Multi-Agent Scheduler for Rent-a-car companies

Transcription

1 Slava Andreev 349 N-Sadovaya St. Samara Ru Multi-Agent Scheduler for Rent-a-car companies George Rzevski 130 Shaftesbury Aven London W1D 5EU, UK +44 (0) ABSTRACT The paper gives overview of multi-agent dynamic scheduling solution for rent-a-car business domain. Specific requirements for cars and drivers scheduling, developed multi-agent and ontologybased approach for real time scheduling, systems architecture and performance measurements are presented. Key design decisions and results of first stage of developments are discussed. One of most important results is the system ability to schedule complex interdependent works of many resources and update generated schedules by events in real time. Developed multi-agent approach can be applied for solving complex real time scheduling and optimization problems for many applications Categories and Subject Descriptors H.4.2 [Information Systems Applications]: Type of Systems - decision support, logistics General Terms Management, Measurement, Design, Economics, Performance Keywords Dynamic scheduling, real-time planning, multi-agent systems, microeconomics, ongoing optimization, events, criteria of decision-making. 1. INTRODUCTION The problem of resources scheduling in real time is one of the most difficult in the modern theory of optimization [1-3]. In particular, when it is required to create schedules of many participants which are connected with each other, and changes in the schedule of one participant cause changes in the schedules of other participants. In the present paper we will consider an example of the scheduling solution for rent-a-car company based on multi-agent approach. In our approach the output schedule is the result of the distributed decision-making processes which are constantly running by agents of demands and resources with conflicting interests. The approach developed allows to solve a complex problem of cars distribution to rentals in real time for the whole network of stations under conditions of high uncertainty and dynamics of demand and supply and with other important advantages. Now the system developed is already delivered to rent-a-car company and successfully works in first pilot stations. It is expected to introduce the system in nearby 25 stations of this company till the end of the year and in the beginning of the next year. Cite as: Multi-Agent Scheduler for Rent-a-car companies, S.Andreev, G.Rzevski, P.Shveikin, P.Skobelev,I.Yankov, Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), Decker, Sichman, Sierra, and Castelfranchi (eds.), May, 10 15, 2009, Budapest, Hungary, pp. XXX-XXX. Copyright 2009, International Foundation for Autonomous Agents and Multiagent Systems ( All rights reserved. Peter Shveykin 349 N-Sadovaya St. Samara Ru Peter Skobelev 349 N-Sadovaya St. Samara Ru Igor Yankov 349 N-Sadovaya St. Samara Ru george.rzevski@ma gentatechnology.com Shveykin@magentatechnology.ru Skobelev@magentatechnology.ru Yankov@magentatechnology.ru In the paper we will consider in more details specific requirements for solving rent-a-car scheduling problem, developed multi-agent approach to the solution of this problem, architecture of delivered system and main features of its implementation, including performance measuring. Also it will be shown that the developed approach can be also useful for a wide range of other applications in transport and production logistics, and other industrial applications. 2. THE PROBLEM DOMAIN OF RENT-A- CAR SCHEDULING At a glance rent-a-car business assumes simple scheduling process: the client specifies group of the desirable car, time and a place where he would like to take it, and also a place and time where he would like to leave it after the rent term expiry. However, to solve this problem it is required to make a number of decisions, from what station to take away the car looking on preferences of the client, distances and costs and presence of cars at stations, what driver to schedule to wash and deliver car accordingly with the business processes of the company, how much driver overtime it will require, etc. As a result to solve the problem just by taking available car from nearest station is not possible. And the problem becomes even more complex because it is also necessary to deliver allocated driver to the chosen car, and after delivery of the car this driver needs to be returned back on station or to direct to another rental or to home. Very often, in turn, for this purpose another car and another driver are required and so on. As a result to solve the problem is necessary to make drivers consolidation for such trips with optimization of routes. Besides, there are a number of unforeseen events in such networks: new rentals arrive and some of them vary or even cancel during the work, the car can break, the driver can become ill or takes day-off suddenly, driver can be late in a traffic jam, etc. Usually the territory of rent-a-car business breaks into set of small regions in which a number of stations are functioning where the rental information is arriving, where cars are accumulating and being served and drivers are beginning the working day. At each station there is a special personnel which is engaged in manual generation of the schedule for drivers and cars. Usually this work is carried out by skilled managers who, considering set of factors, define what car, to what client should be given, and also what driver when and where should take away or deliver the car etc. Besides, these managers can to co-operate both with clients, and with drivers, flexibly correcting schedules in process of situation changes at stations.

2 It is very important to help station managers to solve the problem of automatic scheduling cars and updating of such schedules because it can lead to significant reduction of cars total run and size of cars fleet, increase of service level for clients, etc. Besides, introduction of scheduling solution provides scalability of business and guarantees stability of company growth. For developing scheduling solution for rent-a-car business it is necessary to consider following requirements: 1. The schedules of drivers and cars need to contain interconnected operations which they carry out at different stages of business process taking into account logic sequence, a place and time and a number of other attributes; A good example is the case when a few drivers need to be consolidated in one car to solve delivery problem for a number of orders. For creating such schedule it is necessary to consider delivery time of these orders, and also many geographical and other constraints.also the schedule of drivers should include operations on cars washing and movement of the driver on foot in case the forthcoming distance is not so great. 2. It is important to be able to combine and balance different criteria of decision-making which can change for in time. The major criteria include one mile run cost of the car and the driver, the penalty for delay to rental, the penalty for late delivery of the returned car on station, cost of drivers overtimes, and the penalty for discrepancy of demanded group of the delivered car (upgrade/downgrade). It is very important to notice that scheduling process in practice represents more likely process of discovering and solving conflicts among orders rather than search of optimal variants. Thus an ability to reach the reasonable trade-off between different criteria goes on the foreground. Here are some examples of such conflicts and compromises: it is important that cars to come back to station as soon as possible, and to be delivered as it is possible later, but, in case of great number of rentals and impossibility to carry out all tasks in time, the priority is given to delivery of the most urgent reservations; the system should plan working hours of drivers effectively, but should balance it with optimizing routes. For example, if tomorrow in a point it is necessary to make delivery of one car then it is not useful to send the driver there today to do a collection of other car, even if this driver is free because organization of consolidated trip tomorrow will be more effective; if there are serious delays it is better to reduce profit on a cheap collection and to try to execute new delivery in time instead of nearby located car collection; on one hand, it is necessary to carry out collections of cars, only if there is a necessity in them because it will lead to reduction of the general run on unnecessary car movement on station, and on another hand - it is necessary to do "unnecessary" collections to reduce risk not to cope with rentals flow in the future, because to deliver cars from station is faster, than from a field. To balance different criteria it was offered to use a virtual money's worth that allows to define specific cost of each task performance rather easily and quickly; 3. Dynamic scheduling of rent-a-car business requires eventdriven approach to scheduling in real time. Work of managers and drivers in the considered business is constantly under pressure of real time events which can negatively affect quality of schedules of all participants. For example, there can be a car breakage, or the driver could not find keys from the collected car or could not find the client of the delivered car. All these events demand immediate reaction of the scheduler, revision of all connected schedules and search of the decision which will correct a situation and solve the problem. Among the urgent events are events coming from the drivers on their compliance with the schedule, because drivers can be late or complete the tasks sooner. It should affect all schedule too, because there can be found more optimal ways to perform next operations. 4. Combined planning and execution is the key feature of scheduler in rent-a-car business. The scheduler should be able to respond quickly to many real time events including growing gap between plan and fact because system is aimed not only to generate and update schedules but also monitor execution of planned tasks.. The planned operations go into execution to mobile devices of managers and drivers who should confirm the beginning and the end of each operation, because the delay of one operation can cause a chain of replanning for others. In case of occurrence of unforeseen events, solution should be found as soon as possible, because in the course of time the schedule of resources is fixed and the possibility to find a new good solution dramatically reduced. 5. It is necessary to take into account the large space of options for possible solutions that complicates usage of combinatorial algorithms and makes classical methods of local search ineffective. At the same time, when the system does not handle the events, it should make ongoing efforts to find the near to optimum schedule. 6. There are a lot of another important restrictions and preferences to be taken into account. For example, system usually needs to schedule 100% of all reservations in accordance with their claims, strictly abide by drivers rota and to deliver driver at home station at the end of the day, does not leave drivers in the field without a car, comply with the order of the tasks during car s preparation, to deliver a car firstly we need wash it, etc. Designing a scheduler that can cope with such a variety of operating conditions, handle uncertainty related to the occurrence of events and at the same time continuously produce schedules that maximize the set of specified criteria is a real intellectual challenge. To the best of our knowledge such schedulers have not been proposed in the literature or implemented in practice. 3. SOLUTION There was suggested the multi-agent approach based on the Demand-Resource Networks (DRN) conception to solve considered task of rent-a-car scheduling in real time [4-7]. The scheduling process of DRN approach is modeled by dynamic interaction of the agents representing Demands and Resources of basic participants of a company business network. Among the basic agents for this type of business are agents of the client and

3 the rental order, station and the car, operation of business process, the driver, car delivery and collection task agents and some other. The scheduling process starts with dividing one big task to deliver car to several smaller sub-tasks. Every sub-task is scheduled independently and initiates a complex mechanism of agents negotiations. For example, the agent of a simple station pickup task matches an optimum resource (the most suitable car) and creates the subordinated Wash Task which agent provides search of the driver capable to wash delivered car in time. In more complex case the quantity of subtasks can be essential more, and tasks can establish the interdependences which represent relationships of cause and effect. For example, for a reservation scheduling the rental agent carries out car search, the agent of the car searches for the best driver for delivery to the client, the agent of the driver aspires to find other driver (runner) to come back home after car delivery etc. Not only continuous matching between demands and resources is carried out during such interaction, but also negotiations of agents in which conflicts become apparent and solved. For example, if during the matching the rental has chosen the car which is already reserved by other client, then, probably the given car will refuse to the new rental if there is no possibility to change the given situation, or will try to find other car to the client within the limits of the set restrictions and even breaking these restrictions. Under certain conditions new rental can "move" itself in relation to time which is most preferable to it and as a result of found compromise to be delivered, for example, before demanded term if all drivers are already allocated for other rentals. Degree of a possible dissatisfaction of each agent caused by an exit for set restrictions during finding of the compromise, is regulated by functions of bonuses and penalties for infringement of restrictions that can be adjusted by the user or be accepted by default. Thereby, the links established during interaction of agents, are not created once and for all. On the contrary, even on result achievement, agents do not stop and continue to search for possibilities for improvement of the position, and, accordingly, at the first such possibility can reconsider the made decisions, to break old links and to establish new ones, thus reaching new compromises by various criteria. Proactivity allows to improve schedule when the system is idle or some unpredictable events happen (e.g. driver becomes available). For example, if for any reasons, the chosen driver becomes not available (breakage, accident etc.) his agent will be activated and can easily find all rentals which are planned now for the given driver and to inform them about unavailability of a resource. These rentals are activated now and start to search for other drivers that will allow to re-plan routes of trips operatively. The offered approach, in comparison with classical approaches to optimization, provides following important advantages: Allows to consider increasing complexity of modern business by addition of the new agents representing interests of new participants, tasks or resources; Allows to solve the optimization problems in the big size transport systems which operate with thousands of interrelated rentals, tasks and resources having various features; Provides the opportunity of event-driven dynamic scheduling in real time at the expense of dynamic revealing and solving conflicts in already available schedule (without its full reorganization); guarantees an individual approach to any rentals and resources as with system development corresponding agents can get own sets of criteria, preferences and restrictions; Provides flexibility of schedules being created. It allows both a user and agents to change criteria, preferences and restrictions during the work, also if necessary to balance between various criteria and restrictions changing the service level (car class) to delivery time or cost, delivery risks, driver discomfort, etc; Allows to explain made decisions to the user and to give the chance to correct offered schedules conveniently. The developed approach is based on design decisions created earlier for scheduling of tankers and trucks [5-7]. 4. ONTOLOGY OF RENT-A-CAR BUSINESS In the developed approach a domain ontology is used not only for separating specific knowledge of a rent-a-car domain from a program code, but also for representing the schedule by semantic network of corresponding concepts and relations. Playing a role of "dictionary" of the system, ontology allows to describe the concrete situations (scenes) representing links between demands and resources. For example, a scene shows that at present time the given car is reserved for the given rental, the given driver is driving this car etc. Such description of a situation allows agents to track all important links between objects, operations and participants that represent all complexity and interdependencies in the given problem situation. The ontology network is presented on the figure 1. Figure 1. Fragment of rent-a-car ontology. Ontology is divided into three main parts (sub-ontologies). Basic part of ontology includes concepts describing the rent-a-car domain, such as a reservation, a car, a driver, a type of customer etc. The second group of concepts and relations represents domain of scheduling process. It includes tasks, options and operations. They form network representing both the data about the schedule, and the metadata about cause-effect relations underlying the schedule. Thanks to this part of ontology agents can rapidly move around the scenes, change and transform entire blocks of operations, identify the complex relationships between different resources and

4 assess their alleged actions on the basis of the cumulative effect. figure 2 is an example describing the scene with the schedule of several resources. The third group is those concepts that describe the basic laws of the functioning of the world of agents, determine the criteria by which agents make decisions. First of all, this is graphs of the bonuses and penalties, expressing a degree of undesirability that a parameter possesses the certain value. These include penalty for late collection of cars, early delivery, overtime of the drivers, late reservations, long-standing drivers in the field without cars, etc. In the process of setting these values the user specifies the importance of a criterion, thereby balancing the decision-making logic. calculate preliminary (Average Cost) and full cost of rental placing on a resource (Marginal Cost) and to break an initial task into some smaller and to direct inquiries under their decision to corresponding agents. The most important function of such agent is its ability to create "promise" (obligation) to carry out task landing to a resource for the specified sum. Delivery agent Car agent Washing Task Collection agent Driving Task Driver agent Car Task Task delegation by task agent Task dividing by resource agent Runner Task GoHome Task Resources Interrogation by tasks agents Figure 2. Example of scene Representation of the specified knowledge as ontology allows to separate them from a program code and thereby to facilitate the modification and development of the system in future. 5. MULTIAGENT WORLD OF RENT_A_CAR SCHEDULING In the developed approach the schedule is the network of the tasks (operations to be implemented) linked among themselves at various levels. Complex tasks in schedule are decomposed on subtasks and agents of these subtasks can work independently but in coordinated way, trying to find the best options of the decisions on resources allocation. Resources in turn negotiate best decisions for improving their key performance indicators (KPI). The architecture of multi-agent world for rent-a-car business is shown on figure 3, where the main agents classes and relations between them are presented. In this architecture every task and sub-task has its own agent which is called Demand-agent. Its main goal searching the most suitable variants of the task performance, interrogation and negotiation with resources because the resource can refuse or accept the order. The various agents are created for various tasks types, they differ by decision-making logic, by possibilities to readdress the task or its part to other agents. Demand-agents can inform other agents about results of the activity. The main role of Resource Agents allocation cost estimation of any task (rental) on the given resource. Resource agent is able to Figure 3. Architecture of Multi-Agent World of Scheduling As a whole, developed multi-agent scheduler operates with 7 types of tasks, to each of them the demand-agent of the class corresponds: Delivery; Washing Task; Driving Task; Collect; Runner Task; Car Task; Go Home Task. Besides, it is used two types of resources in the developed architecture: Car and Driver. The majority of events are initiated on demand party s (scheduling and re-scheduling of a new reservation, change of details of cars collection, etc). But in case of proactivity work, agents of resources become more active. They can independently ask all tasks for search of such task for which the given resource would be the best option. At revealing of such tasks the agent of a resource initiates process of their re-scheduling. Besides, when urgent events from the user come (some tasks fail and change of the drivers schedule) "the suffered" tasks initiate re-scheduling process. Figure 4 shows an example one of the main protocol agents negotiations: Scheduling task protocol (Protocol 1). The developed approach to scheduling has a number of advantages over other approaches, but it does not guarantee construction of the optimum plan for all resources for each moment of time as well. As a rule, approximations, average

5 indicators of prices values, various heuristics are used for improving decision in many points of decision-making. Besides, the mechanism of agent interactions convergence described above can bring essential distortion in logic of decision-making. All it leads to generation visible non-optimal resources allocations with inefficient use of some cars or drivers at some sites of the schedule. 6. ARCHITECTURE The architecture of the multi-regional system is presented on figure 5, where main components are shown and also integrated 3rd party software systems indicated. Figure 4. Scheduling task protocol (Protocol 1). To solve this problem we use different kinds of agents proactivity. First of all it is proactivity of schedule agent which can review resulting schedule from time to time and report on inefficient use of different resources. Using this information preselected agents can try to renegotiate their deals. Secondly, all resources agents can get proactivity cycles and look for the most favourable tasks for them and re-negotiate deal with existing tasks. It is only become possible if key indicators for the whole systems are improving in this case. For example, the driver is waiting in the field after car delivery when he is taken for new work. If from the moment of this task scheduling it has passed a lot of time most likely new runner has already appeared which would pick up the driver faster that means cheaper. Thus, in the agent world the proactivity threads are working constantly for step by step optimization of the schedule. At this time the system can destabilize old links between tasks and resources and break integrity of the schedule temporarily. But then agents refresh links and recover schedule which becomes more solid after such provocations and is based on actual data. Ongoing proactivity is related to KPI of the resulting schedule. If these KPIs do not improve then one type of proactivity is reduced to minimum, and other types of proactivity start to push the schedule to the optimum more aggressively As a result developed approach uses trial and error method which helps to improve complex schedule and self-regulate internal system activities in a real time. Figure 5. Solution Architecture. The core of solution is a network of multi-agent schedulers which are dedicated for each region (one region can contain a number of stations) and optimize the total plan for stations in one region. The scheduler (Scheduling Engine) enables allocating cars to rentals, scheduling instructions for drivers to deliver/collect/wash cars, pick-up other driver or transport drivers to collection points. The Engine can reschedule in case of changes in reservations, exceptions etc. In first version of solution all regional schedulers are working independently. In future they will interact and coordinate their decision on solving scheduling issues for cars crossing boundaries of regions and share driver resources. Operational Data Source (ODS) is a read-only storage of operational information about cars, rentals, reservations, rate codes, non-revs, car models and stations. It is a mediator between existing fleet and rental management system and multi-agent schedulers. Multi-agent scheduler database is a relational database which stores operational information (rentals, cars & drivers schedules etc) and reference data (cars, makes, models, rate codes, stations, journey times etc). It is integrated to the internal data backbone to provide read/write access to various data for other components; Offline Archive is simple file storage in a folder on the logical disk. Data can be retrieved from it manually using any file manager. Operational data from Scheduler Database older than a defined time interval (e.g. 2 week) is regularly automatically archived to limit the size of the database and ensure stable performance of read/write operations. Data backbone is an internal component which provides a simplified read/write access to the data stored in permanent

6 storage (scheduler database). Integrated with all components: Archiving, Workflow support, JTM, Pre-processing, Scheduling Engine. The system is integrated with mobility solution which enables wireless communication with drivers at station or in-field. It is integrated with workflow support component to issue instructions to drivers and get confirmations of completion, ETA updates, information on failed tasks etc. Workflow support component is used by Scheduling Engine to issue instructions to drivers. It delivers confirmations and information about exceptions back to Scheduling Engine. Routing Software provides time estimation for travelling between two specific postcodes (postcodes sectors) for JTM component. It also provides visual representation of routes (optional). JTM provides driving time estimation info to Scheduling Engine and caches information from routing software to ensure acceptable performance. Pre-processing component exports information from ODS to scheduler database. It checks consistency of data against various rules and raises tasks for the user to validate automatic decisions (e.g. re-rents) or correct data (like missing or incorrect postcode); System Administrator can modify scheduling engine settings to adjust the scheduling logic. 7. USER INTERFACES The system provides a comprehensive desktop user interface for day-to-day Station activities (tasks, operational information about cars, drivers and their schedule), Fleet managers (fleet movements functionality) and Administrator (exception management and system maintenance). Client application is automatically deployed and updated on user PC via Java Web Start service. driver user selects driver in the list. Tasks are listed in the chronological order. Example of task list is presented on figure 7. Figure 6. Drivers screen. Figure 7. Task list. Figure 5. Tasks Screen. Tasks screen opens by default when Station user enters the system. This screen contains tasks for station user. The Drivers screen (figure 6) displays all drivers and their working hours planned for the current week (Rota): by weekdays and the total number of hours. To see tasks currently planned for a Figure 8. Settings screen. Settings screen (figure 8) allows system administrator to adjust the criteria, which define the scheduling logic.

7 8. PERFORMANCE METRIXES At present the system is tested on a network of stations of one of subsidiary large Rent-A-Car company. A number of performance matrixes which represent most important characteristics of the developed scheduler are presented in table below. The amount of cars 250 The amount of drivers 30 Average stream of rentals Average number of late task Average day speed of events receipt Time processing of one event Number of the tasks which are scheduled for one event Average drop factor (how many on the average tasks were expelled by the task which has lived to commit) The characteristic of negotiations volume and messaging intensity in the system Number of active agents Number of options on one decision The greatest possible depth of a negotiations chain Percent of the events leading to considerable reorganisation of the schedule for a day forward For what period the last solutions prior to the execution of schedule are made What percent of decisions is made long before (more than hour) the beginning of schedule execution reservations per day for one station Approximately the driver was late once for 5 minutes for one task on the average. Approximately 60 % of all tasks were late. About events per hour, including receipt of new rentals, events from handheld computers of drivers and from UI users Maximum sec; minimum 15 msec; average - 7 sec (70 % of all events were processed in the system less than 3 sec) From 150 till 200, splitting on types of tasks:runnertask 30.7, DrivingTask 7.28, WashingTask 6.64, Delivery 1.88, CarTask , Collect (at the tight schedule increase to 15 drops on one task) Task for which a resource is the car, establish connections, and task on drivers (depends on type of a task and a used resource),, thus, number of negotiations begins from 1500 messages task agents (nearby 2500 simultaneously), agents of cars 250, agents of drivers - 30 from 5-10 options in a not-peak mode to 50 in case of a great rescheduling number, for example, because of peak time with tight schedule; 10 levels (it is limited manually) ~40% 90 % of all executed decisions have been made minutes before fixing of the schedule and the execution beginning; less than 1 % Furthermore, figure 9 shows the system KPI state chart. It was derived from real system, which was working at 24 November 2008 at 3 stations. It is clearly visible when the urgent events come (at 7:31, 7:43, 7.48, 7.56) the schedule becomes worse, because of unavailabilities of cars and drivers. But a little bit late the system state becomes normal. Proactivity takes less than minutes to improve the schedule , , , , , , ,00 0, : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :13 9. Related works Figure 9. Schedule KPI state chart. In spite of significant progress in operational research in last decades [1] the opportunity for enterprises for the development of large-scale real time schedulers remains very limited. The schedulers offered by such large companies, as SAP, Oracle, Manugistics, i2, ILOG and others apply various versions of constraint programming methods and tools, based on combinatory search of options in depth, for example, a method of branches and borders. But producing of the optimum plan for the large transport company in one of available optimization engines takes about 8-10 hours but this plan is only 40% feasible. Also during this time the volume of orders can be essentially changed that will require to start planning all over again. In this situation many large transport companies still to contain staff of very skilled and expensive operators to carry out time-consuming manual planning. To reduce the number of options considered in combinatorial search new methods apply 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 best heuristics). In many cases heuristics and metaheuristics provide good quality of decisions in reasonable time by reducing search iterations [2-3]. One of the most known meta-heuristics is Simple Local Search Based Meta-heuristics (SLSBM) local optimization metaheuristics. 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 Ряд1

8 on this successful direction "fade". In last period of time also many other meta-heuristics become more and more popular inheriting other physical or biological concepts, for example Adaptive Memory Programming method which inherits the use of common memory of decisions. What is interesting, in last developments researchers apply mixed miscellaneous metaheuristics, in which several parallel algorithms are acting, and each of them suggest their own decision. As a result in last decades modern theory of optimization is naturally shifting to multi-agent paradigm. But the technology of scheduling in real time is still very primitive, and an opportunity of flexible adaptation of schedule by incoming events is only manually supported or uses open slot techniques (incremental planning). Developed approach [4-7] in many respects integrates the considered above modern ideas of the dynamic scheduling and optimization and provide a solid framework for developing various number of competing and cooperating agents implementing modern heuristics and metaheuristics of optimization. It helps significantly to increase quality and efficiency of scheduling and make results more clear, understandable and adjustable for end-users, and also to reduce time of system development, make system more flexible and efficient. At present the developed approach was used in designing a family of intelligent schedulers for different applications [8]. On the basis of this approach solutions were built for Ocean Logistics [9] and Truck Logistics [10-11], Taxi business and some other domains [12-13]. Future works will be focused on research and design activities in the area of Emergent Intelligence in complex multiagent systems for scheduling and other applications [14]. 10. CONCLUSIONS The developed system can be considered as a one of the first industrial multi-agent systems for dynamic scheduling. Already now the system demonstrates quite intelligent behavior being able to perceive the events in real time, to generate schedule for many conflicting participants, execute formed plan and monitor results. When differences between the plan and the fact is recognized for any of the participants then systems triggers rescheduling process trying to keep in match plan and reality. And if system has enough time it is also constantly trying to improve given KPIs proactively and self-regulating its own activities. At the same time there are still a lot of R&D issues which need further investigation including quality of produced schedules, productive interaction with users, introducing adequate changes in To-Be business processes, balancing criteria and freezing last minute changes, etc. For example, frequent errors and delays of drivers, incorrect addresses of deliveries and collections, fail driving tasks can lead the system to start long ripple effect of changes in last minute. Solving these problems is the main direction of the future developments and the roadmap of discussed solution. Our first results prove that multi-agent technology forms solid basis for addressing above issues and developing new types of solutions which can provide great opportunities for solving very complex scheduling and optimization problems in real time. 11. References [1] Handbook of Scheduling: Algorithms, Models and Performance Analysis. Edited by J. Y-T. Leung // Chapman & Hall / CRC Computer and Information Science Series [2] 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, [3] Metaheuristic Optimization Via Memory and Evolution: Tabu Search and Scatter Search.. Edited by Cesar Rego and Bahram Alidaee. // Kluwer Academic Publishers / Operational Research & Computer Science Series Boston- London, [4] Skobelev P.O. Open multi-agent systems for decision making support. // Avtometriya pp [5] Vittikh V.A., Skobelev P.О. The multi-agent models of interaction in demand-resource networks. // Automatica and Telemechanica pp [6] Andreev V.V., Vittikh V.A., Batischev S.V., Ivkushkin K.V., Minakov I.A., Rzevski G.A., Safronov A.V., Skobelev P.О. Methods and tools of developing multi-agent systems for decisions making support // News of the Russian Academy of Sciences. Theory and systems of control pp [7] Rzevski G.A., Skobelev P.O., Andreev V.V. Magenta Toolkit: A Set of Multi-Agent Tools for Developing Adaptive Real-Time Applications - Proceedings of International Conference of Multi-Agent and Holonic Systems (HoloMAS 2007) - Germany, June [8] 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 [9] Himoff, J., Skobelev, P.О., Wooldridge M. Multi-Agent Systems for Ocean Logistics Proceedings of 4-th International Conference on Autonomous Agents and Multi- Agent Systems (AAMAS 2005). Holland, July [10] 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 [11] Skobelev P.O., Glashchenko A.V., Grachev I.A., Inozemtsev S.V. Case Studies of Magenta i-scheduler for Road Transportation - Proceedings of 7-th International Conference on Autonomous Agents and Multi Agent Systems AAMAS US, Hawai, May [12] Andreev M.V., Rzevski G.A., Skobelev P.O., Shveykin P.K., A.Tsarev. Adaptive Planning for Supply Chain Networks - Proceedings of International Conference of Multi-Agent and Holonic Systems (HoloMAS 2007) - Germany, June [13] Andreev V.V., Glashchenko A.V., Rzevski G.A., Skobelev P.O., Shveykin P.K. Multi-Agent Systems for Dynamic Scheduling // International Conference on Agents and Artificial Intelligence (ICAART ), January 2009, Porto, Portugal, 2009 (in publ.). [14] Rzevski G.A., Skobelev P.O. Emergent Intelligence in Large Scale Multi-Agent Systems - Education and Information Technologies Journal, Issue 2, Volume 1, 2007