Knowledge Genesis Group & Smart Solutions Petr Skobelev Multi-Agent Technology: Ideas, Experiments and Industrial Applications Ekaterinburg, 12-13 May

Knowledge Genesis Group & Smart Solutions

Petr Skobelev Multi-Agent Technology: Ideas, Experiments

and Industrial Applications

Ekaterinburg, 12-13 May 2011

Small, but coordinated forces, produce magic. Prof. A. Konovalov.Lectures on supramolecular chemistry

Agenda

Introduction Key Challenges of Real Time Economy Multi-Agent Technology First Experiments with Multi-Agent

Solutions Industrial Applications in Real Time

Scheduling Future

Knowledge Genesis Group

Started 1997, Samara, Russia Originally from Russian Academy of Science and Aerospace Industry 15+ years of experience in Multi-agent systems and Semantic web Expertise in application development, large-scale systems, web-applications, GPS navigation and e-maps, data bases, mobile solutions 100+ J2EE and .net programmers Knowledge Genesis Group companies:

Magenta Technology (UK) - 2000 Knowledge Genesis Germany – 2008 Knowledge Genesis UK – 2009 Emergent Intelligence, USA – 2010 Smart Solutions, Russia– 2010

Advanced technology & product vision for solving complex problems Own development platform International network of partners Strong links with universities

In Samara Office of Magenta Technology (UK)

616

33 3549

59

85 89

120

154

0

20

40

60

80

100

120

140

160

180

200

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Years

Num

ber o

f peo

ple

Prof. George Rzevski (Open University, UK) and Prof Vladimir Vittikh (Institute of Complex Systems of Russian Academy of Science) Company Growth (Number of Employees)

15 June 1990 – The beginning …

Key Challenges of Real Time Economy

Uncertainty, Complexity & Dynamics of business are growing Clients, partners & resources demand more individual approachHigh efficiency of business requires to become more open, flexible and fast in decision making Solutions for Real Time Decision Making can help to optimize resources, balance and reduce cost & time, service level, risks and penalties

Activity-Based Cost (ABC) model is required to analyze options and provide dynamic pricing in real time

Pro-actively negotiate with clients and resources “on the fly” Solutions need to support not only optimization of resources but also

provide opportunities for business growth, learning and adaptation Use full power of Internet services, GPS navigation, mobile phones,

RFID, etc

New generation of software solutions for smart decision making support and sophisticated user interaction

is required on the market!

Multi-Agent Technology Differentiation

Hierarchy of programsSequential ProcessingTop-down instructions Centralized Data-driven Predictable Stable Reduce Complexity Full Control

Networks of agents Parallel ProcessingNegotiations & Trade-Offs Distributed Knowledge-Driven Self-Organization EvolutionThrive with Complexity

Managing growth

Traditional Systems Multi-Agent Systems

Modules are working as a co-routines simultaneously

Distributed Approach Wins!

The Beginning of Multi-Agent Systems

Started in the beginning of 1970’s … Based on achievements in Artificial Intelligence + Object-

Oriented and Parallel Programming + Telecommunications

Traditionally focused on logic reasoning (Wooldridge, etc) Our approach is bio-inspired (Van Brussel, Paulo Letao,

etc) but strongly influenced by: Ilya Prigozhin in Physics (auto-catalytic reactions), Marvin Minsky in Psychology (society of mind), Artur Kestler in Biology (holonic systems)

Key focus: self-organisation and evolution, synergy, non-linear thermodynamics, collective (emergent) intelligence

First Applications: Internet e-commerce Current Applications: logistics, data mining, text

understanding, etc Future: Web-Intelligence

Classification of Agents

Agent Type Simple Agents Smart Agents Intelligent Agents

Truly Intelligent Agents

Autonomous execution

Communication with other agents and users

Monitoring of environment

Ability to use symbols

Problem Domain Knowledge

Goals and Behavior

Adaptive Learning from Environment

Tolerant Reaction to

Input Errors

Errors Processing Real Time

Natural language

Current FocusCurrent Focus

How Agents work?

A new agent is created at runtime whenever there is a task to be performed

The agent begins its life by analysing the task and studying rules of engagement

Agent activities include: analysing situation composing messages receiving & sending messages to other agents or humans interpreting received messages deciding how to react acting upon their decisions

This enables agents to run concurrently When an agent completes its task it is

destroyed

Examples of Multi-Agent Systems

• Winestein Technologies – http://www.weinstein.com• NuTech – http://www.nutech.com• Living Systems – http://www.livingsystems.com• AgentBuilder - http:// www.agentbuilder.com • Quarterdeck - http:// arachnid.qdeck.com• GeneralMagic - http://www.genmagic.com• Intelligent Reasoning System - http://members.home.net:80/marcush/IRS• BiosGroup – http: www.eurobios.com• LostWax – http://www.lostwax.com

About 30 companies on the market.More than 100 University projects are known.

Existing Multi-Agent Systems

Single-Agent Approach – no self-organization Based on results of traditional AI research (Prolog-

style deductive machine for reasoning) – not effective for dynamical environments with high uncertainty

Concept of Mobile Agents: problems with security Traditionally Oriented on e-Commerce Do not have Knowledge Base and Reasoning Tools to

support Decision Making Processes of End-Users Do not have Re-Negotiations support Memory intensive and slow – low performance, only a

few Agents can work on server in parallel Not supported with development tools (basic

platforms only) - very expensive and difficult to design & develop

Our Approach: Main Ideas

Our Multi-Agent Systems working in Swarms consisting of a large number of small autonomous programs (objects) called Smart Agents

Smart Agents have special in-built tools for decision making and ontology-based scene support

Main feature of Smart Agents is the ability to solve complex problems through negotiations

Every complex problem can be solved by self-organization and evolution, in competition and cooperation of Smart Agents

Examples: real time logistics, pattern recognition, text understanding, data mining, etc

Demand and Supply Matching on Virtual Market Engine - is Core Part of Real Time Multi-Agent Solutions

for Any Type of Complex Problems

Virtual Market Engine

D S

D S

D S

D S

S

S

S

D

S

S

D

D

S

D

D

D S

Demand-Supply Match

Demand Agent

SupplyAgent

MatchContract

Swarm: Demand and Supply Networks

Our Approach: Main Ideas

Software Agents model physical objects, people and abstract concepts forming Virtual Markets in which they allocate supply to demands

The agent interaction is based on the free-market model – Demand Agents purchase resources from Supply Agents and Supply Agents sell resources to Demand Agents, all working concurrently Logistics: orders to resources Text understanding: words to meanings Data mining: records to clusters

Agents learn how to accomplish their tasks by accessing Ontology where they consult the detailed knowledge of the domain in which they work

Ontologies Ontology by definition is “knowledge as it is”

or “conceptualisation of abstraction” (Gruber) Knowledge can be represented by semantic

network of concepts and relations OntologyEditors: OntoEdit, WebODE,

WebOnto, Protégé-2000, OWL/RDF/RDQL Our ontologies are used for pecification of

situations (scenes) Ontologies are the combination of declarative

semantic network and operational knowledge (scripts)

Concepts and relations can represent objects, roles, properties, processes, attributes, etc.

Some ontologies can be hard-coded to improve performance

Example of Ontology /Scene

Multi-Agent PlatformMulti-Agent Platform

• Java-based / .net• Peer-to-peer architecture• Scalable/Robust• Strong visualizations• Desk-Top & Web-Interface

Knowledge BasedDecision Making

EnterprisePlatform

MAS

driven by

Inner virtual

market

• Based on Semantic web innovations• Ontology to capture Enterprise

Knowledge and keep it separately from source code

• Decision Making Logic instead of rules

• Able to Learn in Future (Using Pattern Discovery module) - source codes separated from knowledge

• Adaptive, Real time and Event-driven • Swarm-based approach (vs mobile

agents)• Virtual Market as a Core engine• Highly Reactive & Pro-Active• Provide Emergent Intelligence

Smart Clash Analysis for Airbus Wings

Semantic network: scene of wing

Part B

Part D

Part C

Part A

Is-Assembly

Can-Rotate

Fix-LinkFix-Link

1. When change happen (let’s assume that in our example it is size of part C) we create agent of changed part

2. This agent will investigate scene and find his neighbors (Part B)

3. Agent of part C will create agent of part B and to inform him on changes and his new boundaries

4. Agent of Part B will compare new size or position of Part C and will check his boundaries according with the type of relation

5. If these changes not affect his position – it will be recognized as the end of wave of changes.

6. If yes – the situation will be repeated for other neighbors of the network in the same way (Part A, Part E, Part D)

7. As a result of this process it will be ripple-effect from initial change which will take place until it affects positions of other parts

Agent of Part C

1, 2

Part E

34

Value for Client

• Analysis can be made in real time (and even for dynamically reconfiguring complex objects)

• The approach proposed can be applied for any complex object or machine without full re-programming (need another ontology and scene mainly and change of interpretation of links)

• Many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel for engineers

Agent of Part BRipple effect of Part C changes

Noble Group Solution: Smart Coal Mining in Indonesia

Semantic network: scene of

Noble Group network

Crane 4

Client 4

Barge 2

Is-Contain

Booked

1. When change happen (let’s assume that in our example it is bad Weather in Region A: heavy rain!) we create agent of region and send message about weather event.

2. This agent will use ontology for find out the consequences. Usually bad weather affects Jetty Loading Rate and Waterway availability. Then agent finds all affected instances of jetties and waterways in this region and inform them about bad weather.

3. Agents of all these objects will estimate impact and make changes in their schedules. This can leads to new changes in a network. For example due to the new jetty schedule some barge will be late on anchorage. Agent will inform his operator immediately and will present current options, for example barge will come 3 days late.

4. If this decision will be confirmed by operation, Agent of Barge will create agent of Vessel and FC and will inform them about delay, and they will check options and inform their operators if needed.

5. If these changes will not be possible to solve inside region and they will affect client – it will be needed to inform clients and it will be the end of wave of changes.

6. As a result of this process it will be ripple-effect from initial change which will take place until it affects positions of other parts

Agent of Weather in Region A

2

Region A1

Value for Client

• Team of managers can be coordinated in real time according with events coming

• The approach proposed can be applied for any team coordination without full re-programming (need another ontology and scene mainly and change of interpretation of links)

• Flexibility: many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel for users

Ripple effect of changes

Booked

Contract

Vessel 3

Jetty 1

Booked

34

Jarvis Solution: Smart Pattern Recognition

Semantic network: partially reconstructed scene during patterns recognition

1. Input image flow comes as binary digital photos taken on new landscapes with different configuration of patterns and high level of noise.

2. All agents of patterns start their work in parallel and compete because it is not known in advance where strong patterns will be recognized.

3. Looking into ontology all agents trying to make their best match with image fragments (and all of them can invoke some specific methods for this).

4. If for one of patterns matching is Ok then he adds object into scene specifying parameters of recognized pattern (lake, forest, etc) and links it with other objects.

5. If matching is not Ok (for example agents of house and cloud have conflict and are competing for the same fragment of image in brackets) – they need help and switch for cooperation based on domain semantics.

6. For this example agent of house will look in ontology and find out that usually there are garage and road near houses. Now he can investigate scene and will see that garage and road are already there.

7. Then probability of the fact that it is house (not a cloud) becomes higher because of this links (sometimes it is needed that garage and road will agree also that their neighbor looks like a house).

8. In this situation cloud also can know from ontology that it can move and will give priority to the house.

Agent of Road

Agent of Lake

Agent of Cloud

Agent of Garage

Agent of House

Agent of Forest

Garage

Road

Lake

HousePlaced-Near

Between

Value for Client

• Analysis can be made in real time or batch image processing

• The approach proposed can be applied for any complex image processing system for pattern recognition without full re-programming (need another ontology and scene mainly and low level methods of image processing)

• Flexibility of solution: many threads of activities can go in quasi-parallel mode starting from best recognized parts of image (unknown in advance)

• Quality of pattern recognition can be very high because of semantic links and errors checking during the process of recognition

• Proposed solution is very generic not only for image processing but also for text understanding and other applications (patterns of sense can compete for strings of texts, etc)

OmPrompt Solution: Smart Fax Recognition

Semantic network: partially reconstructed scene of fax recognition

Name

From… To …

Is-Header on the top of page

BelowBelow

1. This task has the same solution as for images considered above.

2. When new fax is coming agent of first pattern according with fax template starts looking his part of image. If he finds 100% matching – he writes results in scene and initiates next agent looking into scene of fax template.

3. But if matching is not 100% a few agents of this area can compete for the same part of the image (for example Osipemagen – it is wrong end of one field and wrong beginning of the new field).

4. Agent of first field will recognized that it is beginning of the address and will ask agent of the next field – do you recognize rest of the string as a company name connected with this address? In general the best one will try to get support from other with whom he can cooperate investigating his local area via relations.

5. Recognized part of image is saved in scene and is used by all other agents to detect next parts and find solution of conflicts.

Fax Number

Value for Client

• Analysis can be made in real time

• The approach proposed can be applied for any complex fax or image processing without full re-programming (need another ontology and scene mainly and change of interpretation of links)

• Flexibility: many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel

• Quality of fax (image) recognition can be very high because of semantic errors checking during the process of recognition

From … To …

Name

List of Items

List of Items

Price

Fax Number

Next in line

From … To …

Name

List of Items

List of Items

Price

Fax Number

Real fax

Fax template

VineWorld Solution: Smart Diet Management

Semantic network: Scene of Tuesday menu

1. When new event happen (let’s assume that in our example it is user request to replace Fish by Pork at dinner time) we create agent of changed object

2. This agent of Pork will replace Fish informing other agents in dinner group and agent of dinner.

3. Immediately agent of white wine (good with fish) will leave the dinner and agent of red wine will propose Dinner agent to enter the menu as a good match with user preferences.

4. Agent of Dinner will calculate calories and find out that now it is more than 2000 calories for a day.

5. To solve the conflict agent of Dinner will try to find candidates to reduce number of calories calculating the difference.

6. If it is not possible to solve the conflict inside dinner – it will ask agent of Tuesday menu – who else can be involved in this process. Maybe both other groups (lunch and breakfast) will be recommended to start looking variants in parallel.

7. All potential candidates will be asked to find nearest possible food option according with user preference and less calories.

8. All options will be not simply sorted and presented to user for final decision – but will compete to be recognized as a best option. Best possible option (remove ice-cream) can also switch to cooperation with other options to get more points.

9. As a result of this process a few food items can drop out of menu, or size of portion will be reduced or physical exercises will be added to menu to reduce extra calories.

10. In all cases it will be ripple-effect from initial change which will take place until decision is found or not

Value for Client

• Solution can be find in real time (and even during update of food items types)

• Solution is open for adding new types of services: health, exercises, fridge, etc

• Solution is flexible: changes can start from any point and run in parallel threads of activities

Bre

akfa

stLu

nch

Din

ner

Apple juice – 177 kcal

Omelet – 261 kcal

<empty>

Soup – 205 kcal

Pudding – 362 kcal

Ice cream – 450 kcal

Strawberry – 41 kcal

River fish – 216 kcal

White Wine – 192 kcal

Pork – 537 kcal

Total – 1904 kcal

Agent of Pork

Agent of Menu

Customer

! 50%

x

Ice cream 50%

Refuse omelet

Bicycle

Red Wine – 180 kcal

Change Wine

Agent of Dinner

Agent of Lunch

Agent of Breakfast

!

Total – 1988 kcalTotal – 2225 kcal

Smart Content: Semantic Network of Celebrities

Upload and specify new photos

Ontology of Celebrities

Ontology/Scene Editor

Add new photo and agents will change network

New Photo is added to Semantic Network

Text Understanding Projects

Intelligent Documents Classifier (Rubus/Aon) Classification of all documents into groups with

the similar sense - semantic proximity Ability to build the template document on the

base of the group of similar documents Intelligent Requests System

(Integrated Genomics) Intelligent search and comparison of the

abstracts’ semantic descriptors on the basis of the problem domain ontology

Database Natural Language Requests System (Hotel Booking) Intelligent partial matching on the base of the

ontology to make complex search of several interconnected items

On-line clustering analysis of customers types and their patterns of requests thus generating new rules to enlarge the ontology

MEDLINE Database - MEDLINE Database - Internet search for molecular biology abstracts

The MedLine database contains brief abstracts of articles on biological themes, which are presented to users free of any charge.

If the abstract of a found article is satisfies the user, he can order the full version of the article for a certain price.

Search conditions - keywords and logical expressions

Text Understanding ProcessText Understanding Process

Example phrase: MagentA will provide support for Software Programs employed by the Client.

Morphology stageMorphology stage

Syntax stageSyntax stage

Semantics stageSemantics stage

Text Understanding SystemText Understanding System

Two pUC-derived vectors containing the promoterless xylE gene (encoding catechol 2,3-dioxygenase) of Pseudomonas putida mt-2 were constructed. The t(o) transcriptional terminator of phage lambda was placed downstream from the stop codon of xylE. The new vectors, pXT1 and pXT2, contain xylE and the t(o) terminator within a cloning cassette which can be excised with several endonucleases.





Text Understanding SystemsText Understanding Systems

Intelligent Requests System statistics:Time to build one semantic descriptor ~ 1-2 min.Time to search through 1000 abstracts ~ 1 min.Ontology of problem domain contains ~150 concepts and ~3100 relations (with inheritance)

ResultsResultsIn “good” groups in

general accuracy of finding correct article is higher than 81%, in certain requests it’s almost 90%

In “bad” groups the probability of still good article put there by mistake is less than 8%


Intelligent Requests System statistics:Time to build one semantic descriptor ~ 1-2 min.Time to search through 1000 abstracts ~ 1 min.Ontology of problem domain contains ~150 basic concepts and relations

Comparison with Comparison with keywordskeywords

The proposed approach demonstrated significant quality increase comparing to keywords

Keyword search even with all improvements (synonyms etc) still demonstrates rather bad results, clearly insignificant to the required task

Accuracy of proposed search higher than simple keyword search

Multi-Agent Solutions for Real Time Resource Allocation, Scheduling and Optimization

Your solution & application?

MAT Solutions for Real Time Logistics

Truck SchedulingOcean SchedulingTaxi SchedulingCourier SchedulingCar Rental OptimizationFactory SchedulingAirport SchedulingWork forces ...

VOL: 10 PALLETSSLA: 10 DAYS

40%


80%


60%

20%20%

20%


60%

20%


120%60%

60%

100%

This order has a shortest journey route……but the capacity is not available on one of the legs.

This order has a shortest journey route……but the capacity is not available on one of the legs.

It is important to be able to assess alternate routes, to meet services levels and minimum cost.

It is important to be able to assess alternate routes, to meet services levels and minimum cost.

How It Works in Transportation Networks

Imagine the power of having a single system that can automatically plan and re-plan a network like this, as events occur, such as new orders being added or resource availability changes.

Imagine the power of having a single system that can automatically plan and re-plan a network like this, as events occur, such as new orders being added or resource availability changes.

Transport Logistics Network Complexity

Real-time scheduling with shrinking time windowsLarge & complex networks (> 1000 orders per day, > 100 locations, > 50 vessels )Less-than-Truck loads requiring effective consolidationNeed to find backhaul opportunitiesIntensive use of crossdocking operationsTrailer swapsNumerous constraints on products, locations, dock doors, vehicles: types, availability, compatibilityIndividual Service Level agreements with major clientsOwn and third-party fleetFixed and flexible schedulesDependent schedules (trailers, drivers, dock doors, etc)Activity Based Cost ModelOther client-specific requirements

Most of large & complex transport networks are still scheduled manually!

Pattern Discovery

Resulting Plan and KPIsAdaptive Scheduler

Input Events Flow (New order,

Resource unavailable, etc)

Network Designer

Ontology Editor

Simulator

Domain

Ontology

Network Configuration

& Situation specs (Scene)

Modeling Data (Flow of orders, fleet size, etc)

Patterns and Ongoing Forecast

Vision of MAT Scheduling Solutions

Current Situation and Ongoing Plan

Modeling Plan and KPIs

Domain Knowledge

Evolutional Design

Advise on How-To make Network More Efficient

Network Assets

& Real Situation

MAT Schedulers: Screens Example

Describe your classes of concepts and relations

Ontology as a Way to Capture Domain Knowledge

Client Order Cargo TI TIConsolidation Fleet Trailer

DD Trailer Standard

Truck:

Tractor Rigid

Dock Trip Location:

Cross Dock RDC

TI Operations: Collect Drop

Truck operation: Stop Move Idle

ClientHasOrderOrderHasCargoOrderHasTIFleetHasTruckFleetHasTrailer

Ontology concepts:

Ontology Relations:

TruckHasScheduleTIConsolidationHasTIJourneyHasTITIHasTIScheduleTIHasTIOperation

Examples of Concepts and Relations

Truck Logistics Scene Example

Scene objects: 27 clients 154 cargoes Fleet:

22 DD Trailer 12 Rigid Truck

72 locations: MANCH MILTO EXEBOTFR CHIPP CONIC YORFI PENRITFR …

Create a situation (scene)

Logic of Multi-Agent Scheduling

Truck 1

08:00 16:0012.00 20:00

Time

Order 1

Order 2

Order 3

•Existing schedule•New Order arrives•Pre-matching•New order ‘wakes up’ Truck 3 agent and starts talking to him•Truck 3 evaluates the options to take New order•Truck 3 ‘wakes up’ Order 3 agent and asks it to shift•Order 3 analyzes the proposal and rejects it•Truck 3 asks New order if it can shift to the right•Truck 3 decides to drop Order 3 and take New order•Agents of New Order and Truck 3 disappear•Order 3 starts looking for a new allocation and finally allocates on Truck 1 by shifting Order 1

Truck 2

Truck 3

New order

Which Truck looks like the best for me?

I can take New order if I:•Shift Order 3 to the left•Shift New order to the right•Drop Order 3

Can you transport me?Can you shift to the left?

My time window is too tight – I cannot shift

Can you shift to the right?

No

Next

Back

A

Consider business-network of a company

1.Order1 goes from Location C to Location Z2.Order2 goes from Location B to Location X3.Order3 appears, which goes from Location A to Location Z4.Order3 decides to go to B and then travel with Order 2 via cross-dock15.Order4 appears, which goes from Location A to Location Y6.Order3 decides to travel the first leg with Order 4 and the second leg with Order 1 via cross-dock 2, to avoid going alone from A to B

Cross dock 2

Cross dock 1

B

C

Z

Y

X

Next

Back

Logic of Multi-Agent Routing

Case Study: UK Logistics Operator

Network Characteristics:4500 orders per dayOrder profile with high complexity

• Many consolidations should be found• Few Full Truck Load orders• Few orders can be given away to TPC• Majority of orders require complex

planning – the price of a mistake is high

600 locationsLarge number of small orders3 cross docks9 trailer swap locations140 own fleet trucks, various types20 third party carriers

• Carrier availability time• Different pricing schemes

Key Problem: Real-time planning in a highly complex network with X-Docks and Dynamical Routing

Problems to be Solved:

Location availability windowsBackhaul ConsolidationVehicle capacityConstraint stressingPlanning in continuous modeDynamic routingCross-dockingHandling driver shifts

Case Study: UK Market leaderin supply chain management

Key Problems:Automatic Search for effective scheduling decisions using own fleetAdaptively distribute orders among the journeys of static schedule

Network Characteristics:Employs around 5,000 staff, rising to 7,000 during Christmas peakHas 40 operating sitesManages 300,000 sq m of warehouse spaceHas sites across EuropeHas a turnover of £400 million Moves in excess of £10bn worth of merchandise each yearServices over 3,000 retail outlets around the globeTravels 75m miles each yearOperates a fleet in excess of 1,300 vehiclesHas over 35 years of supply chain experience

Problems to be solved:Maximise utilisation of capacity – minimise need for ad hoc journeysComply with constraints – temperature regimes, collection and delivery times, customer priority, product compatibilities, product weight, etcOptimise trunking through best use of changeovers and cross docksDo not over split orders to prevent problems on reconsolidationMake best use of subcontractors versus own fleetMake best use of store returning vehicles

Summary of Benefits (Before / After)

BEFORE IMPLEMENTATION AFTER IMPLEMENTATION

Two operators worked for a dayto make a schedule for 200 instructions

Planning day 1 for day 3: no chance to Support backhauls and consolidations in real time

8 minutes to schedule 200 transportationinstructions

Planning day 1 for day 2 and even day 1 for day 1

No software for schedule 4000 ordersWith X-Docks and Drivers (manual procedure only)

Hard to consider various criteria quickly and choose the best possible option

4 hours to plan orders 4000 orders via X-Docks and ability to add new orders incrementally (a few seconds for a order)

Choosing the best route from the point of view of consolidation or other criteria

Knowledge was hard to share, it was “spread” among different experts

Capture best practice and domain knowledge in ontology. New knowledge can be inserted quickly.

Case Study: Taxi Dispatching (UK)

Network Characteristics:Call centre with about 130 operators receiving orders concurrentlyA fleet of more than 2,000 vehicles (each with a GPS navigation system)A very large number of orders: more than 13,000 orders per day; the order flow occasionally exceeds the rate of 1,500 orders per hour; order arrival times and locations are unpredictableThe order attributes are as follows: place of pick-up and drop; urgent or booked in advance (for a certain date and time); type of service (minivan, VIP, etc.); importance (a number from 0 to 100 depending the client); special requirements (pet, need for child chair, etc.)A large variety of clients, e.g., personal, corporate, VIPs, with a variety of discounted tariffs, with special requirements for drivers, disabled, requiring child seats, requiring transportation of pets, etc. A large number of freelance drivers who lease cars from the company and are allowed to start and finish their shifts at times that suite them, which may differ from one day to anotherAt any time around 700 drivers are working concurrently, competing with each other for clients Guaranteed pick up of clients in the centre of London within 15 minutes from the time of placing an orderUnpredictability of the traffic congestion in various parts of London causing delays and consequently the interruption of schedules, unpredictability of times spent in queues at airports and railway stations

Key Problem: Real-timeresource reallocation

Car 111

Order A pick-up

Car 222

Order BOrder A drop

Problems to be solved:

React on events in real timeProvide individual approach to clientsBalance costs vs time and risksIncrease efficiency of businessSatisfy drivers

Case Study: UK Corporate Taxi Company

Main Results: The system began its operation and maintenance phase in March

2008, only 6 months from the beginning of the project The total number of processed orders increased +7% (1000 orders

per day * 20 pounds cash in average) in a first month with the same number of resources

98.5 % of all orders were allocated automatically without dispatcher’s assistance

The number of lost orders was reduced to 3.5 (by up to 2 %) The number of vehicles idle runs was reduced by 22.5 % Each vehicle was able to complete two additional orders per week

spending the same time and consuming the same amount of fuel, which increased the yield of each vehicle by 5 – 7 %

Profitability Increase: +4.8% Orders collecting time: 40% faster Time for Operators Training: 4 times less ROI: 6 months

Key Customers

Avis (UK): Leading car rental provider• Real time scheduler for downtown market reducing car assets required and

improving service levels Addison Lee (UK): largest private hire car firm in London

• Operational system and real time scheduler for resource optimizationTankers International (UK): Manage a large oil tanker fleet

• Real time scheduler for tankers scheduling and optimizationOne Network (USA): logistics software provider

• Providing development services to implement new core, scheduling and visual features/components for their platform

GIST (UK): M&S supply chain• Real-time scheduler for increased fleet utilisation and reduced transportation

costsAirbus/Cologne University (Germany)

• Catering RFID scheduler for improving service level and airport efficiencyEnfora (USA) : major manufacturer of handheld devices

• Development of a wide range of software modules and market partnership for a real time scheduling web service

Aerospace Enterprises “Energy”,“CSKB-Progress”, Izevsky Motozavod (Russia)

• Prototyping P2P network of real time workshop schedulers for workers optimization

RusGlobal & Prologics (Russia)• Real time truck scheduler for resource optimization

Russian Fund of Fundamental Research, Ministry of Science and Education• Real time Swarm of Sattelites, Scheduler of Personal Tasks for Mobile Users, etc

Event 1: Factory is late for 4 hours with producing products. Factory Scheduler need to negotiate with Truck Scheduler on re-scheduling previously booked truck and avoid penalties for 4 hours delay. If booked truck can be reallocated by Truck Scheduler to other client – no penalties is required.

Event 2: Now small Truck is late. Then Truck Scheduler need to negotiate with Factory Scheduler that bigger truck (more expensive) will be sent to Factory to avoid penalties. Factory Scheduler can re-schedule production lines to produce more products which can be loaded into this truck for the same client to use capacity of bigger truck fully.

Enterprise Service Bus

Real Time Factory MAT Scheduler

Real Time Truck MAT Scheduler

XML messages

Event 1: delay on factory side

Event 2: delay in truck delivery

10.00 Monday

Adaptive Network of Real Time Schedulers

16.00 Monday

Future

That Was Then This is Future

Batch

Optimizers

Rules Engines

Constraints

Real-time

Manage Trade-offs

Decision-Making Logic

Cost/value equation

Visualize Learn, Simulate Adapt and Forecast

Enjoy beauty of self-organized systems for solving complex

problems.

Thank you!

Knowledge Genesis Group & Smart Solutions

Petr Skobelev Multi-Agent Technology: Ideas, Experiments

and Industrial Applications

Ekaterinburg, 12-13 May 2011

Small, but coordinated forces, produce magic. Prof. A. Konovalov.Lectures on supramolecular chemistry