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Lock scheduling optimization for a chain of locks. Markus Krauß, ZFT. Agenda. Introduction of ZFT Motivation Model Experiments Results Further Steps. Lock scheduling optimization for a chain of locks. Intruduction of ZFT. Introduction of ZFT. C enter for Telematics.

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agenda
Agenda
  • Introductionof ZFT
  • Motivation
  • Model
  • Experiments
  • Results
  • Further Steps
introduction of zft
Introductionof ZFT

Center forTelematics

  • Founded 2008 as University Würzburg spin off frominformatics/roboticschair
  • Operating in bothworlds
    • Public fundedresearchprojects, national and international / (duration: 1-4 years)
    • Directindustrialcontracts / (duration: weekstomonths)
  • Interdisciplinaryteam
    • Informatics
    • Control Engineering
    • Communications Engineering
    • Physics
  • Contact: www.telematik-zentrum.de
introduction of zft1
Introductionof ZFT

TELEMATICS = Telecommunication + Automation + Informatics

Main Idea: „Providing servicesover a distance.“

introduction of zft2
Introductionof ZFT

CompetenciesandApplicationareas

motivation1
Motivation

Chain oflocksoptimization

  • Single lock
    • Main parametersanddisturbances?
    • Optimization potential fortraveltime/waittimeexists!
  • Chain oflocks
    • AIS enables optimal planningfor a extendeddomain!
    • Howtorealize?
    • Whichcharacteristics?
    • Potential ofoptimization?
motivation2
Motivation

Requirements & Usage-Scenario fromourcustomer WSV

  • Planning
    • Software hasrealtimeaccess (in withinseconds) on AIS dataandsystemparameters
  • Deviation bythe lock operator
    • The lock operatoristhe last decisiondrawinginstance. He acceptsthesystemssuggested plan or not!
  • Deviation byshipcaptains
    • Eachcaptainperformshisjourney on his individual responsibility.
    • An incentivetocomplytothescheduleshouldbegivenbyoverallreducedtraveltimes.
  • Replanning
    • Automatically on recognizeddeviationorexplicitelycalledby lock operator.
    • The newsolutionhastobeavailable after a maximumtime of 5 minutes.
motivation3
Motivation

State ofthe Art

  • Single Lock
    • Different (also different in complexity) approachesavailable
    • Becauseoftheirhugecalculation time, not useablefor a chainoflocks
  • Chain oflocks
    • Nohelpfulapproachesavailable on themarket
    • Ifthetopic was addressed, thenwithother environmental conditions (e.g. Missisippi) thanneeded in thedomainof WSV (German rivers, especially Rhein-Main-Donau).
  • ProductionLogisticsand „Supply Chain Management“
    • Approaches not transfereable, becauseofa different problemfocus in theirfield
      • A calculation time ofweeks/monthsisacceptable. A logisticschain (shapeandlocation) isthesolutionoftheoptimizationproblemand will thenbebuilt. In contrastto WSV: The lockschainisalreadyexisting, but cyclically optimal planningsolutionsarerequired in realtime.
model1
Model

Consequencesof ZFT

  • The waterwaysofthe WSV havespecialsourroundingconditionsandrequirements
    • Realtime limit: Solution required in within 5 Minutes
    • Locks withrelativelysmallchambersandshortlockageprocesstimes
    • Main actuating variable istheshipsspeed (trafficguidance)
  • The ZFT hadtocreate a newsolution
    • Noavailableortransfereablesolution on themarket
  • The modelhastobekeptas simple aspossible
    • Tosatisfytherealtimelimit
    • Toofferclearandinterpretabletestresults
model2
Model

Short IntroductiontoOptimization

  • Model (= mathematicalformulationoftheproblem)
    • A goalfunctionshallbeminimizedormaximized. In ourcase, thesumof all shipstraveltimesshallbe minimal.
    • Asolutionhastosatisfy a numberofconstrains, e.g. thepackingproblem (whatships fit together in thelockschamber).
  • Solver (= Solving Tool)
    • Search for an optimum
      • Solvability, linearity, localoptimum
    • Integer constrains
      • Mixed Integer Linear Program (MILP)
      • Search tree, heuristics
model3
Model

Lock schedulingoptimizationof a chainoflocks

  • Chaining, sequencing
    • Scheduledtime ofarrivalatlock
  • Initialization
    • Start ofships outside oflocks
    • Start ofships in within a lockageprocess
  • Grouping, Packing Problem
    • One dimensional packingproblemonly
model4
Model

Implementation: Principalsteps

  • The softwareisimplemented in JAVA
  • The problemissolved in threesteps
    • Preprocessing: Automaticformulationofthemathematicalmodel, byanalyzingthecurrentsituation (positions, speeds etc. from AIS)
    • Optimization: solvertoolsearchesforfeasible optimal solution
    • Postprocessing: Solution ispreparedfortheviewsofthe „customers“
      • Shipscaptainsget a travel plan (whentoarriveatwhat lock plus a recommended medium speedforeachsection)
      • Lock operatorsget a detailed plan, whichship(s) whentoprocess (andiftogether)
model5
Model

Software: Main view

overview

ships

locks

groupstatistics

model6
Model

Software: Lock operatorsview

lock operatorview

waiting

downstream

upstream

chamber

experiments1
Experiments

Researched Topics

  • System Size
    • Runtime (minutes, hours, days)?
    • Ressources (Memory consumption, Processing load)?
    • Howmanyships, howmanylocks, practicablechainlength?
  • System Dynamics
    • Stabilityof a solution
    • Robustnessof a solutionagainsdeviations
experiments2
Experiments

System Size: Global tendencyofruntime

  • For a biggerquantitystructureofparts (ships, locks, rules) theruntimeincreasesexponentialorworse.
experiments3
Experiments

System Size: Calculation time dependency on „conflictsdensity“

3 configurations – 4 locks, 13 ships

  • Extremelycloselypackedjourneys calculation time 30 minutes RAM 600 MB
  • Same locks/ships, but slightlyeasedpackingbymovingthe starttime ofsomeships calculation time 5 minutes RAM 300 MB
  • The conflictsdensityisagainloweredbymovingtwomoreships calculation time 1 second RAM 100 MB
experiments4
Experiments

Results: System Size

  • Calculation time andmemoryconsumptiondependmainly on thenumberofoverlappingjourneys („conflictdensity“)
  • All investigatedtestscenariosweregiven a high systemload (conflictdensity), closetothe „worstcase“
  • Typicallyourmodelcanbesolvedfor4-6 locksand10-15 shipsin lessthan5 minutes.
  • The plannninghorizonistherebyoneday(an upstreamjourneyover all 6 lockstakesroughly 24 h)
experiments5
Experiments

System Dynamics

A high planningreliabilityforshipscaptainsand lock operatorsisrequired.

  • The stabilityof a solutionshallbe high
    • foridenticalinitialconfigurations (repeatability)
    • Forfuture time situationsthatarecompliantwiththeschedule (futuredevelopment)
  • The robustnessagainstdeviationsshallbe high
    • Tardinessof a ship (e.g. arrivalat lock toolate)
    • Earlinessof a ship (fasterthanplanned)
    • Shipis not processednow due to lock operatorsdecision
    • Shipneedsunexpectedlylong „setup time“ forpreparationoflockage
    • Unannouncedoriginatingorterminatingtraffic
experiments6
Experiments

Results: System Dynamics

  • Stability
    • Forourinvestigatedconstellations, thesolutionswerealwaysreproducible / stable.
  • Robustness
    • In caseofdeviationsfromthe plan, thealterationoftheoptimizationsolutiondepends on thesituation:
      • Forlowconflictdensity, deviationsfromthe plan canbecompensatedwithoutchangingthelockagesequence. Typicallyonly a slightdelayorspeedadaptionsarerequired.
      • At a high conflictdensity, a deviationcaninitiate a biggerreorginizationoftheschedule (different lockagesequencesand time schedulesformanyships), becausethisismore time efficientforthewholegroupofships.
results1
Results

First availablesolutionfor a chainoflocks

  • Forthefirst time, thereisavailable a solutionforoptimizing a wholechainoflocks.
  • A practice relevant quantitystructureofshipsandlockscanbecalculatedin withina realtimelimitof 5 minutes.
  • Chain lengthsof4-6 lockswithgroupsof10-15 shipsaresolvable in time.
  • The planninghorizonof such a systemsizeisaboutoneday.
  • A goodplanningreliabilityandrobustnessagainstdeviationsisachieved.
results2
Results

Group delay, FIFO rule, waittimes

  • Comparedto an exclusiveuseofthewaterway, thegroupdelayincreasesonlyslightlyaboutsomefewpercents(typicallybetween 1 and 15 percent).
    • Imagineyoucoulddrive on thehighwayandarriveonly 15 percentlaterthanwhenusingthehighwayexclusively!
  • The FIFO rule (firstcomefirstserve)couldbeomitted, becausethedevelopedvelocitycontrol (bymeansofthescheduledarrivaltimes) createstherightsequenceofshipsalready on theirwaytothelocks.
  • Waittimesalmostdisappear.
results3
Results

Densityofjourneys, shipssetup time

  • The majorparameterforcalculation timeandmanageablesystemsizeistheconflictdensity (= overlap) oftheshipsjourneys.
  • The setup timeofshipsis a sensitive parameter. Ithasgreatinfluence on theoptimizationsolution.
    • Itshouldbeestimatedveryprecisely.
further steps1
Further steps

Roadmap

  • Next stepshouldbe an evaluationofthecreatedsolution in practice.
  • AP3: Passive evaluationofthesimulation
      • Observingsingle lock baseddecisionsof a human lock operatorandcomparingthemwiththechain-optimal solutionsofthesoftware.
  • AP4: Extension ofthemodel
      • River flowspeed, automaticforecastmanagementfordelayedships, (different) parallel lock chambers, two dimensional packingproblem (withdockingrules), possibilitytoforcing FIFO-rule, prioritylockage (alternatingchainofwhite/greylineships), …
  • AP5: Activeevaluationofthesoftware
      • Limited test Rollout, Software generatedplans will befollowedby all shipsand lock operators, Communications/Messaging systemisavailable, aceptabilityproblems, neededregulations, …
conclusion
Conclusion

Optimization Potential

  • Thereisseen a high optimization potential forreducedtraveltimesaswellasautomaticplanningsupportforchainsoflocks.
thank you
Thankyou!

Lock schedulingoptimizationfor a chainoflocks