[rules-users] Using accumulate function
Hi, Iam trying to use the accumulate function of the drools.I need to accumulate based on the result of a rule execution. Eg: Suppose i have list of orders and another list of conditions for a particular package.I need to check weather each of the order is part of package or not. Suppose a qty limit is set to 1 for a item in package definition and the item is ordered 2 times. While checking the condition, both of the item will go under package. Actually for second record the rule should get executed based on result of the 1 record's rule execution and so on. So I require a looping rule execution. How can i achive this using Drools. Rule Package Rate_21 salience 65515 when $objOrderedComponents:AccumulatedComponent() $packageDefinition:PackageDefinition($packageId:packageId) $accumulatedQty:Number() from accumulate( $accQtyComponent:OrderedComponent(packageChargeRecordId==$packageId, $AccQty : componentQty) from $objOrderedComponents.getOrderedComponentList(),sum($AccQty ) ) $orderedComponent:OrderedComponent($id1:componentKey,$qty:componentQty) from $objOrderedComponents.getOrderedComponentList() and $pkgComps:PackageComponent(componentKey==$id1,(quantityLmt =($qty+$accumulatedQty.intValue( from $packageDefinition.getPackageComponentList() then $orderedComponent.setIsComponentApplicable(true); $orderedComponent.setPackageChargeRecordId($packageId); end Regards Sanal.P -- View this message in context: http://drools.46999.n3.nabble.com/Using-accumulate-function-tp3896078p3896078.html Sent from the Drools: User forum mailing list archive at Nabble.com. ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users
[rules-users] Objects from Working Memory not available in drl
Hey guys, I'm playing with the drools planner a few days now and got stuck. I have a Solution with an PlanningEntity Ambulance and a normal Class SendAmbulance. I add a List SendAmbulance to the Working Memory with the getProblemFacts() method. The Ambulance objects are loaded automatically. The problem now is that i can access the Ambulance Objects within the drl correctly but nut the SendAmbulance objects. Has somebody an advice for me to solve this? thanks, Magublafix -- View this message in context: http://drools.46999.n3.nabble.com/Objects-from-Working-Memory-not-available-in-drl-tp3896957p3896957.html Sent from the Drools: User forum mailing list archive at Nabble.com. ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users
[rules-users] Employee Shift Rostering: algorithm details
Hi, I'm new both to Drools Planner and to metaheuristic algorithms like Tabu Search. Though I have done some preliminary reading on the Net on how this algorithm 'generally' works -- including reading the 'plain-English' portions of Chapter 8 of the 'Essentials of Metaheuristics' book -- it still isn't perfectly clear to me how an actual implementation of the algorithm would code the 'Tweak' / 'Next Move' function (other than via a random combination of planning variables) in an application such as Employee Shift Rostering with a very large search space and with multiple planning variables. On the page (http://www.jboss.org/drools/drools-planner.html) where Drools Planner is compared side-by-side to other naive (greedy algorithms-based) approachesfor various kinds of problems, could it be the case that the algorithms used in Drools Planner were able to perfectly solve the highlighted problems only because of the use of extremely small search spaces / input datasets?! A wikipedia entry related to metaheuristic algorithms says that not only is it possible that such an algorithm may not give an optimal solution, it may not even give any solution at all(!!) due to the algorithm reaching its timeout limit specified by the user. I'm assuming it won't be possible to specify a timeout value in days or even hours for any metaheuristics-based approach to a real-world problem of a real-world scale. Lastly, from looking at the pseudocode of Tabu Search, it *appears* that the core, 'driver portion' of the algorithm may itself be not all that hard to implement; it is rather the call-back portion of the algorithm (such as the problem-specific Tweak / Next Move function, which the driver portion calls back) that may be hardest to implement. Given this, what would be the advantages of using Drools Planner over a from-scratch, hand-coded version of Tabu Search (other than its declarative, rule-based flexibility which a hand-coded, from-scratch version may not want to implement due to its focus on solving a specific planning problem vs. a whole slew of generic planning problems requiring a generic piece of code such as Drools Planner)? Would greatly appreciate if someone more knowlegeable than me could clarify / elaborate on the above getting-started questions of mine. Regards, /HS -- View this message in context: http://drools.46999.n3.nabble.com/Employee-Shift-Rostering-algorithm-details-tp3897546p3897546.html Sent from the Drools: User forum mailing list archive at Nabble.com. ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users
Re: [rules-users] Return List of Fired Rule Name from Decision table
Title: CARTE DE VISITE
You should use an AgendaListener instead of polluting your rules
with such things.
see there :
http://docs.jboss.org/drools/release/5.4.0.Beta2/drools-expert-docs/html_single/index.html#d0e2003
Le 09/04/2012 03:21, s a a crit:
I have resolved this problem using the following
approach
Create a Class with a method (say)
PrintRuleName(KContext
kcontext) { System.out.println(kcontext.getRuleName());}
In the decision table in the action Create an Instance of
this class and call this method by passing kcontext. Action
would look like
( Action for each row in decision table will have this)
PrintClass ThePrintClass = new PrintClass();
ThePrintClass.setPrintRuleName(kcontext);
SA
From:
srinivasasanda srinivasasa...@gmail.com
To:
rules-users@lists.jboss.org
Sent:
Thursday, April 5, 2012 8:56 AM
Subject:
[rules-users] Return List of Fired Rule Name from
Decision table
Hi,
when a rule fires i am storing the rule name in Result field
of type list as
result.add(drools.getRule().getName()) -- in 'part' of a
normal rule.
Now i am using decision table,how can i get the rule names
fired.
I am not able to give java statement in action or in row
contain field.Where
to write that Java statement in Decison Table?Can you
suggest me please.
Thanks
Srinivasa Sanda
--
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Re: [rules-users] Employee Shift Rostering: algorithm details
Drools Planner has 7 real examples, which use large datasets and are solved near optimal in reasonable time. For example, the nurse rostering example have search spaces over 10^2000. Research competions show that metaheuristics are the only algorithems that can handle it in robuust way. Wikipedia's notes are correct: there is no guarentee that the optimal solution is found, but exact methods take forever and simplex like methods perform much worse overall in the competitions. For example, in the nurserostering example, the medium hint 01 example performed 100 times ( 10 000%) on a well known simplex framework (the other datasets were around 5% better or worse). Tabu search is easy to implement, but there are many optimizations that Planner implements, such as aspiration, ... Its been improved and under constant benchmarking since 2006, so it would take some work to replicate :-) -- View this message in context: http://drools.46999.n3.nabble.com/Employee-Shift-Rostering-algorithm-details-tp3897546p3897706.html Sent from the Drools: User forum mailing list archive at Nabble.com. ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users
Re: [rules-users] Employee Shift Rostering: algorithm details
I may not be an expert, but I may give you some tips. Read below Patrik Dufresne On Mon, Apr 9, 2012 at 1:57 PM, HarrySimons simonsha...@gmail.com wrote: Hi, I'm new both to Drools Planner and to metaheuristic algorithms like Tabu Search. Though I have done some preliminary reading on the Net on how this algorithm 'generally' works -- including reading the 'plain-English' portions of Chapter 8 of the 'Essentials of Metaheuristics' book -- it still isn't perfectly clear to me how an actual implementation of the algorithm would code the 'Tweak' / 'Next Move' function (other than via a random combination of planning variables) in an application such as Employee Shift Rostering with a very large search space and with multiple planning variables. If you look closer to the Nurse-Rostering example, you will notice the most important moves are EmployeeChangeMove and ShiftAssignmentSwapMove. Those moves may be qualify as small moves. As recommended in the Drool-planner user guide, Tabu search perform great with small moves (see Chapter 8.5). It's generally very simple to implement this type of moves and may be sufficient for your problem. As I understand, you generally create all the possible moves. Then drools-planner will select them randomly. On the page (http://www.jboss.org/drools/drools-planner.html) where Drools Planner is compared side-by-side to other naive (greedy algorithms-based) approachesfor various kinds of problems, could it be the case that the algorithms used in Drools Planner were able to perfectly solve the highlighted problems only because of the use of extremely small search spaces / input datasets?! A wikipedia entry related to metaheuristic algorithms says that not only is it possible that such an algorithm may not give an optimal solution, it may not even give any solution at all(!!) due to the algorithm reaching its timeout limit specified by the user. I'm assuming it won't be possible to specify a timeout value in days or even hours for any metaheuristics-based approach to a real-world problem of a real-world scale. In real-world problem involving workforce management, you should get a feasible solution very fast. With drools-planner you can configure a construction heuristic to get a first feasible solution -- the nurse-rostering example use one. Then the tabu search is used to improve the solution. You may then consider the solution created by the construction heuristic as the worst solution. Lastly, from looking at the pseudocode of Tabu Search, it *appears* that the core, 'driver portion' of the algorithm may itself be not all that hard to implement; it is rather the call-back portion of the algorithm (such as the problem-specific Tweak / Next Move function, which the driver portion calls back) that may be hardest to implement. Given this, what would be the advantages of using Drools Planner over a from-scratch, hand-coded version of Tabu Search (other than its declarative, rule-based flexibility which a hand-coded, from-scratch version may not want to implement due to its focus on solving a specific planning problem vs. a whole slew of generic planning problems requiring a generic piece of code such as Drools Planner)? I would definitely recommend drools-planner to your own implementation of the Tabu search just to benefit from the drools engine it self which provide you a very fast way to calculate the solution's score. The score is calculated using an accumulate and then doesn't required to calculate the score from scratch. I benchmark drools vs a plain java score function and drools is way way faster. Would greatly appreciate if someone more knowlegeable than me could clarify / elaborate on the above getting-started questions of mine. Regards, /HS -- View this message in context: http://drools.46999.n3.nabble.com/Employee-Shift-Rostering-algorithm-details-tp3897546p3897546.html Sent from the Drools: User forum mailing list archive at Nabble.com. ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users ___ rules-users mailing list rules-users@lists.jboss.org https://lists.jboss.org/mailman/listinfo/rules-users
