International Conference on Dynamics in Logistics, August 28th - 30th, 2007, Bremen, Germany
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Transcript of International Conference on Dynamics in Logistics, August 28th - 30th, 2007, Bremen, Germany
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International Conference on Dynamics in Logistics,August 28th - 30th, 2007, Bremen, Germany
Dynamic Decision Making on Embedded Platforms in Transport Logistics –
A case study
Reiner Jedermann, Luis Javier Antûnez Congil, Martin Lorenz,Jan D. Gehrke, Walter Lang and Otthein Herzog
Institute for Microsensors, -Actuators and -Systems IMSAS
Center for Computing Technologies – TZI
IM SASIM SAS
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Outline
Introduction
Decentralized route planning
Agent-based shelf life supervision
Extension for multi package problem
Experimental evaluation
Summary and future work
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Introduction
Shifting intelligence from central control to transport containers
Complexity and cost pressure in supply chains forces new approaches
Individual planning for each palette / freight item- Transportation of perishable goods- Setting with high amount of data per cargo for monitoring- Unexpected changes in product quality may force re-planning
(change of vehicle and/or destination)- Vision: intelligent cargo- Current hardware solution on vehicle/container level
Two points of view- Planning for full truckloads (existing demonstrator)- Combined planning for part loads (simulation of new concept)
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Bremen
Berlin
Agent fortropical fruits
Recommended temperature overstepped.
Shelf life reduced to 24 hours.
Currently planned route
requires 36 hours
Search for alternatives / rescue plan
Autonomous transport supervision
Individual software agents to supervise each freight item
Adapts to individual requirements of the loaded goods
Asses the influence of deviations of the environmental parameters (temperature) to the freight quality
Triggers re-planning if some risk is detected
Current solution: freight quality evaluation within vehicle/container
Vision: Intelligent Cargo
PlanningAgent
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Autonomous Transport Scenario
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Route Planning
Autonomous routing for perishable goods has to consider shelf life criteria and dynamic environments, e.g., (unexpected) quality changes
Routing problems: TSP, VRP, VRPPD, VRPTW
(Optimal) routing solutions are NP-hard in general
- Constrains dimensions of maximum problem space
- Limits practicability for embedded systems
Cost function with shelf life is subject to information privacy concerns when using external routing services
Heuristic (sub-optimal), cooperative (distributed) approaches needed
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Software Agents
What are agents?
‘‘An agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives” (Jennings & Wooldridge 1998)
Autonomous agents act without direct intervention of others
Multiagent systems: agents communicate and cooperate to solve complex problems that are beyond the capability of a single agent
Pro
pose
Accept/R
eject
A1
A2
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Software Agents
What are agents?
‘‘An agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives” (Jennings & Wooldridge 1998)
Autonomous agents act without direct intervention of others
Multiagent systems: agents communicate and cooperate to solve complex problems that are beyond the capability of a single agent
Multiagent system architecture and communication is standardized by the IEEE Foundation for Intelligent Physical Agents (FIPA)
FIPA multiagent runtime environments: JADE and LEAP
Agent architectures: e.g. BDI, goal-oriented architecture, with autonomous goal selection (deliberation), and means-end reasoning
Pro
pose
Accept/R
eject
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A2
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Autonomy in Software Agents
Levels of Autonomy (Timm 2006) Strong regulation: No autonomous capabilities; every decision is
determined by external entities (reflex agent architectures).
Operational autonomy: Competence to choose course of action in predefined strategic boundaries(goal-oriented architectures, means-end reasoning).
Tactical autonomy: Enables the system to deliberate on different alternatives for operational behavior (BDI architectures, deliberation).
Strategic autonomy: Conventionally determined by the system designer (desires and algorithms). Beyond classic BDI architecture.
Autonomy in Case Study Local vehicle agent has operational autonomy: route selection
Possibly tactical autonomy: customer/cargo preference adaptation
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Example shelf life (Lettuce)
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Temperature °C
Sh
elf
life
/ lo
ss in
day
s .
Shelf life(T)
Loss per Day
Reference temperature 6 °C
Tripple speed of quality decay at 14 °C
Activation energy for Lettuce
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Local processing
Sensor raw data
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0 10 20 30
T (°C)
KQ
(d
ay
s)
TransportOperator
QualityModelling
StandardT&T
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0 10 20 30
T (°C)
KQ
(d
ay
s)
TransportOperator
QualityModelling
Standard T&T + Processor
QualityInformation
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Implementation of agents on embedded systems
Software agents on embedded systems ARM Processor 1 Watt @ 400 MHz Embedded real time JAVA runtime environment Implementation of a reduced agent platform (JADE –
LEAP)
Adapt agent systems to the restrictions of embedded systems: Power, memory and computation resources
100%
< 5%
~0.1%<< WSN
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Hardware
RFIDReader
Local Pre-Processing
SensorNodes
ExternalCommu-nication
FreightObject(RFID)
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Agent Transmission process
Intelligent Agent
Transport- and handling- instruction Supervision in behalf of the owner
Logistical object Passive RFID-Label
Intelligent truck or container
CPU platform Sensors RFID-Reader
Reading RFIDat loading
Truck requests agent at loading
Dynamic link
RFID
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Evaluation of sensor data
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0 10 20 30
T (°C)Sh
elf
Lif
e (
da
ys
)
Humidity
Quality-Index
Temperature
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Multiple package problem
Setting: Truck contains several pallets of perishable goods for different destinations. In which order should the destinations be served to deliver the goods before
expiration?
Additional requirements High local temperature deviations force individual supervision Simply multiplying the number of agents also multiplies the amount of
communication Truck / Container has to find a route that serves the individual needs of the
majority of all loaded packages
Planned improved solution Extension towards the current demonstrator software of intelligent container Idea: Reducing communication by shifting part of the route planning into the
means of transport Simulation Further improvement by increasing the level of autonomy
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1.5
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1.5
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4.87°C
4.01°C
5.06°C
3.97°C
3.33°C
5.11°C3.08°C
1.71°C
2.60°C
1.79°C
1.40°C
2.64°C
0.70°C
1.77°C
0.49°C
Temperature along the xyz-axis
Average of reefer side ~2 °C colder than other side
Single loggers behave 'chaotic'
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The test case
Extension of the Traveling Salesman Problem Not shortest way, but minimize shelf life losses by route planning Dynamic form: unexpected changes of shelf life and traffic jams
Item Nr
Desti-nation
Initial
Shelf life
1 Town 7 12 hours
2 Town 3 50 hours
3 Town 1 36 hours
… … …
… … …
Distance Town
1
Town
2
Town
3
…
Town 1 - 5 hours 7 hours …
Town 2 5 hours - 3 hours …
Town 3 7 hours 3 hours - …
… … … … …
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Distributed Planning by truck agents
Route Planning Agent (RPA) Remote Server Access to road maps and traffic
information Public information
Local Vehicle Agent (LVA) Embedded System (Truck) Evaluates Shelf life Private information
Goal fulfillment Maximize sum of remaining shelf life
at delivery Strongly avoid zero shelf life /
expired products
Request route proposals
Set of suggestions with low driving time
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Experimental evaluation
Distributed heuristic solution Software simulation Comparison with optimal solution
Process repeated in each town Unit: Travel distance in hours
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First planning step
Shortest round trip through remaining towns
Some decision alternatives
Starting Point
Current Position
Continue counter
clock wise
Continue clock wise
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Experimental evaluation 2
Replanning Change of planned route in step 2 caused by new information Caused by new route suggestions or Changed shelf life / traffic situation
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First Suggestion (84.6 points)
Final Route (269.2 points)
Starting Point
Re-Planning
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Experimental evaluation 3
Comparison to optimal solution In most cases solution close to optimum But hard to find if big difference between short route and optimal solution
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Heuristic Solution (269.2 points)
Optimal Route (276.1 points)
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Results
Summary of experimental results 600 “runs” with identical town-map and random initial shelf life values The points give a measure for the remaining shelf life at delivery. In 2/3 of all experiments the same number of packages had sufficient
remaining shelf life at delivery as in optimal solution (Row A) In average the remaining shelf life was 92% of the optimal possible value In the remaining 1/3 of experiments more packages as in the optimal solution
had zero shelf life (“lost packages”) at delivery (row B)
Runs Local planning Optimal Ratio
A (no losses) 402 252,73 points 272,02 points 92,62% ± 7,37
B (with losses) 198 More package losses as optimal solution
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Summary and Future work
Case study for an autonomous logistic process Reduced
communication costs Lower computation
resources needed Continue locally if
communication fails Privacy Higher degree of
autonomy by enhanced architecture to change strategy if required (replacing software components on request)
Simple HeuristicLVA
Evaluation of Solution
Acceptable?LVA
Change of planning strategy
Time window based route optimization
RPA
No
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The End
Thanks for your attentionwww.intelligentcontainer.com
ContactDipl.-Ing. Reiner JedermannUniversität Bremen, FB1 (IMSAS), Otto-Hahn-Allee NW1, D-28359 Bremen, GermanyPhone +49 421 218 4908, Fax +49 421 218 4774 [email protected]
Dipl.-Inf. Jan D. GehrkeUniversität Bremen, FB3 (TZI), Am Fallturm 1D-28359 Bremen, GermanyPhone +49 421 218 8614 , Fax +49 421 218 [email protected]