drools

Geoffrey De Smet

Drools Planner optimizes automated planning

Agenda

Drools and jBPM Platform overview

Use casesBin packingWhat is NP complete?

Employee shift rosteringHard and soft constraints

Patient admission scheduleHow many possible solutions?

Algorithms

Results

Future

Drools and jBPM Platform

Overview

Business Logic Integration

BusinessLogicIntegrationPlatform

Rule
engine

Workflow

Complex event
processing
(CEP)

Business Rule
Management System(BRMS)

Automated
planning

Use cases

What are planning problems?

Hard constraint implementation

// a nurse can only work one shift per dayrule "oneShiftPerDay" when $left : EmployeeAssignment( $employee : employee, $shiftDate : shiftDate, $leftId : id ) $right : EmployeeAssignment( employee == $employee, shiftDate == $shiftDate, id > $leftId ) then // Lower the hard score with a weight ...end

Soft constraint implementation

rule "dayOffRequest" when $dayOffRequest : DayOffRequest( $employee : employee, $shiftDate : shiftDate, $weight : weight ) $employeeAssignment : EmployeeAssignment( employee == $employee, shiftDate == $shiftDate ) then // Lower the soft score with the weight $weight ...end

Object orientated

Readable
by domain experts

Fast and scalable:
Indexes, ReteOO, ...

Java methods usable

Isolated

Hospital bed planning

Hospital bed planning constraints

Hard constraintsNo 2 patients in same bed in same nightRoom gender limitationDepartment minimum or maximum agePatient requires specific room equipment(s)Soft constraintsPatient prefers maximum room sizeDepartment specializationRoom specializationPatient prefers specific room equipment(s)

Needle in a haystack

How many possible solutions?310 bedsin 105 roomsin 4 departments84 nights2750 patients (admissions)Numbers from a real dataset

Needle in a haystack

How many possible solutions?310 bedsin 105 roomsin 4 departments84 nights2750 patients (admissions)> humans?7000000000 humans

Source: NASA (wikipedia)

Needle in a haystack

How many possible solutions?310 bedsin 105 roomsin 4 departments84 nights2750 patients (admissions)> minimum atoms in the observable universe?10^80

Source: NASA and ESA (wikipedia)

Needle in a haystack

How many possible solutions?310 bedsin 105 roomsin 4 departments84 nights2750 patients (admissions)> atoms in the universe
if every atom is a universe
of atoms?(10^80)^80 = 10^6400

Source: NASA and ESA (wikipedia)

Needle in a haystack

How many possible solutions?310 bedsin 105 roomsin 4 departments84 nights2750 patients (admissions)A little over 10^6851

Do the math

1 patient310 beds310 ways to schedule 1 patient2 patients310 * 310 = 961003 patients310 * 310 * 310 = 297910002750 patients310 * 310 * ... * 310310^2750= a little over 10^6851

A little over 10^6851

17565400009613647714189309909847019528176031676612802408947467896773536824694320253771058944295328388965847321421820520338269384218763243353186553203291774156243401969717704429199723195574019731357439221586010900901318775661618988149185744289903563474940721372704649669447515706252843900344472960295273324597346780837674886972275338744554737771986776337765496773882810395955542351928331414378439283405132892358744224215499792287586474426914111457027335258269167103194692790665217766902628732489519488236496931246157419949856522543510386164584826962224171420152255658502323853504643497053252922727924406406179159327025274968691052425818270121710670552641837503435108794481961062422002729288737980404185875233912428178009558605680683578864680145557998942327371019832139824665975180911386722774004539981342785523851686636374432671601485496423110930175953308580416767966832068095365964656939583089443708945844323888227816296382464122209980716936990539499472027590738400791514217875461942733015467480308665074008529461146577114465977072581447925882122906827160570007228017056499674188148507908716786164922536467490587163626945689452927061532150637454615233641645612791027410608416476380642469085143980464067453971429400369608313929289399595696359958354101721624055729520838609453039985592726289376243856941429063767903919977138724432513602703448146045970566585076809576476984036923221553270878279574239866657156729051295085970100212856025787345034666683590797377984104207041334053480226788236770435009014979334517769826461063281178894554527012859966520606253098788699367180806367012372895824963357992764969799123361044456461687410981522493093316910464937089299655880447014074876397812210684054337706530175130912335567383560436635091489083375525519539844805718811296858076507685112192499405286337668107046095023999871224655107877174220679360212410573001491104381221662138764756898834588381340410892158544837200229008533930816794663631470201197595487045022087615873490295940409113638894083753062801416644858

A little over 10^6851

70757942487218045035508810158461046502812782292385633846174494690914238485407798379765738528402484632449685642401410897637632172694954465509230907381501728706686840208173164426348468614134650930610728341876292346711310677332648553951422991989751468506508069513397904821612697383659788320905691994864296149528670873271380180066507702490525594196383327289726362280241278856579591895744202499646581373849829964867870738964842426372580420928473929652466453066089306581572745139056256181505240205578741292314133858985615181677968313683876880079649763914095651402272047776108451445066888366968448972791816669030945790810396895725248399188822034667566483522327685006195080178525107491255245054238976705620547582329759857450557583834141364747838395082871684183199560673441708538602779816347949276957201268066627372831370768073558934679410276824283049183299518869516908654179971718550810202675628457097617280232889096038128616543128200089047813223519902714196694639844273986565523012106816309974964021700560537928432528631741787455155275823033005847637107210747721372066369346754152090839849611389908167353907349234368276522287410730637555342957452428266968002573227849933691449063418286501311036314048960528249465982665132492491072491788618403253474529440348798670244615185355092357283764936387607076234181916670755269421546530337284689838773122323053171794271444358533638806848969871884168582847613016398013006633016140503743175611255484273419291435502775849577615159921009571496639402549077872124227731739936370100132762333353473908080215719004335487157010620020523763648856692728699459471603410776592535816520745995861336577877431293776796124264697049418795186010546056975226424227455446011031581123438684685838655333776830823720356749120051129394691394743488410658613543537795455760655157849602214428915084056181314465895075924498263846040474495622654552157933867572542745838370889391243702358459244386561081479905545570084491443709439642235090455604548030317754849613813010298858282615659336373785985294

A little over 10^6851

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A little over 10^6851

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The search space is big!Compare with WWW size 22020000000 pagesEach possible solution2750 patients scheduled into 310 bedsStill need to calculate the score! => Drools Expert

Brute force?
Throw hardware at it?

Calculate 10^9 scores per msImpossible today!Actually more like 1-10 scores per ms per CPU

31579200000 ms in 1 year< 10^11 ms in 1 year

10^9 * 10^11 scores per year= 10^20 scores per year

How many years? 10^6851 / 10^20= 10^6831 years

CPU 1000 times fasterIt becomes 10^6828 years

Algorithms

Operations research is fun.

N Queens: use case

Place n queens on a n-sized chess board

No 2 queens can attack each otherScore -1 for every 2 queens that can attack each other

Score = -2

Score = 0

Brute force algorithm

Useless with
a real world n

A huge performance gain
because of Drools Expert

Critical for ALL of your constraints!

No extra code thanks to Drools Expert

Branch and bound algorithm

Useless with
a real world n

Useful algorithms

Construction heuristicsFirst fit (decreasing), Best fit (decreasing), ...

Easy to implementDrools Planner score supportOut-of-the-box support for Drools Planner 5.3

Short and stable time (for example 8 seconds)

MetaheuristicsMove things aroundStart from result of construction heuristics

Tabu search, Simulated annealing, ...

More time = better score

Greedy algorithm
First fit

Greedy algorithm
First fit decreasing

Human planner are worse
than this algorithmand take more time. (*)
(*) with a real world n

Metaheuristics:
Move things around

Move = from solution A to solution BChange the row of 1 queen

Give 2 queens each others rows

...

Score = -6

Score = -4

Local search 1/2

Local search
2/2

Search pathNot a tree

Local optima

1) Construction heuristic StartingSolutionInitializer

2) Simple local search

3) Stuck in local optimum!

Source: Wikipedia

Score function

Local search++
Don't get stuck in local optima

Tabu SearchSolution tabu (high tabu size)Been there, no need to go there again

Move tabu (low tabu size)Done that recently, no need to do that again

Property tabu (low tabu size)Changed that recently,
no need to change that again

Simulated annealing

Great deluge, late acceptance,

Hyper heuristics

Termination

Synchronous (configurable)Max timeMillis/seconds/minutes/hours spend

Score attained

Max step count

Max unimproved step count

Asynchronous (from another thread)planner.terminateEarly();

Get the resultplanner.getBestSolution();

Results

Measure, don't guess.

Results: input

Competition use cases and datasetsReal world data

Real world constraints

Not only the datasets that perform well (no cheating)

Greedy: First Fit Decreasing (FFD)Performs better than humansEspecially when the dataset or constraints scale out

Metaheuristics: Tabu search or SAStarting from Greedy First Fit Decreasing

10 minutes total time (greedy included)Normal PC: Xeon 3.07 Ghz (using 1 of 4 CPU's)

Results: Greedy vs Metaheuristics

Hospital bed planning (PAS)Dataset testdata01-13

Tabu search is on average 26% better than FFDFrom 9% to 53% better

=> Better healthcare, more single rooms sold, ...

Nurse rostering (INRC2010)Dataset medium01-05, medium_hint01-03

SA is on average 164% better than FFDFrom 24% to 543% better

=> Better healthcare, less overtime, happier nurses, ...

Tabu search is slightly worse than SA

Results: Greedy vs Metaheuristics

Examination (ITC2007 track 3)Dataset exam_comp_set1-5,8Broken hard constraints make fair calculations difficultExcluded exam_comp_set6: infinity% better-51hard/-5338soft to 0hard/-5037soft

Excluded exam_comp_set7: 0% better-6hard/-9802soft to -6hard/-9116soft

Not even counting soft where there's hard improvement

Tabu search is on average 85% better than FFDFrom 1% to 195%

=> Happier students

Don't believe me?Download the examples!

Benchmarker class

Real output from
the Benchmarker class

CPU power VS algorithms

more CPU power

better
algorithm

Real output from
the Benchmarker class

Future

Simpler, faster and more powerful

Repeated planning

Better Guvnor integration

Planning domain experts create the score rulesNurse rostering: head nurse

Truck routing: logistics expert

Job shop scheduling: supply chain expert

Using Guvnor's web interface

Summary

Summary

Drools Planner solves planning problems

Adding constraints is easy and scalable

Switching/combining algorithms is easy

Try an example!

Q & A

Questions?

Useful linksDrools Planner homepage:http://www.jboss.org/drools/drools-planner

Reference manual:http://www.jboss.org/drools/documentation

Download this presentation:http://www.jboss.org/drools/presentations

Mailing list (use forum) for further questions: http://www.jboss.org/drools/lists.html

Team blog: http://blog.athico.com/

Twitter: @geoffreydesmet

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