Multi-agent Planning of the Network Traffic between Nanosatellites and Ground Stations

AbstractMulti-agent technologies application for adaptive planning of communication sessions establishment requests with nanosatellites in the ground stations network in response to the arising events, considering constraints, is considered. Mathematical problem statement of adaptive communication sessions scheduling is given. Method of coupled interactions extension based on the demand-resource networks model for operative requests allocation for communication sessions between ground stations and nanosatellites implementation is described

A Strategy for Managing Complexity of the Global Market and Prototype Real-time Scheduler for LEGO Supply Chain

ABSTRACT The paper describes main features of a strategy for managing complexity of the global market and real-time scheduling multi-agent system designed for the LEGO Company. The design is based on Multi-Agent Technology Group (MATech) own strategy blueprint and multi-agent platform, which provide real-time adaptive event-driven scheduling to replenish products to LEGO Branded Retail stores. The prototype system has been used to schedule 20 US-based LEGO retail outlets for a yearlong trial period and has achieved the following results: • Reduction of lost sale from 40% to 16%; • Increase in service level from 66% to 86%; • Increase in profitability 56% to 81%. The results show a considerable potential value for full scale LEGO supply chain multi-agent solution which would be able to dynamically and adaptively re-schedule deliveries in real time

Knowledge-driven adaptive production management based on real-time user feedback and ontology updates

Abstract-This paper presents the principles of the knowledgedriven adaptive management in manufacturing. The problems of real-time resource allocation, reaction to the unexpected events, on-the-fly update of the knowledge stored in ontology are considered. The possibilities of simultaneous change of the existing factory or workshop processes and schedules according to the information provided by the users are described. Finally, the possible ways of development of the presented approach and its application in production resource management are considered

Emergent Intelligence in Smart Ecosystems: Conflicts Resolution by Reaching Consensus in Resource Management

A new emergent intelligence approach to the design of smart ecosystems, based on the complexity science principles, is introduced and discussed. The smart ecosystem for resource management is defined as a system of autonomous decision-making multi-agent systems capable to allocate resources, plan orders for resources, and to optimize, coordinate, monitor, and control the execution of plans in real time. The emergent intelligence enables software agents to collectively resolve conflicts arising in resource management decisions by reaching a consensus through a process of detecting conflicts and negotiation for finding trade-offs. The key feature of the proposed approach is the ontological model of the enterprise and the method of collective decision-making by software agents that compete or cooperate with each other on the virtual market of the digital ecosystem. Emergent intelligent systems do not require extensive training using a large quantity of data, like conventional artificial intelligence/machine learning systems. The developed model, method, and tool were applied for managing the resources of a factory workshop, a group of small satellites, and some other applications. A comparison of the developed and traditional tools is given. The new metric for measuring the adaptability of emergent intelligence is introduced. The performance of the new model and method are validated by constructing and evaluating large-scale resource management solutions for commercial clients. As demonstrated, the essential benefit is the high adaptability and efficiency of the resource management systems when operating under complex and dynamic market conditions

Adaptive Clustering through Multi-Agent Technology: Development and Perspectives

The paper is devoted to an overview of multi-agent principles, methods, and technologies intended to adaptive real-time data clustering. The proposed methods provide new principles of self-organization of records and clusters, represented by software agents, making it possible to increase the adaptability of different clustering processes significantly. The paper also presents a comparative review of the methods and results recently developed in this area and their industrial applications. An ability of self-organization of items and clusters suggests a new perspective to form groups in a bottom-up online fashion together with continuous adaption previously obtained decisions. Multi-agent technology allows implementing this methodology in a parallel and asynchronous multi-thread manner, providing highly flexible, scalable, and reliable solutions. Industrial applications of the intended for solving too complex engineering problems are discussed together with several practical examples of data clustering in manufacturing applications, such as the pre-analysis of customer datasets in the sales process, pattern discovery, and ongoing forecasting and consolidation of orders and resources in logistics, clustering semantic networks in insurance document processing. Future research is outlined in the areas such as capturing the semantics of problem domains and guided self-organization on the virtual market

MAGENTA Technology Case Studies of Magenta i-Scheduler for Road Transportation

The paper describes functionality of Magenta Multi-Agent Logistics i-Scheduler Engine presented on AAMAS 2006 conferences and gives examples of its application in business domain. The i-Scheduler Engine was designed to be scalable without a risk of combinatorial explosion, in order to handle large transportation networks as a whole. The multi-agent architecture combined with semantic network allows for a very granular approach for every business entity of transportation network (client, order, cargo, truck, driver, etc) and balancing of their conflicting interests. The i-Scheduler considers individual constraints and, interestingly, specific preferences of customers, drivers, trucks, cargoes, etc. This results in a unique ability to combine inbound and outbound deliveries, different fleets or private networks, driving more value from finding effective backhauls and consolidations. The paper covers the history of development, architecture and current functionality of the engine and provides a set of case studies in different transportation networks which outline the most serious challenges Magenta overcame in each case. Categories and Subject Descriptors I.2.11 [Computing Methodologies]: Artificial Intelligence – distributed artificial intelligence General term

Emerging key requirements for future energy-aware production scheduling systems: A multi-agent and holonic perspective

The aim of this paper is to study a set of emerging key-enabling requirements for the design of multi-agent or holonic manufacturing systems dealing with the energy aware scheduling of future production systems. These requirements are organized according to three different views, namely informational, organizational and lifecycle views. It is shown that these emerging key-enabling requirements are not sufficiently addressed by the research literature. An illustrative futuristic example of a system complying with these requirements is provided. From this example, new research opportunities and issues can be easily found