lock scheduling optimization for a chain of locks
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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. - PowerPoint PPT PresentationTRANSCRIPT
Lock scheduling optimization for a chain of locks03.12.2013 1
Lock scheduling optimization for a chain of locksMarkus Krauß, ZFT
Lock scheduling optimization for a chain of locks03.12.2013 2
Agenda• Introduction of ZFT
• Motivation
• Model
• Experiments
• Results
• Further Steps
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Intruduction of ZFTLock scheduling optimization for a chain of locks
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Introduction of ZFT
• Founded 2008 as University Würzburg spin off from informatics/robotics chair
• Operating in both worlds
– Public funded research projects, national and international / (duration: 1-4 years)
– Direct industrial contracts / (duration: weeks to months)
• Interdisciplinary team
– Informatics
– Control Engineering
– Communications Engineering
– Physics
• Contact: www.telematik-zentrum.de
Center for Telematics
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Introduction of ZFTTELEMATICS = Telecommunication + Automation + Informatics
Main Idea: „Providing services over a distance.“
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Introduction of ZFTCompetencies and Application areas
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MotivationLock scheduling optimization for a chain of locks
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Motivation
• Single lock
– Main parameters and
disturbances?
– Optimization potential for
traveltime/waittime exists!
• Chain of locks
– AIS enables optimal planning for
a extended domain!
– How to realize?
– Which characteristics?
– Potential of optimization?
Chain of locks optimization
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Motivation
• Planning
– Software has realtime access (in within seconds) on AIS data and system parameters
• Deviation by the lock operator
– The lock operator is the last decision drawing instance. He accepts the systems suggested
plan or not!
• Deviation by ship captains
– Each captain performs his journey on his individual responsibility.
– An incentive to comply to the schedule should be given by overall reduced travel times.
• Replanning
– Automatically on recognized deviation or explicitely called by lock operator.
– The new solution has to be available after a maximum time of 5 minutes.
Requirements & Usage-Scenario from our customer WSV
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Motivation
• Single Lock
– Different (also different in complexity) approaches available
– Because of their huge calculation time, not useable for a chain of locks
• Chain of locks
– No helpful approaches available on the market
– If the topic was addressed, then with other environmental conditions (e.g. Missisippi) than
needed in the domain of WSV (German rivers, especially Rhein-Main-Donau).
• Production Logistics and „Supply Chain Management“
– Approaches not transfereable, because of a different problem focus in their field
• A calculation time of weeks/months is acceptable. A logistics chain (shape and location) is the
solution of the optimization problem and will then be built. In contrast to WSV: The locks chain is
already existing, but cyclically optimal planning solutions are required in realtime.
State of the Art
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ModelLock scheduling optimization for a chain of locks
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Model
• The waterways of the WSV have special sourrounding conditions and requirements
– Realtime limit: Solution required in within 5 Minutes
– Locks with relatively small chambers and short lockage process times
– Main actuating variable is the ships speed (traffic guidance)
• The ZFT had to create a new solution
– No available or transfereable solution on the market
• The model has to be kept as simple as possible
– To satisfy the realtime limit
– To offer clear and interpretable test results
Consequences of ZFT
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Model
• Model (= mathematical formulation of the problem)
– A goal function shall be minimized or maximized. In our case, the sum of all ships
traveltimes shall be minimal.
– A solution has to satisfy a number of constrains,
e.g. the packing problem (what ships fit together in the locks chamber).
• Solver (= Solving Tool)
– Search for an optimum
• Solvability, linearity, local optimum
– Integer constrains
• Mixed Integer Linear Program (MILP)
• Search tree, heuristics
Short Introduction to Optimization
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Model
• Chaining, sequencing
– Scheduled time of arrival at lock
• Initialization
– Start of ships outside of locks
– Start of ships in within a lockage process
• Grouping, Packing Problem
– One dimensional packing problem only
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Model
• The software is implemented in JAVA
• The problem is solved in three steps
– Preprocessing: Automatic formulation of the mathematical model, by analyzing the
current situation (positions, speeds etc. from AIS)
– Optimization: solver tool searches for feasible optimal solution
– Postprocessing: Solution is prepared for the views of the „customers“
• Ships captains get a travel plan (when to arrive at what lock plus a recommended medium speed
for each section)
• Lock operators get a detailed plan, which ship(s) when to process (and if together)
Implementation: Principal steps
Preprocessing< Optimization Postprocessing
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ModelSoftware: Main view
ships locks
overview
group statistics
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ModelSoftware: Lock operators view
lock operator view
upstream downstream
waiting
chamber
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ExperimentsLock scheduling optimization for a chain of locks
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Experiments
1. System Size
– Runtime (minutes, hours, days)?
– Ressources (Memory consumption, Processing load)?
– How many ships, how many locks, practicable chain length?
2. System Dynamics
– Stability of a solution
– Robustness of a solution agains deviations
Researched Topics
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Experiments
• For a bigger quantity structure of parts (ships, locks, rules) the runtime increases
exponential or worse.
System Size: Global tendency of runtime
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Experiments
3 configurations – 4 locks, 13 ships
• Extremely closely packed journeys
calculation time 30 minutes
RAM 600 MB
• Same locks/ships, but slightly eased packing by moving the starttime of some ships
calculation time 5 minutes
RAM 300 MB
• The conflicts density is again lowered by
moving two more ships
calculation time 1 second
RAM 100 MB
System Size: Calculation time dependency on „conflicts density“
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Experiments
• Calculation time and memory consumption depend mainly on the number of
overlapping journeys („conflict density“)
• All investigated test scenarios were given a high system load (conflict density), close
to the „worst case“
• Typically our model can be solved for 4-6 locks and 10-15 ships in less than 5
minutes.
• The plannning horizon is thereby one day
(an upstream journey over all 6 locks takes roughly 24 h)
Results: System Size
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Experiments
A high planning reliability for ships captains and lock operators is required.
• The stability of a solution shall be high
– for identical initial configurations (repeatability)
– For future time situations that are compliant with the schedule (future development)
• The robustness against deviations shall be high
– Tardiness of a ship (e.g. arrival at lock too late)
– Earliness of a ship (faster than planned)
– Ship is not processed now due to lock operators decision
– Ship needs unexpectedly long „setup time“ for preparation of lockage
– Unannounced originating or terminating traffic
System Dynamics
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Experiments
• Stability
– For our investigated constellations, the solutions were always reproducible / stable.
• Robustness
– In case of deviations from the plan, the alteration of the optimization solution depends on
the situation:
• For low conflict density, deviations from the plan can be compensated without changing the
lockage sequence. Typically only a slight delay or speed adaptions are required.
• At a high conflict density, a deviation can initiate a bigger reorginization of the schedule (different
lockage sequences and time schedules for many ships), because this is more time efficient for
the whole group of ships.
Results: System Dynamics
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ResultsLock scheduling optimization for a chain of locks
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Results
• For the first time, there is available a solution for optimizing a whole chain of
locks.
• A practice relevant quantity structure of ships and locks can be calculated in within a
realtime limit of 5 minutes.
• Chain lengths of 4-6 locks with groups of 10-15 ships are solvable in time.
• The planning horizon of such a system size is about one day.
• A good planning reliability and robustness against deviations is achieved.
First available solution for a chain of locks
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Results
• Compared to an exclusive use of the waterway, the group delay increases only
slightly about some few percents (typically between 1 and 15 percent).
– Imagine you could drive on the highway and arrive only 15 percent later than when
using the highway exclusively!
• The FIFO rule (first come first serve) could be omitted, because the developed
velocity control (by means of the scheduled arrival times) creates the right sequence
of ships already on their way to the locks.
• Wait times almost disappear.
Group delay, FIFO rule, wait times
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Results
• The major parameter for calculation time and manageable system size is the
conflict density (= overlap) of the ships journeys.
• The setup time of ships is a sensitive parameter. It has great influence on the
optimization solution.
– It should be estimated very precisely.
Density of journeys, ships setup time
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Further stepsLock scheduling optimization for a chain of locks
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Further steps
• Next step should be an evaluation of the created solution in practice.
• AP3: Passive evaluation of the simulation
• Observing single lock based decisions of a human lock operator and comparing them with the
chain-optimal solutions of the software.
• AP4: Extension of the model
• River flow speed, automatic forecast management for delayed ships, (different) parallel lock
chambers, two dimensional packing problem (with docking rules), possibility to forcing FIFO-
rule, priority lockage (alternating chain of white/grey line ships), …
• AP5: Active evaluation of the software
• Limited test Rollout, Software generated plans will be followed by all ships and lock operators,
Communications/Messaging system is available, aceptability problems, needed regulations, …
Roadmap
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Conclusion
• There is seen a high optimization potential for reduced travel times as well as
automatic planning support for chains of locks.
Optimization Potential
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Thank you!Lock scheduling optimization for a chain of locks