaluate at least a subset of all those possible solutions.ĭrools Planner supports several optimization algorithms to efficiently wade through that incredibly large Because there is no silver bullet to find the optimal solution, any As you can see in the examples, most instances have a lot more possible solutions than the minimal number The best solution found is likely to be feasible and, given enoughĬounterintuitively, the number of possible solutions is huge (if calculated correctly), even with a smallĭataset. Implementation in a given amount of time. The best solution found is the solution with the highest score found by an There is always at least 1 optimal solution, even in the case that thereĪre no feasible solutions and the optimal solution isn't feasible. Every feasible solution is a possible solution.Īn optimal solution is a solution with the highest score. Sometimes there are no feasible solutions. The number of feasible solutions tends to be relative to the number of possible solutions. Many of thoseĪ feasible solution is a solution that does not break any (negative) hardĬonstraints. Planning problems tend to have an incredibly large number of possible solutions. It isĪ possible solution is any solution, whether or not it breaks any number ofĬonstraints. Under the hood, it combines optimization heuristics and metaheuristics with very efficient score calculation.ĭrools Planner, like the rest of Drools, is open source software under the Apache Software License 2.0 ( layman's explanation). This is known asĬonstraint Satisfaction Programming (which is part of the discipline Operationsĭrools Planner helps normal Java TM programmers solve planning problems efficiently. įinancial optimization: investment portfolio optimization, riskĮvery organization faces planning problems: provide products and services with a limited set ofĬonstrained resources (employees, assets, time and money). Sport scheduling: planning football leagues, baseball leagues. Job shop scheduling: planning car assembly lines, machine queue planning,Ĭutting stock: minimizing waste while cutting paper, steel, carpet, Vehicle routing: planning vehicles (trucks, trains, boats, airplanes,īin packing: filling containers, trucks, ships and storage warehouses, Īgenda scheduling: scheduling meetings, appointments, maintenance jobs,Įducational timetabling: scheduling lessons, courses, exams, conference ![]() Employee shift rostering: timetabling nurses, repairmen. ![]()
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