MAGENTA MULTI-AGENT SYSTEMS

FOR DYNAMIC SCHEDULING

Vyacheslav Andreev, Andrey Glashchenko, Anton Ivashchenko, Sergey Inozemtsev

George Rzevski, Petr Skobelev and Petr Shveykin

Magenta Technology Ltd, 349 Novo-Sadovaya St., Samara, 443125 Russia

Keywords: Multi-agent systems, Adaptive scheduling, Real time, Mobile resources.

Abstract: The document presents an overview of Magenta multi-agent solutions for real time scheduling and

optimization of mobile resources. Brief survey on traditional scheduling methods, main principles of multi-

agent approach, system architecture, functionality, industrial applications and perspectives are described.

The multi-agent approach for dynamic scheduling gives opportunity to solve complex problems, react on

events in real time, improve resource utilization and provide a number of other benefits.

1 INTRODUCTION

The increasing number of the modern enterprises

meets problems of optimal scheduling resources in

real time. This problem becomes especially actual

and important for enterprises of transportation

logistics operating large fleets of mobile resources

(ships, trucks, taxi and others), in connection with

increase of complexity and dynamics of business,

and also a rise in fuel prices.

Thus it is a question of the enterprises having

hundreds and thousands of mobile resources,

simultaneously being in movement, receiving tens of

thousands orders a day, in unpredictable moments of

time, operating in regional or national scale, and

trying to satisfy various client requirements. For

such enterprises the solution of the specified

problem becomes crucially important for business,

as without using of the automated systems of

resource scheduling these enterprises simply can not

run business successfully in the future, to be

effective and competitive.

However, new methods, algorithms and software

tools which would allow adapting flexibly plans of

realization of orders in real time are necessary for

solving this problem. Flexibility supposes an

operative automatic reaction on unpredictable events

to be here, such as a new order arriving, the

cancellation of already accepted and allocated order,

failure or a delay of a resource, arriving of a new

resource, change of criterion or strategy of planning,

etc.

Thus, unlike known "batch" methods when all

orders and resources are known in advance (and can

be more or less optimized with well-known

methods), in case of real time software solution

should work in adaptive manner dynamically

adjusting existing plans instead of full re-scheduling

of already allocated orders each time: any new event

should activate processing of corresponding orders

and resources, causing a chain of re-scheduling

operations which depth can be limited by available

time of the reply or other factors. At the same time,

if there is enough time, the schedule can be exposed

to continuous optimization or, in general, to

balancing of interests of all participants as each

order or a resource can have their own specific

system of criteria, preferences and constraints.

Introduction of such new methods and tools in

many respects is stimulated by the appearance of the

Internet-services supporting work in real time

(giving operative data about possible routes,

weather, traffic jams, etc.), opportunities of GPS

navigation, e-maps, and new mobile phones,

handheld computers and communication devices

with an opportunity of getting user geographic

coordinates. A driver, armed by such device, can

constantly be on "radar" of system that allows to

react instantly to arising events, to look for the best

resource for each order, to count the schedule of

performance of the order, constantly to compare

489

Andreev V., Glashchenko A., Ivashchenko A., Inozemtsev S., Rzevski G., Skobelev P. and Shveykin P. (2009).

MAGENTA MULTI-AGENT SYSTEMS FOR DYNAMIC SCHEDULING .

In Proceedings of the International Conference on Agents and Artiﬁcial Intelligence, pages 489-496

 SciTePress

with the plan and a reality, to monitor possible risks

of delay. Besides these devices allow to cooperate

online with control centre of the enterprise (or other

drivers), receiving and choosing possible options,

chacking order starting and finishing, reporting

about unforeseen events, requesting the information

on the nearest fuel station, etc.

Thus modern e-maps (Map24, MapPoint, Google

Maps, MapInfo and others) allow to receive a

detailed route "door - to door", considering not only

seasonal throughput of roads and typical places of

traffic jams occurrence during various time of day,

but also traffic signs. At last, modern Internet-

services in big cities allow receiving the information

on weather conditions change or really arisen jams

on the roads, the nearest restaurants and cafe, auto

repair shops, etc.

In this document the multi-agent approach for

developing adaptive schedulers for mobile resources

is considered, the generic architecture of adaptive

schedulers is shown, examples of industrial

applications in transport logistics are given, and also

next steps of the developments are discussed.

2 BRIEF SURVEY OF

SCHEDULING METHODS

AND TOOLS

In spite of significant progress regarding

development of large-scale Enterprise Resource

Planning (ERP) systems, opportunities of the

enterprises on development of adaptive scheduling

systems remain very limited.

Traditionally the ERP systems include

subsystems of orders collection, large databases for

orders and resources, accounting and reporting

subsystems and a lot of other components. However

in these systems batch or manual scheduling of

orders is supported, that was already discussed

above. The schedulers offered by such large

companies, as SAP, Oracle, Manugistics (it was

recently bought by JDA), i2, ILOG and others

usually realize various versions of Constraint

programming methods, based on combinatory search

of options in depth, for example, a method of

branches and borders (Handbook of Scheduling,

2004).

To reduce the number of options considered in

combinatorial search new methods consider various

heuristics and meta-heuristics (the term "heuristics"

is usually understood as a set rules, defining what

option is the best, and "meta-heuristics" means a

rules to choose heuristics), allowing to provide good

decisions for reasonable time and reducing search

iterations (Stefan Vos., 2000 – 2001).

Well-known heuristics in optimization are

"greedy" methods. In such methods the decisions are

taken by a choice of the best of options on each step,

and once made decision is never reconsidered.

Various other methods of local optimization are

more complex, where initial solution which then is

improving by local changes can be changed

randomly or in some pre-defined way, if the good

final solution is not reached, and the process repeats

many times.

As one of the most known meta-heuristics we

can consider Simple Local Search Based Meta-

heuristics (SLSBM) – local optimization meta-

heuristics. Here one of heuristics can implement

casual choice of one candidate from the list of the

best, another one - looking forward or randomizing

of criteria, etc. One more meta-heuristics developing

recently is Simulated Annealing which is based on

modeling of process of cooling. This method

represents an expansion of methods of local

optimization in which many options could be formed

on each step and it is possible to consider not only

the best options, but also some worsening decisions

with the probability calculated as function from

some attribute, analogue of temperature.

The main idea of becoming more and more

popular Tabu Search is the usage of history of

decisions of local optimization when some

investigated options are becoming prohibited (tabu)

and consequently they are not considered on a

following step.

One more new meta-heuristic is Ant Search, in

which the behavior of the ants, getting food is

modeled. The success of one ant in getting of

"food", i.e. taking of some decision, during some

time prompts other ants a correct direction, but in

due course signs on this successful direction "fade".

In last period of time also many other meta-

heuristics become more and more popular inheritting

physical or biological concepts. Another example

here is Adaptive Memory Programming method

which inherits the use of common memory of

decisions. In last developments researchers apply

mixed miscellaneous meta-heuristics, in which

several parallel algorithms are acting, and each of

them suggest their own decision.

At the same time, even in view of considered

methods and tools of local search of variants require

greater expenses of memory and time for producing

schedules. For example, producing of the optimum

plan for the large transport company in one of

ICAART 2009 - International Conference on Agents and Artificial Intelligence

490

available software packages takes about 8-10 hours.

During this time the volume of orders can be

essentially changed that will require to start planning

all over again. At the same time the technology for

planning in real time remain rather primitive, and an

opportunity of flexible adaptation on the base of

happening events refer mainly to an opportunity of

manual plans updating. As a result, according to the

estimations of transportation logistics experts, the

created schedules are feasible only on 40 %, that

compels many large transport companies still to

contain staff of very skilled and expensive operators

on planning and to carry out time-consuming manual

or semi-automatic planning.

This, certainly, is promoted by both high

complexity and labour intensity of planning,

unpredictability of dynamics of a stream of events,

by requirements of an individual approach to each

order and resource, constant change of conditions of

functioning of the enterprise forced by clients and

competitors, and also necessity of the account of

many other very specific features in each business.

For example, the operator of trucks fleet should

constantly keep in a head preferable time windows

of loading-unloading of warehouses and shops,

conditions of contracts with clients, rules of

compatibility of cargoes, experience of the concrete

driver and even such specific facts, that the certain

road became impassable for greater wagons because

of rank branches of trees.

As a result many of existing classic methods of

planning and resource optimization have a number

of very important limitations in practice:

• Do not consider complexities of the modern

business operating in thousand of orders

and resources, supporting interdependency

between all operations, reflecting and

balancing interests of many parties

involved, having a lot of their own features;

• Do not provide opportunities for adaptive

planning in real time which requires

dynamic event-driven conflict solving in

already available schedule;

• It is supposed that all orders and resources

are “identical” but in practice they all have

their own individual criteria, preferences

and restrictions, each can change during the

sistem work (service level, time of delivery,

costs and profits, risks of delivery,

inconvenience of the driver, etc);

• Do not give the tools for the aquiring

knowledge which are specific to every

enterprise, influencing quality of provided

schedules;

• Do not allow an operator to explain and

adjust decisions easily and in convenient

way.

All this not only reduces productivity and

efficiency of existing methods and tools, but also in

practice in many respects stops their use.

3 MULTI-AGENT APPROACH

TO DYNAMIC SCHEDULING

To provide opportunity to build adaptive schedulers

on the top of existing ERP systems and eliminate the

specified lacks in scheduling mobile objects multi-

agent approach was offered which is based on the

concept of networks of demand and supply

(Skobelev P.O., 2002; Vittih V.A., Skobelev P.О.,

2003).

Below there is a short description of the offered

approach in application to transportation logistics.

The model of any transport network can be based on

the description of dynamic interaction of agents

which take roles of demands and resources. For

example, for truck operator the model of a

transportation network can include agents of a client

and an order, a truck and a cargo, a crossdock and a

store, a driver, etc. Complexity of model and

accuracy of modeling of a real network increase as

with growth of a number of program agents

representing interests of different physical and

abstract essences, necessary for the description of

network, and growth of intensity of interactions

between agents of different types.

Thus a basis of interaction of all specified agents

is a virtual market on which agents can buy or sell

the services: the order searches for the truck, the

truck for the driver, etc. During these interactions

agents can take decisions on building links between

demand and supply and change their decision when

new events take place.

The role of demand bears in itself the knowledge

of "ideal" requirements of order implementation

(future), and a role of an supply (resource) -

knowledge of "reality" (past and present). As a

result, each truck knows for certain what is its route,

where it is now, what cargo it is loaded, etc.

Receiving offers from different trucks, the order can

decide, which of them suits better. But, on the other

hand, the truck can generate new demand, specifying

which orders are necessary for it at present to be full

and significantly increase utilization.

MAGENTA MULTI-AGENT SYSTEMS FOR DYNAMIC SCHEDULING

491

The constant activity of all agents, either from

demands’ side, or from supplys’ one, calls multi-

thread negotiations in the virtual market,

going quasiparallely. Thus the feature of the

approach is that each agent is considered as a

machine of states that returns control to the

dispatcher after each step of negotiations. Each

agent constantly tries to achieve its goal and for this

purpose enters into relations with other agents (the

order is reserved on the truck, the truck on the

driver, etc.) which can be reconsidered by agents as

a result of decision making, and also under action of

events coming from the outside or generated inside.

le.

So, after getting the new order in the system its

agent is created, that on behalf of this order enters

interaction with agents of resources for search of the

best location. If the most suitable resources are

already occupied, they can start to suggest to the

orders placed on them earlier to look for

new locations. This process, as chain reaction, can

grasp all new orders and resources, forming a wave

of negotiations and changes, and, theoretically, can

end with full reconstruction of the whole schedu

If suddenly by some reason a chosen truck

becomes not available (damage, breakdown, etc.),

then its agent must be activated and then it will find

all the orders, which are planned to this truck and

report them about resource un-availability. These

orders activate and start to look for other trucks.

This allows to re-plan the routes of trips operatively,

flexibly and safely. The result is considered done

and the system ends its work when all agents don’t

have opportunities to improve their status anymore.

Thus the decision of a problem at the given

approach is formed evolutionary during the

exercising of each new event and consequently is

irreversible (for convertibility it’s necessary

reproduction of conditions at which the decision was

accepted). But the formed schedule is considered not

as "static" structure of data received as a result of

unitary application of one central monolithic

algorithm, but as unstable balance of interests of

many parties involved, being got and supported

during interactions of two opposite entities of

demands and resources.

As a result the given approach in many respects

integrates the considered above modern ideas of the

dynamic planning and optimization and provide a

number of meta-heuristics and solid framework for

developing various number of competing and

cooperating agents implementing modern algorithms

of optimization. It helps significantly to increase

quality and efficency of scheduling and make results

more clear, adjustable for end-users, and also to

reduce delivery time.

4 ARCHITECTURE OF SYSTEMS

FOR ADAPTIVE SCHEDULING

To implement the developed approach in scales of

the large enterprise the architecture of system for

adaptive scheduling is offered, it’s presented on Fig.

Let's consider in detail the basic components of

the given architecture expanding applicability of the

adaptive scheduler up to scales of the enterprise

(Andreev V.V., Vittih V.A., Batischev S.V.,

Ivkushkin K.V., Minakov I.A., Rzevski G.A.,

Safronov A.V., Skobelev P.О., 2003; Batishev S.V.,

Ivkushkin C.V., Minakov I.A., Rzevski G.A.,

Skobelev P.O., 2001).

In general, the system implements a standard

three-tier architecture including servers for web-

interface, business-logic and databases, and also can

get the operative information from external web-

services and cooperate with communication devices

of users (for example, drivers).

Web-interface layer of the system gives an

opportunity to make settings and process orders and

resources of the enterprise, etc. Through a web-

interface the system operator can see the current

schedule of system formed by the adaptive

scheduler, in the form of Gantt chart (the schedule

on each resource) or in a tabulated mode, from the

side of both orders, and resources. At last, one more

important component is for a display and processing

of events of different type which can be transferred

to scheduling manually or automatically. It is

important to note, that internal and external events

processing report is available for a user, that allows

to explain the decision making logic of the system to

an operator.

If necessary a user can be provided both with a

desk-top interface for more convenient work at the

local machine using web-start technology.

ICAART 2009 - International Conference on Agents and Artificial Intelligence

492

Web-interface

Settings Schedule Events

Web–service

of e-maps

Business-logic

Figure 1: Architecture Of Systems For Adaptive Scheduling.

The layer of business-logic actually provides a

reaction to events, adaptive scheduling and delivery

of results. A basis of this part of the system is the

adaptive scheduler constructed using the described

above multi-agent approach. For each problem there

can be developed a new scheduling engine, but at

the same time there are certain opportunities of

adaptation of existing "engine" according to new

requirements by ontology configuration. The tools of

ontology support allow to describe objects and

attitudes of a problem domain, and also the scene

describe current position of resources and orders in a

transportation network at the moment of time. On

this basis the rules of decision-making are formed,

which can be switched on and off, be modified or

adjusted by the user. The logic of decision-making is

supported by the set of components, allowing to

carry out calculations of distances or costs, which

are specific for transport logistics, and other

functions.

The database layer allows to save the

information of concrete orders and resources, and

also history of changes of the schedule.

The adaptive scheduling system can integrate

itself with client platform or to use components of

the offered platform including tools of a security

control and management rights of users, provide

visual reports, etc.

On the basis of given representation of

architecture there can be developed the solutions on

adaptive scheduling of resources for enterprises of

various domains, considering the specific

requirements and restrictions.

The examples of industrial application of the

described approach and solution architecture are

given below.

5 APPLICATIONS IN

TRANSPORTATION

LOGISTICS

5.1 Tankers Scheduling System

This system is used for management of large-

capacity tankers, carrying out transcontinental

transportations of oil (Himoff, J., Skobelev, P.О.,

Wooldridge M., 2005).

Everyday a company, carrying out up to 70 % of

world transportation in a considered class of vessels,

gets 10-15 inquiries about oil transportation.

Operators of the company should make the analysis

of a situation in real time, sometimes even on the

phone, to analyze situation, provide all economic

calculations and make a decision, on what tanker it

is necessary to execute the order.

Database

Web–service

of traffic, wether,

Other web-services

(by demand)

Communication

devices support

Operational platform

Adaptive

schedule

Ontology

Orders Resources History

Decision

MAGENTA MULTI-AGENT SYSTEMS FOR DYNAMIC SCHEDULING

493

At the same time it is necessary to keep

constantly in a head arrangement and traffic

schedules of about 50 own vessels, and also

positions of competitors, count routes of traffic,

consider features of passage of Suez canal if it is

necessary (time for partial unloading of oil is

required), consider, what ships can enter into what

ports, where and when it is better to refuel the

tanker, what are weather conditions, etc.

To solve this problem the system of adaptive

scheduling has been developed, which was

integrated with a data management system. Due to

small horizon of scheduling (the number of orders

planned forwarding advance), in this system the

arrival of a new event entails long chains of possible

changes including up to 7 exchanges of orders

between tankers. Thus, the new order can affect

changes of a lot of tankers and even alteration of

contracts with a number of clients.

Now the system is in regular operation by the

customer for already more than two years, daily

allowing to simulate orders allocations and schedule

choosen orders more effectively. The cost of one day

of idle time of such tanker is about $150,000 that

allows to estimate economic benefit of introduction

of the system.

At the same time, the opportunity to take and

formalize valuable domain-specific knowledge of

operators which are necessary for decision-making

turned out also very important for the customer.

Actually, as it was very difficult to replace

operators, and in case of retirement, illness or

vacation of those key employees before, all business

of this company was under threat of efficiency loss.

5.2 Corporate Taxi Scheduling System

This system allows the company, who is the leader

in its country, to schedule adaptively about 13

thousand orders a day at presence of several

thousand machines, up to 800 from which are

always on the road. The company basically serves

orders of corporate clients, but also each interested

person can call a taxi by phone through call center in

which 130 operators simultaneously accept calls, or

using the system of collecting orders in the Internet.

The company tries to provide an individual approach

to each client, allocating only machines of the

necessary class or a class above, with well-reputed

driver, give on demand the car for disables, with the

trailer, for smoking passengers, for transportation of

animals, etc.

The drivers work in the company as freelancers,

deciding themselves what number of days and hours

per week (with some restrictions) to work, renting

cars at the company. At the same time they can

come to work at any time. The drivers have

handheld computers which allow the driver to

appear on "radar" of the system when starting to

work. At occurrence of the new order the system

automatically finds the best car and preliminary

reserves the order.

On the average the submission of the car takes

about 9 minutes. From the moment reception of the

urgent order, the system continues to redistribute

orders for concrete time continuously in view of

appearing of new resources, and does not make of

the final decision till dynamically defined moment

when it is necessary to send the car to client. During

this time the system can change the decision on

distribution of the order to cars some tens times.

When already it is time to send the car to the client,

the system makes the final decision and sends a

message with criteria of order to driver, after that

gets a message about receiving the order. At the

same time the driver also gets a city map in view

with the route how to reach the client with the view

on police signs.

It’s important to note, that the system balances

distribution of orders to the driver, providing fair

distribution of orders. Thus, it is possible to avoid

claims of drivers to dispatchers who they think often

distributed good orders to “own” drivers. Besides

when the driver informs, that finishes the work, the

system selects orders on road home for him, that not

only raises profit of the company, but also earnings,

and satisfaction of drivers.

The system is in commercial operation for half a

year and has allowed the client to increase total

amount of sold orders by 7 %, at the same volume of

fleet.

5.3 Truck Scheduling System

This system provides the truck scheduling for world

famous networks of supermarkets on the country

scale. Among the transported goods there are food

stuffs and drinks, including the frozen products,

household electronics, clothes, etc. (Himoff J.,

Rzevski G.А., Skobelev P.О., 2006; Skobelev P.O.,

Glashchenko A.V., Grachev I.A., Inozemtsev S.V.,

2007).

The level of orders in corporate network – about

4 000 a day, the fleet of the company includes about

300 trucks of various volume, and a number of them

is equipped by the additional equipment

(refrigerators, etc.), the delivery network includes

about 600 geographical locations all over the

ICAART 2009 - International Conference on Agents and Artificial Intelligence

494

country. The complexity of a problem in many

respects is connected with presence of warehouses

of intermediate storage, necessity of splitting of

greater orders for some trips and, on the contrary,

consolidations of small orders of different volume,

requirements of compatibility of cargoes, different

opportunities of acceptance of trucks in different

warehouses, etc.

For solving this problem the adaptive scheduling

system was developed. It automates all main steps of

orders execution: from orders receiving and adaptive

splitting and consolidation, routing and scheduling –

to reports making. This system turned out to be the

most difficult, where architecture of the virtual

market includes a lot of agents acting together and

proactively.

In particular, the orders are dynamically broken

to sub-orders that are then consolidated in groups,

and the trips also are formed dynamically from

groups, and they, in turn, are planned for trucks. If

the order has been splitted unsuccessfully, and it was

not possible to plan good trips, it is made re-splitting

and routing and scheduling begins anew. The big

number of active agents (tens and hundred thousand)

has led to necessity of application of more

developed mechanisms of scheduling of agents,

when only the most perspective agents get activity,

competing with each other.

In present time the system is on implementation

step, and decision making logic tuning is taking

place. Before the deployment started the operators

planned trips manually on the basis of numerous

Excel tables. In this connection a lot of time was

spent for adjustment of initial data in which there

were many issues, including different versions of

names of the same warehouse, etc.

It’s expected with the system introduction we’ll

get not only significant economic benefit of more

effective scheduling resources, but also the number

of operators will be essentially reduced.

5.4 Car Rental Scheduling System

This system is for car rental scheduling. A company

has about 100 stations. Each of them has on the

average up to 150 cars of different classes. Clients

can order the car by phone, directly come to station

or book car via the Internet.

For convenience of clients it is possible to agree

about delivery of the car during necessary time to

the necessary place. But then it is required to send

the car with the driver which can work at stations as

in the certain days, and overtime. Also it’s necessary

to send drivers, to take away cars from clients,

therefore in some cases it is necessary to send

several drivers in one car, someone will bring the car

to the client, and someone will take away the used

cars.

For solving the problem the adaptive scheduling

system was developed, that allows to re-schedule

operatively the delivery of cars for new-coming

orders and also in case of different kind of events

(delay, a driver’s sickness, traffic jams, etc.), and

make schedule for drivers who bring or take cars. At

the same time the system also addresses to an e-map

and shows drivers recommended routes of traffic,

and also sends them in real time all other necessary

instructions. The system is integrated with an

available system of gathering of orders by phone and

Internet. Now development and testing of system on

real data is finished and its expansion at first five

stations is begun, up to the end of current year

introduction in all other stations is expected.

The economic benefit of introduction of system

consists in distributing cars in view of a situation

being the whole country as a whole, and to estimate

more precisely, from what station it is necessary to

give the car. Before introduction of the system the

decisions were made locally at stations that led to

constant surplus of cars at one stations and to

deficiency of the necessary cars at other stations, and

also to infringement of obligations to clients.

Besides after introduction of system reduction of the

total number of cars in the network up to 10% and

savings on fuel expenses and salaries of drivers are

expected as the number of the superfluous trips,

involved drivers and amount of overtime will be

reduced.

Let's note that the developed systems have been

constructed with application of methods and means

(Rzevski G.A., Skobelev P.O., Andreev V.V., 2007;

Andreev M.V., Rzevski G.A., Skobelev P.O.,

Shveykin P.K., A.Tsarev, 2007)

which can be

applied to a wide range of businesses.

6 CONCLUSIONS

In this document we described the multi-agent

approach for development of the systems for

adaptive scheduling of resources in real time.

Positive results of the first industrial development on

the basis of the offered approach prove its important

advantages and define future benefits of its

development and application in various spheres of

transportation logistics.

At the same time, the first experience shows, that

adaptive scheduling systems are demanded for a

MAGENTA MULTI-AGENT SYSTEMS FOR DYNAMIC SCHEDULING

495

wide range of other enterprises (Rzevski G.A.,

2008). In particular, for small enterprises, the

workers who are supplied by specialized GPS-

devices or usual cellular telephones, communicators

or handheld computers with built in GPS services or

services of definition of coordinates on cells, also

can be considered as mobile resources.

At the same time the further development

perspectives of all scale of adaptive scheduling

solutions are connected with the use of emergent

intelligence concept (Rzevski G.A., Skobelev P.O.,

2007).

REFERENCES

Handbook of Scheduling: Algorithms, Models and

Performance Analysis. Edited by J. Y-T. Leung //

Chapman & Hall / CRC Computer and Information

Science Series. – 2004.

Stefan Vos. Meta-heuristics: The state of the Art. // Local

Search for Planning and Scheduling. Edited by A.

Nareyek // ECAI 2000 Workshop, Germany, August

21, 2000 // Springer-Verlag, Germany, 2001.

Skobelev P.O. Open multy-agent systems for operative

data reduction in decision making processes. //

Avtometriya. – 2002. - №6. - pp. 45-61.

Vittih V.A., Skobelev P.О. The multy-agent models of

interaction for forming requirements and opportunities

networks in open systems. – 2003. - №1. – pp. 177-

185.

Andreev V.V., Vittih V.A., Batischev S.V., Ivkushkin K.V.,

Minakov I.A., Rzevski G.A., Safronov A.V., Skobelev

P.О. Methods and resources of making open multy-

agent systems for decisions making processes support

// News of the Russian Academy of Sciences. Theory

and systems of management. 2003. № 1. pp.126-137.

Batishev S.V., Ivkushkin C.V., Minakov I.A., Rzevski G.A.,

Skobelev P.O. MagentA Multi-Agent Systems:

Engines, Ontologies and Applications // Proc. of the

Intern. Workshop on Computer Science and

Information Technologies CSIT’2001, Ufa, Russia,

21-26 September, 2001. – Ufa State Aviation

Technical University – Institute JurInfoR-MSU, Vol.

1: Regular Papers, 2001, pp. 73-80.

Rzevski G.А., Himoff J., Skobelev P.О. “Magenta

Technology: A Family of Multi-Agent Intelligent

Schedulers”. Proceedings of Workshop on Software

Agents in Information Systems and Industrial

Applications (SAISIA). - Fraunhofer IITB, Germany,

February 2006.

Himoff, J., Skobelev, P.О., Wooldridge M. Magenta

Technology: Multi-Agent Systems for Ocean Logistics

– Proceedings of 4-th International Conference on

Autonomous Agents and Multi Agent Systems

(AAMAS 2005). – Holland, July 2005.

Himoff J., Rzevski G.А., Skobelev P.О. Magenta

Technology: Multi-Agent Logistics i-Scheduler for

Road Transportation – Proceedings of 5-th

International Conference on Autonomous Agents and

Multi Agent Systems (AAMAS 2006). – Japan, May

2006.

Skobelev P.O., Glashchenko A.V., Grachev I.A.,

Inozemtsev S.V. MAGENTA Technology Case Studies

of Magenta i-Scheduler for Road Transportation -

Proceedings of 7-th International Conference on

Autonomous Agents and Multi Agent Systems

AAMAS 2007 - US, Hawai, May 2007.

Rzevski G.A., Skobelev P.O., Andreev V.V.

MagentaToolkit: A Set of Multi-Agent Tools for

Developing Adaptive Real-Time Applications -

HoloMAS 2007, Germany.

Andreev M.V., Rzevski G.A., Skobelev P.O., Shveykin P.K.,

A.Tsarev. Adaptive Planning for Supply Chain

Networks - HoloMAS 2007, Germany.

Rzevski G.A. Investigating Current Social, Economic and

Educational Issues using Framework and Tools of

Complexity Science, World University Forum, Davos,

January 2008.

Rzevski G.A., Skobelev P.O. Emergent Intelligence in

Large Scale Multi-Agent Systems - Education and

Information Technologies Journal, Issue 2, Volume 1,

2007.

ICAART 2009 - International Conference on Agents and Artificial Intelligence

496