Lock scheduling optimization for a chain of locks

1. Lock schedulingoptimizationfor a chainoflocks Markus Krauß, ZFT

2. Agenda • Introductionof ZFT • Motivation • Model • Experiments • Results • Further Steps

3. Lock schedulingoptimizationfor a chainoflocks Intruductionof ZFT

4. 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

5. Introductionof ZFT TELEMATICS = Telecommunication + Automation + Informatics Main Idea: „Providing servicesover a distance.“

6. Introductionof ZFT CompetenciesandApplicationareas

7. Lock schedulingoptimizationfor a chainoflocks Motivation

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

9. 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.

10. 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.

11. Lock schedulingoptimizationfor a chainoflocks Model

12. 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

13. 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

14. 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

15. 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)

16. Model Software: Main view overview ships locks groupstatistics

17. Model Software: Lock operatorsview lock operatorview waiting downstream upstream chamber

18. Lock schedulingoptimizationfor a chainoflocks Experiments

19. 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

20. Experiments System Size: Global tendencyofruntime • For a biggerquantitystructureofparts (ships, locks, rules) theruntimeincreasesexponentialorworse.

21. 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

22. 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)

23. 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

24. 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.

25. Lock schedulingoptimizationfor a chainoflocks Results

26. 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.

27. 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.

28. Results Densityofjourneys, shipssetup time • The majorparameterforcalculation timeandmanageablesystemsizeistheconflictdensity (= overlap) oftheshipsjourneys. • The setup timeofshipsis a sensitive parameter. Ithasgreatinfluence on theoptimizationsolution. • Itshouldbeestimatedveryprecisely.

29. Lock schedulingoptimizationfor a chainoflocks Further steps

30. 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, …

31. Conclusion Optimization Potential • Thereisseen a high optimization potential forreducedtraveltimesaswellasautomaticplanningsupportforchainsoflocks.

32. Thankyou! Lock schedulingoptimizationfor a chainoflocks