Multi-Agent System Interaction in Integrated SCM\ud

Coordination between organizations on strategic, tactical and operation levels leads to more effective and efficient supply chains. Supply chain management is increasing day by day in modern enterprises.. The environment is becoming competitive and many enterprises will find it difficult to survive if they do not make their sourcing, production and distribution more efficient. Multi-agent supply chain management has recognized as an effective methodology for supply chain management. Multi-agent systems (MAS) offer new methods compared to conventional, centrally organized architectures in the scope of supply chain management (SCM). Since necessary data are not available within the whole supply chain, an integrated approach for production planning and control taking into account all the partners involved is not feasible. In this study we show how MAS architecture interacts in the integrated SCM architecture with the help of various intelligent agents to highlight the above problem

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt

Flexible Decision Control in an Autonomous Trading Agent

An autonomous trading agent is a complex piece of software that must operate in a competitive economic environment and support a research agenda. We describe the structure of decision processes in the MinneTAC trading agent, focusing on the use of evaluators – configurable, composable modules for data analysis and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports sales and procurement decisions, and how those decision processes can be modified in useful ways by changing evaluator configurations. To put this work in context, we also report on results of an informal survey of agent design approaches among the competitors in the Trading Agent Competition for Supply Chain Management (TAC SCM).autonomous trading agent;decision processes

Integrated engineering environments for large complex products

An introduction is given to the Engineering Design Centre at the University of Newcastle upon Tyne, along with a brief explanation of the main focus towards large made-to-order products. Three key areas of research at the Centre, which have evolved as a result of collaboration with industrial partners from various sectors of industry, are identified as (1) decision support and optimisation, (2) design for lifecycle, and (3) design integration and co-ordination. A summary of the unique features of large made-to-order products is then presented, which includes the need for integration and co-ordination technologies. Thus, an overview of the existing integration and co-ordination technologies is presented followed by a brief explanation of research in these areas at the Engineering Design Centre. A more detailed description is then presented regarding the co-ordination aspect of research being conducted at the Engineering Design Centre, in collaboration with the CAD Centre at the University of Strathclyde. Concurrent Engineering is acknowledged as a strategy for improving the design process, however design coordination is viewed as a principal requirement for its successful implementation. That is, design co-ordination is proposed as being the key to a mechanism that is able to maximise and realise any potential opportunity of concurrency. Thus, an agentoriented approach to co-ordination is presented, which incorporates various types of agents responsible for managing their respective activities. The co-ordinated approach, which is implemented within the Design Co-ordination System, includes features such as resource management and monitoring, dynamic scheduling, activity direction, task enactment, and information management. An application of the Design Co-ordination System, in conjunction with a robust concept exploration tool, shows that the computational design analysis involved in evaluating many design concepts can be performed more efficiently through a co-ordinated approach