Multi-Agent Technology & Scheduling Solutions

Transcription

1 Multi-Agent Technology & Scheduling Solutions

2 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

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, webapplications, 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!

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

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

7 Virtual Market Engine of Demands and Supply Virtual Market Engine D D S D D S D D S S S S S S S Demand-Supply Match D Demand Agent Match Contract S Supply Agent 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: 5 PALLETS SLA: 2 DAYS VOL: 10 PALLETS SLA: 10 DAYS 20% 60% 20% VOL: 10 PALLETS SLA: 10 DAYS 120% 80% 20% 60% VOL: 5 PALLETS SLA: 8 DAYS 20% 60% 100% 60% 40% Imagine It is important the power to of be having able to a assess single This system alternate order that has routes, can a automatically shortest meet journey services plan and re-plan levels and a route network minimum like cost. this, as but events the occur, capacity such is as not new available orders being added on one or of resource the legs. availability changes.

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 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 Time Truck 1 08: :00 20:00 Order 1 Truck 2 Can you shift to the right? Truck 3 Order 3 Can you shift to the left? Which Truck looks like the best for me? New order I can take New order if I: Shift Order 3 to the left Shift New order to the right Drop Order 3 My time window is too tight I cannot shift Order 2 No Can you transport me? Back Next

14 Logic of Multi-Agent Routing A Cross dock 1 X B Z C Cross dock 2 Consider business-network of a company Y 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 crossdock 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 Planning day 1 for day 3: no chance to Support backhauls and consolidations in real time No software for schedule 4000 orders With X-Docks and Drivers (manual procedure only) Knowledge was hard to share, it was spread among different experts Hard to consider various criteria quickly and choose the best possible option 8 minutes to schedule 200 transportation instructions Planning day 1 for day 2 and even day 1 for day 1 4 hours to plan orders 4000 orders via X-Docks and ability to add new orders incrementally (a few seconds for a order) Capture best practice and domain knowledge in ontology. New knowledge can be inserted quickly. 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 222 Order A pick-up Order B Car 111 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

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

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

22 Multi-Agent Technology & Scheduling Solutions