Post on 14-Dec-2015
Passengers steering trainsA Multi-Actor Approach for Operations and Control in the Netherlands Railways
Niels R. Faber*, René J. Jorna*
Erwin Abbink†, Ramon Lentink† & Fred van
Blommestein*
* University of Groningen† Netherlands Railways
Outline
› Introduction› Research questions› Method› Case 1: initial MAS› Case 2: statistically simulated passengers› Discussion
MAS projects at RUG/EB/IMS › Simulatie projectmanagement bij Ministerie OCW (1990):
Gazendam› Computational Transaction Cost Economics (Tomas Klos,
2000) Jorna/Gazendam› Multi-Actor SOAR (Hans van den Broek, 2001):
Gazendam/Jorna› The Social Cognitive Actor (Martin Helmhout, 2006):
Gazendam/Jorna› Simulation of Crowd and Riot Control (Nanda Wijermans):
Jorna/Jager› Transportbesturing door Smart Agents (MAS@NS) (2007)
Helmhout, Gazendam, Jorna & Faber. MAS@NS: Transportbesturing door Smart Agents
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Introduction
› Netherlands Railways› Train timetable specifies
planned train movements
› However:delays & disruptions occur
Introduction
› Handling delay & disruption• Dispatching task• Specific type of planning & control
› Objective:• Restore train movements according to train
timetable as quickly as possible
Introduction
› Dispatch task:• Solving a logistical puzzle
› Available means:• Train level:
• Speed of train (faster / slower)• Direction of train (reversing movement)• Action of train (activate / cancel train)
• Between trains level:• Holding trains at station for transfer of
passengers
Introduction
› Dispatch task organization:
Control center
Node coordinatio
n
Network control
Train shift control
NSRProRail
Central control
Local control
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Introduction
› Dispatch task knowledge:• Railway network• Train timetable in controlled region• Contact information train drivers and ticket
inspectors
Introduction
› Main foci of dispatch task• Restore train timetable• Balance moving material• Balance personnel
Introduction
› But what about passengers travelling by train?• Are they considered in dispatching?• How are demands and wishes of passengers
included in dispatching solutions?• What does it mean to give passengers a
voice in dispatching?
Introduction
› Types of control• Input oriented control
•Dominant: budgets• Process oriented control
•Dominant: production planning, utilization of capacity
• Output oriented control•Dominant: costs and revenues per
product, marketing
Introduction
› Desire to incorporate passenger demands in dispatching• Currently, implicit consideration in handling
delays / disruptions• No formal role in decision process of
choosing dispatch solution• No coordination structure for incorporation
in dispatch task
Research questions
› What is the intended role of passengers in dispatching? (informing / deciding)
› What organizational structure supports the involvement of passengers in dispatching?
› What knowledge about passengers needs to be included in dispatching?
› What knowledge should passengers provide if they participate in dispatching?
Method
› Developments in thinking about planning and organization• System theory -> Cognition• Top-down -> Bottom-up• Logic -> Computational Mathematics• Fixed task distribution (DAI) -> learning
systems (MAS)
Method
› Human cognition• Fayol as forerunner• Simon, March• DSS movement: The user at the driving
wheel• KADS
(Henk Gazendam, 2007)
Method
› Top-down -> bottom-up• Anthony: Hierarchical model planning• Lindblom: The science of muddling through
•Planning cannot proceed until consensus is reached
•Technically acceptable solutions -> Socially acceptable solutions
• Mintzberg: The Rise and Fall of Strategic Planning•Emergent strategy
(Henk Gazendam, 2007)
Method
› Logic• Closed worldview• Problems with processing of changes and
time• Logic other than first order logic large
computational complexity
(Henk Gazendam, 2007)
Method
› Computational mathematics• Emergence (Holland)• Coherence (Thagard)• Production of complex systems (Wolfram)• Evolution (Gigerenzer, Dennett)
(Henk Gazendam, 2007)
Method
› DAI -> MAS• From fixed task distribution (DAI) to learning systems
(MAS)• DAI: Distributed Artificial Intelligence
• Actors are not autonomous• Actors only able to execute specific task• Top-down, hierarchical coordination model
(contract net protocol)• Communication limited to task execution• Fixed task distribution and fixed specialization of
actors
(Henk Gazendam, 2007)
Method
› Multi-Actor system (identified as promising technique)
› Actor• Autonomous• Communicative ability
• Ontologies• Protocols
• Task execution ability• General problem solving methods
• Recognition of task environment• Search in problem spaces (weak methods)
• Learning ability• Exploration and imitation• Optimization (neural net)• Evolutionary learning (variation and selection)
Method
› Actors can be:• People• Active computer programmes meeting certain
conditions (agents)• “An agent is a computer system, situated in
some environment, that is capable of flexible autonomous action in order to meet its design objectives” (Wooldridge, 2002, p.15)
• Organisational units represented by a human actor or computer agent
Method
› Characteristics of a MAS (Jennings et al., 1998)• Each agent has incomplete information, or
capabilities for solving the problem, thus each agent has a limited viewpoint.
• There is no global system control• Data is decentralized• Agents communicate through messages• Patterns of messages can be specified in
protocols(FIPA: Foundation for Intelligent Physical Agents)
Method
› Agent task execution:• Tasks are executed through behaviours• Types of behaviours from simple to
complex, composite• Various models to shape behaviour, e.g.:
•Utility•BDI
MethodSocial abilities
Individual abilities
Individual knowledge Social knowledge
Distributedsystems
Multi-agentsystems
Control knowledgeSolution plansWorld mapsBehaviorsresources
RoleCommitments, beliefsProtocols, primitives
Distributedproblem-solving
CooperationCommunicationInteraction
PlanningNavigation & obstacle avoidanceTask solvingSecure mechanisms, perception
Cooperative agents
Autonomous agents
(Glaser, 2002)
Method
› For Netherlands Railways: MAS used to explore organizational structuring when incorporating passengers in dispatching task
› Human actors and software agents collaborate to solve problem
Case 1: initial MAS
› Objective:• develop a MAS prototype that enables
passenger involvement in dispatching in situations of delays and disruptions of the train timetable
• prototype is a useable simulation platform for future simulations
Case 1: initial MAS
› Exploration of;• organizing for retrieval of desires and
demands from passengers
• consequences of delay scenarios
› Using real-time passenger agents
Case 1: initial MAS
› Methodology:• Prometheus Design Tool
(http://www.cmis.rmit.edu.au/agents/pdt)• UML
Case 1: initial MAS› Main agents:
• Planner• communication with dispatcher• handling disruptions and delays
• TravelManager• handles travelling information• message forwarding to/collection from passengers
• CustomerTravelCoach• aids passengers with travel plan selection
• TravelAssistent• communication with passengers
• SecurityAssistent• handling subscription of passengers
Case 1: initial MAS
› JADE characteristics• agents have behaviours• agents communicate through messages• protocols fix specific messages passing
patterns• ontologies available to specify valid message
content• communication between agents follows
communication act theory• BDI possible through JADEX extension
Case 1: initial MAS› Scenario:
• Disruption: tracks between Haren and Zwolle• Cause: leaves on tracks
Case 2: statistically simulated passengers› Extension of initial MAS› Inclusion of statistical data to:
• generate more realistic payloads of trains (passengers)• shape statistically based passenger agents with
characteristics for:• travelling motive• ticket type• travelling frequency• departure-destination combinations• travel plan
Case 2: statistically simulated passengers› Objectives:• extend MAS with passive passenger agents• integrate statistical data about passenger
movements• prepare alternative disruption / delay
scenarios• coordinate passenger responses• plan for empirical testing
Case 2: statistically simulated passengers› Additional agents:
• StatisticalPassenger• simulates one passenger in a train based on statistical
data• StatisticalManager
• handles statistical data• CommunicationManager
• transforms information about delayed trains into StatisticalPassenger agents
TravelManager
StatisticalPassengerStatisticalPassenger
CommunicationManager StatisticalManager
Statistical data
Statistical data
Case 2: statistically simulated passengers› StatisticalPassenger
• Station of departure• Station of destination• Ticket type, travel motive, travel frequency,• Travel plan (route and schedule)• Responds to messages about delays and disruptions from
TravelManager
Case 2: statistically simulated passengers› Coordination of reactions of passengers• TravelManager agent is enhanced• TravelManager needs to send one clear
message to the dispatcher (through the Planner agent)
• Collect responses from real-time passengers• Collect responses from StatisticalPassenger
agents
Case 2: statistically simulated passengers› Current status:• Statistical passengers are created• Behaviour of passengers needs to be
implemented• Behaviour of TravelManager agent for
collecting and handling responses from real-time and statistical passengers needs to be implemented