1. Multi-Agent Technology & Scheduling Solutions

2. Real Time Scheduling Solutions Based on Multi-Agent Technology
Knowledge Genesis, Ltd
Real Time Scheduling Solutions Based on Multi-Agent Technology
Prof. Petr Skobelev
Knowledge Genesis (Samara, Russia)
Founder and Chairman of Directors Board
Magenta Technology (London, UK)
Co-Founder and Member of Directors Board 1

3. Knowledge Genesis, Samara, Russia
Started 1997
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
Sister company – Magenta Technology (UK) (150+ programmers)
Advanced technology & product vision for solving complex problems
Own development platform
International network of partners
Strong links with universities

4. Key Challenges of Real Time Economy
Uncertainty, Complexity & Dynamics of business are growing
Clients, partners & resources demand more individual approach
High efficiency of business requires to become more open, flexible and fast in decision making
Solutions for Real Time Resource Allocation, Scheduling and Optimization can help to optimize, 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 & intelligently 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! 3

5. Multi-Agent Technology Differentiation
Traditional Systems
Multi-Agent Systems
Networks of agents
Parallel Processing
Negotiations & Trade-Offs
Distributed
Knowledge-Driven
Self-Organization
Evolution
Thrive with Complexity
Managing growth
Hierarchy of programs
Sequential Processing
Top-down instructions
Centralized
Data-driven
Predictable
Stable
Reduce Complexity
Full Control
Modules are working as a co-routines simultaneously

6. Knowledge Based Decision Making
Multi-Agent Platform for Scheduling
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
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 (Using Pattern Discovery module)
MAS
driven by
Inner virtual
market
Knowledge Based Decision Making
Java-based / .net
Peer-to-peer architecture
Scalable/Robust
Strong visualizations
Desk-Top & Web-Interface
Enterprise Platform

7. Virtual Market Engine of Demands and Supply
Demand-Supply Match D D S S
Match Contract D S S D D D S
Demand Agent
Supply Agent S S D S D D S D
Demand and Resource Matching on Virtual Market Engine - is Core Part of Real Time Scheduling Solutions

8. MAT Solutions for Real Time Logistics
Truck Scheduling
Ocean Scheduling
Taxi Scheduling
Courier Scheduling
Car Rental Optimization
Factory Scheduling
Airport Scheduling
Work forces ...

9. How It Works in Transportation Networks
VOL: 10 PALLETS
SLA: 5 DAYS
VOL: 10 PALLETS
SLA: 10 DAYS
VOL: 5 PALLETS
SLA: 2 DAYS
20%
20%
60%
VOL: 10 PALLETS
SLA: 10 DAYS
VOL: 5 PALLETS
SLA: 8 DAYS
60%
20%
120%
80%
60%
20%
It is important to be able to assess alternate routes, to meet services levels and minimum cost.
100%
60%
40%
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.
This order has a shortest journey route…
…but the capacity is not available on one of the legs. 8

10. Transport Logistics Network Complexity
Real-time scheduling with shrinking time windows
Large & complex networks (> 1000 orders per day, > 100 locations, > 50 vessels )
Less-than-Truck loads requiring effective consolidation
Need to find backhaul opportunities
Intensive use of crossdocking operations
Trailer swaps
Numerous constraints on products, locations, dock doors, vehicles: types, availability, compatibility
Individual Service Level agreements with major clients
Own and third-party fleet
Fixed and flexible schedules
Dependent schedules (trailers, drivers, dock doors, etc)
Activity Based Cost Model
Other client-specific requirements
Most of large & complex transport networks are still scheduled manually!

11. MAT Schedulers: Screens Example

12. Ontology as a Way to Capture Domain Knowledge
Describe your classes of concepts and relations

13. Logic of Multi-Agent Scheduling
Which Truck looks like the best for me?
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
08:00
12.00
16:00
20:00 No
Time
New order
Truck 1
Order 1
My time window is too tight – I cannot shift
Truck 2
Order 2
Can you shift to the right?
Truck 3
Order 3
Can you shift to the left?
Can you transport me?
I can take New order if I:
Shift Order 3 to the left
Shift New order to the right
Drop Order 3
Back
Next

14. Logic of Multi-Agent RoutingX
Cross dock 1 Z B
Cross dock 2 C Y
Consider business-network of a company
1.Order1 goes from Location C to Location Z
2.Order2 goes from Location B to Location X
3.Order3 appears, which goes from Location A to Location Z
4.Order3 decides to go to B and then travel with Order 2 via cross-dock1
5.Order4 appears, which goes from Location A to Location Y
6.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
Back
Next

15. Case Study: UK Logistics Operator
Network Characteristics:
4500 orders per day
Order 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 locations
Large number of small orders
3 cross docks
9 trailer swap locations
140 own fleet trucks, various types
20 third party carriers
Carrier availability time
Different pricing schemes
Problems to be Solved:
Location availability windows
Backhaul
Consolidation
Vehicle capacity
Constraint stressing
Planning in continuous mode
Dynamic routing
Cross-docking
Handling driver shifts
Key Problem: Real-time planning in a highly complex network with X-Docks and Dynamical Routing

16. Summary of Benefits (Before / After)
BEFORE IMPLEMENTATION
AFTER IMPLEMENTATION
Two operators worked for a day
to make a schedule for 200 instructions
8 minutes to schedule 200 transportation
instructions
Planning day 1 for day 3: no chance to
Support backhauls and consolidations
in real time
Planning day 1 for day 2 and even day 1
for day 1
No software for schedule 4000 orders
With X-Docks and Drivers
(manual procedure only)
4 hours to plan orders 4000 orders via
X-Docks and ability to add new orders
incrementally (a few seconds for a order)
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.
Hard to consider various criteria
quickly and choose the best possible
option
Choosing the best route from the point
of view of consolidation or other criteria

17. Case Study: Taxi Dispatching (UK)
Network Characteristics:
Call centre with about 130 operators
A fleet of more than 2,000 GPS-based vehicles
More than 13,000 orders per day
A large variety of clients, e.g., personal, corporate, VIPs, with a variety of discounted tariffs
A large number of freelance drivers
At 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 order
Unpredictability 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
Car 111
Order A pick-up
Car 222
Order B
Order A drop
Problems to be solved:
React on events in real time
Provide individual approach to clients
Balance costs vs time and risks
Increase efficiency of business
Satisfy drivers
Key Problem: Real-time resource reallocation

18. 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

19. Key Customers GIST (UK): M&S supply chain
Real-time scheduler for increased fleet utilisation and reduced transportation costs
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 optimization
Tankers International (UK): Manage a large oil tanker fleet
Real time scheduler for tankers scheduling and optimization
One Network (USA): logistics software provider
Providing development services to implement new core, scheduling and visual features/components for their platform
Airbus/Cologne University (Germany)
Catering RFID scheduler for improving service level and airport efficiency
Enfora (USA) : major manufacturer of handheld devices
Development of a wide range of software modules and market partnership for a real time scheduling web service
Taxi 956 (Russia)
Real time resource allocation for taxi company business optimization
RusGlobal (Russia)
Real time truck scheduler for resource optimization 18

20. Future: Shift of Paradigm
That Was Then
This is Future
Batch
Real-time
Optimizers
Manage Trade-offs
Rules Engines
Decision-Making Logic
Constraints
Cost/value equation
Learn, Simulate
Adapt and Forecast
Visualize

21. Conclusions Thank you! For contacts: Petr Skobelev
Mobile Phone: +7 (902)
Knowledge Genesis (Russia) 20

22. Multi-Agent Technology & Scheduling Solutions21