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)
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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Years
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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 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 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%
Text Understanding SystemText Understanding System
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%
VOL: 10 PALLETSSLA: 5 DAYS
80%
VOL: 5 PALLETSSLA: 2 DAYS
60%
20%20%
20%
VOL: 5 PALLETSSLA: 8 DAYS
60%
20%
VOL: 10 PALLETSSLA: 10 DAYS
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
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