A Multiagent Framework for Integrating Revenue- Creating Control Processes Klaus Weber Lufthansa...

A Multiagent Framework for Integrating Revenue-Creating Control Processes

Klaus Weber Lufthansa SystemsJunqing Sun Civil Aviation Institute of ChinaZhaohao Sun Bond University, Australia

AGIFORSReservation & Yield ManagementStudy Group Annual MeetingBerlin 16 – 19 April 2002

18 April 2002Chart 2

Klaus Weber, Junqing Sun, Zhaohao Sun:A Multiagent Framework for Integrating Revenue-Creating Control Processes

Who is Who?

Klaus Weber– Lufthansa Systems

Berlin, Germany

Junqing Sun– Civil Aviation Institute of China

Tianjin, China

Zhaohao Sun– Bond University

Gold Coast, Australia

Special Thanks to

Johannes J. Bisschop

– Paragon Decision TechnologyThe Netherlands



Agenda

Motivation

Revenue-Creating Control Processes

Intelligent Agents and Multiagent Systems

A Multiagent Framework

Conclusions / Outlook



Agenda

Motivation







Motivation

I System performance depends not only on components but also on

– System structure

– Interrelationship among the components

I Integrated optimization of interdependent processes is better than independent optimization

I Some achievements in vertical integration of business processes

I However, usual situation at airlines:

– distributed hardware

– separated databases

– different organizational units

but

– many generally identical functions, e.g. forecastint, optimization

– use same data

Key idea



Motivation (cont)

Software engineering viewpointI Systems integration increases system complexityI Object-oriented paradigm

– is a powerful tool but not the most appropriate to deal with complexityI Agent-oriented techniques

– new means of analyzing, designing and building complex software systems

– Multiagent technology is a new paradigm for distributed decision making (i.e. problem solving)

Here we presentI Multiagent framework for integration of revenue-creating control processes

Fleet assignment, Pricing, Revenue management under one roof



Motivation (cont)

Badly-organized system disastrous result

Tower of Babel

Well-organized system sustainable result

Great Wall



Agenda

Motivation







Revenue-Creating Control ProcessesO&D Revenue Management System

Flight Schedule

PNR Data

Availabilities

Actual Data

Forecast Interface Database

Group Database

Optimizer Database

Fares Database

Forecaster

Optimizer

Data Loading

Forecast Kernel

Forecast Building

O&D Group Optimizer

O&D Optimizer

Availability Processor

Bid Price Server

Unconstraining

Exponential Smoothing

Induction Trees

Booking Class Optimization

Overbooking OptimizationFlight Schedule



Revenue-Creating Control ProcessesFleet Assignment

Internal Fare Data

ATPCO

MIDT

Market SizeForecaster

Market Demand

Flight Schedule

Average Revenue

ProfitabilityEvaluation

Model

Objective FunctionFleet

AssignmentAssignment

Connection Builder

Market Share Model

Spill & Recapture

Revenue & CostEstimation

Optimizer

Assignment Evaluation

Multinomial Logit Model

Parameter Calibration

Internal Fare Data

ATPCO

MIDT

Cost Model

Unconstraining

Simulated Annealing

Mixed Integer Program



Revenue-Creating Control ProcessesPricing

Internal Fare Data

ATPCO

MIDT

Market SizeForecaster

Price ElasticityModel

CompetitorFare Actions

Market Change

Auto Matcher

Product Change

Alternative Analyzer

Competitor ReactionAnalyzer

Traveller PreferenceAnalyzer

Market StimulationModel

Cost Estimation

Revenue Estimation

Cost Model

Booking Class Optimization

Multinomial Logit Model

Parameter Calibration



Revenue-Creating Control ProcessesDifferences & Commonnesses

– Not all databases are commonly used.

– Each system comprises some specific functions

RM

Pricing

FAI Differences



RM

Pricing

FA

Revenue-Creating Control ProcessesDifferences & Commonnesses (cont.)

RM

Pricing

FAI Commonnesses

– Some databases are used by all systems.

– Each system comprises functions which are partof the other systems as well.

– Each system is composed of inter-related sub-systems. Each System is hierarchical.

– Some sub-systems are hierarchical themselves.

– Relationship between sub-systems varies, e.g. peer, client-server, team.

– Whether system components can be considered primitive or complex depends on the viewpoint.

– It is possible to distinguish between interactions among sub-systems and the interaction between sub-systems.

Each system is decomposable to some degree.

C o m p l e x S y s t e m



Revenue-Creating Control ProcessesComplexity

Canonical Complex System [Jennings & Wooldridge 2000]

Sub-systemSub-system componentComposed ofFrequent interactionInfrequent interaction



Revenue-Creating Control ProcessesIntegrated View

I Optimization

– Method 1, Method 2, ..., Method n

I Forecasting


I Parameter Estimation


I Data Pre-Processing


I Other algorithms

– Specific (complex) calculations

– Auxiliary calculations

Model specific



Agenda

Motivation







Intelligent Agents and Multiagent SystemsWhat is an Agent?

I Agents can perform some activities autonomously.At a minimum, they must be able to carry out some instructions ... without the help of other agents. Additionally, they may be able to make decisions of various levels of complexity on their own.

I Agents are part of a community.No agent is an island. When agents co-exist in a community, although some may exhibit a very high degree of autonomy, they are never truly independent of the other agents because they share an environment and therefore may compete for resources, whether intentionally or not.[Hayes1999]

I ... we define an agent as referring to a component of software and/or hardware which is capable of acting exactly in order to accomplish tasks on behalf of its user.

I ... it isan umbrella term, meta-term or class, which covers a range of other more specific agent types ...[Nwana1996]



Intelligent Agents and Multiagent SystemsWhat is an Agent?

What is Agent-Oriented Software? [Wooldridge1997]

I ... an agent is an encapsulated computer system that is situated in some environment, and that is capable of flexible, autonomous action in that environment in order to meet its design objectives ...

I Agents are

– Clearly identifiable problem solving entities

– Situated in a particularly environment

– Designed to fulfill a specific role

– Autonomous

– Capable of exhibiting flexible problem solving behavior



Intelligent Agents and Multiagent SystemsWhat is a Multiagent System?

What is a multiagent system? [Sun2001]

I A multiagent system is a group of agents that work together to find answers to problems that are beyond the individual capabilities of knowledge of each agent.

I A multiagent system (MAS)

– is a loosely coupled network of agents,

– (which can be distributed over several computers)

– that communicate together

– to accomplish individual and/or common goals,

– which cannot be solved by one single agent alone.

– MAS do not have global system control over the agents,

– usually uses decentralized data.

– Communication between agents mainly takes place asynchronously.



Intelligent Agents and Multiagent SystemsWhat is a Multiagent System? (cont)

Questions

I Which agent roles?

I Which interactions between agents?

I Which acquaintances between agents?

I Which system structure?

To be cleared later



Intelligent Agents and Multiagent SystemsComplex Systems and Agents

Integration of Revenue Management, Fleet Assignment and Pricing results in a complex system.

Are multiagent systems the appropriate paradigm? Paradigm

shift?

Principle mechanisms to manage complexity

I Decomposition

– divide problem, deal and solve parts in relative isolation

I Abstraction

– define simplified model, emphasize some details,suppress others

I Organisation

– identify and manage inter-relationships betweenproblem solving components

limit thedesigner’sscope

Software Engineering Viewpoint



Intelligent Agents and Multiagent SystemsComplex Systems and Agents (cont.)

Decomposition

I sub-systems work together to achieve functionality of their parent system

I localisation and encapsulation

I interactions occur at unpredictable times, for unpredictable reasons, between unpredictable components

multiple, interacting, autonomous components

Abstraction

I minimize semantic gap between units of analysis and the constructs in the solution paradigm

I system sub-system Decomposition

I interplay between sub-systems= high level social interaction

I agents cooperate to achieve common objectives

Organization

I broad variety of relationships

– peers control hierarchies

– short-term ongoing

I relationships may frequently change

organizations are first-class entities in agent systems

MAS MAS MAS



Intelligent Agents and Multiagent SystemsMultiagent Systems and OO Software Engineering

Important differences

I Objects are generally passive in nature

I Objects encapsulate state and behavior - they do not encapsulate behavior activation (action choice)

I Object-orientation fails to provide an adequateset of concepts and mechanisms formodeling such systems

I Object-oriented approaches provideminimal support for structuringcollectives.



Agenda

Motivation







A Multiagent FrameworkAgent Roles

I Main parts in software architecture [Perry1992]

– Data elements

– Processing elements

– Connecting elements

I Agent roles

– Coordinating agents

– Dispatcher agents

– Control agents

– Worker agents

e.g. observe forecast,register forecast changee.g. compute EMSR curve,solve linear program

Agent interaction (next slide)OptimizationForecasting

e.g. observe booking level,register deviating booking trende.g. perform unconstraining,train induction tree

Parameter Estimation

e.g. analyse system state,register significant system changee.g. perform maximum likelihood estimation,compute weights by least squares fit

Data Pre-Processing

e.g. detect PNR update

e.g. extract itinerary information from PNR

Other algorithms

e.g. detect capacity change

e.g. calculate spill and recapture

agents

agents

Databases



A Multiagent FrameworkAgent Interaction

I Dispatcher agents

– Keep track of idle worker agents

– Divide computational work effectively

I Coordinating agents

– Gather status information from agents below them in the hierarchy

I To be defined

– Interdependencies and interrelations of roles

– Agent interactions: messages

– Message priorities (e.g. highest priority for user interference)

– Who? With whom? How?

– Not: When?



A Multiagent FrameworkSystem Overview

DB 10DB 9

DB 8

DB 7

DB 3

DB 4

DB 5

DB 1

DB 2

DB 6

Dispatcheragent

Dispatcheragent

Dispatcheragent

Dispatcheragent

Coordinatingagent

Coordinatingagent

Coordinatingagent

Coordinatingagent

Controlagent

Controlagent

Controlagent

Controlagent

Controlagent

Workeragents



A Multiagent FrameworkTools

I Several development tools for agent-oriented software available

– Commercial tools

– Research prototype

http://www.agentbuilder.com/AgentTools

BritishTelecom

AgentOrientedSoftware

ParagonDecisionTechnology

I Toolbox

– Libraries including functional agent components

– Predefined coordination and organisation relations

– General planning and scheduling mechanisms

– Editors

– Visualisation tools



Agenda

Motivation








I Vertical integration requires adequate software engineering techniques

Multiagent Framework

I Revenue management, pricing, and fleet assignment systems have all characteristics of complex systems

I From a software engineering viewpoint the new paradigm of multiagent technology is advantageous for development of complex systems.

I We introduced agents, multiagent systems, agent roles and agent interaction both generally and with respect to revenue management, pricing and fleet assignment systems.

I Special development tools already exist.



Outlook

I Further characterise agents within the framework

I Develop a prototype using multiagent technology

I Study on integration of existing systemsusing multiagent technology

I Study on integration of additional processese.g. processes in airline operations control



Thank You for Your Attention!Any Questions?

[email protected]

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