The Experts below are selected from a list of 1587 Experts worldwide ranked by ideXlab platform

Jean-marc Frayret - One of the best experts on this subject based on the ideXlab platform.

  • multi behavior Agent model for Planning in supply chains an application to the lumber industry
    Robotics and Computer-integrated Manufacturing, 2008
    Co-Authors: Pascal Forget, Sophie Damours, Jean-marc Frayret
    Abstract:

    Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for Planning systems to present Planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, Agent-based technology takes advantage of the ability of Agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, Planning Agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior Planning Agent model using different Planning strategies when decisions are supported by a distributed Planning system. The implementation of this solution is realized through the [email protected] experimental Agent-based platform, dedicated to supply chain Planning for the lumber industry.

  • Multi-behavior Agent model for Planning in supply chains: An application to the lumber industry
    Robotics and Computer-Integrated Manufacturing, 2008
    Co-Authors: Pascal Forget, Sophie D'amours, Jean-marc Frayret
    Abstract:

    Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for Planning systems to present Planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, Agent-based technology takes advantage of the ability of Agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, Planning Agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior Planning Agent model using different Planning strategies when decisions are supported by a distributed Planning system. The implementation of this solution is realized through the FOR@C experimental Agent-based platform, dedicated to supply chain Planning for the lumber industry. © 2007 Elsevier Ltd. All rights reserved.

Pascal Forget - One of the best experts on this subject based on the ideXlab platform.

  • multi behavior Agent model for Planning in supply chains an application to the lumber industry
    Robotics and Computer-integrated Manufacturing, 2008
    Co-Authors: Pascal Forget, Sophie Damours, Jean-marc Frayret
    Abstract:

    Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for Planning systems to present Planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, Agent-based technology takes advantage of the ability of Agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, Planning Agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior Planning Agent model using different Planning strategies when decisions are supported by a distributed Planning system. The implementation of this solution is realized through the [email protected] experimental Agent-based platform, dedicated to supply chain Planning for the lumber industry.

  • Multi-behavior Agent model for Planning in supply chains: An application to the lumber industry
    Robotics and Computer-Integrated Manufacturing, 2008
    Co-Authors: Pascal Forget, Sophie D'amours, Jean-marc Frayret
    Abstract:

    Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for Planning systems to present Planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, Agent-based technology takes advantage of the ability of Agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, Planning Agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior Planning Agent model using different Planning strategies when decisions are supported by a distributed Planning system. The implementation of this solution is realized through the FOR@C experimental Agent-based platform, dedicated to supply chain Planning for the lumber industry. © 2007 Elsevier Ltd. All rights reserved.

Massimo Poesio - One of the best experts on this subject based on the ideXlab platform.

  • The TRAINS project: A case study in building a conversational Planning Agent
    Journal of Experimental and Theoretical Artificial Intelligence, 1995
    Co-Authors: James F. Allen, Marc Light, Chung Hee Hwang, Tsuneaki Kato, Nathaniel Martin, Bradford Miller, Peter Heeman, Lenhart K. Schubert, George Ferguson, Massimo Poesio
    Abstract:

    The Trains project is an effort to build a conversationally proficient Planning assistant. A key part of the project is the construction of the Trains system, which provides the research platform for a wide range of issues in natural language understanding, mixedinitiative Planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year we have produced a demonstration system that focused on a dialog that illustrates particular aspects of our research. The commitment to building complete integrated systems is a significant overhead on the research, but we feel it is essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and our experience with the effort so far.

  • The TRAINS project: A case study in defining a conversational Planning Agent
    Journal of Experimental and Theoretical AI, 1995
    Co-Authors: J. Allen, Marc Light, Chung Hee Hwang, Tsuneaki Kato, N.g: Martin, B W Miller, Peter Heeman, Lenhart K. Schubert, George Ferguson, Massimo Poesio
    Abstract:

    The TRAINS project is an effort to build a conversationally proficient Planning assistant. A key part of the project is the construction of the TRAINS system, which provides the research platform for a wide range of issues in natural language understanding, mixed-initiative Planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year we have produced a demonstration system that focused on a dialog that illustrates particular aspects of our research. The commitment to building complete integrated systems is a significant overhead on the research, but we feel it is essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and our experience with the effort so far. .pp This paper is to appear in the Journal of Experimental and Theoretical AI, 1995.

Sophie Damours - One of the best experts on this subject based on the ideXlab platform.

  • multi behavior Agent model for Planning in supply chains an application to the lumber industry
    Robotics and Computer-integrated Manufacturing, 2008
    Co-Authors: Pascal Forget, Sophie Damours, Jean-marc Frayret
    Abstract:

    Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for Planning systems to present Planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, Agent-based technology takes advantage of the ability of Agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, Planning Agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior Planning Agent model using different Planning strategies when decisions are supported by a distributed Planning system. The implementation of this solution is realized through the [email protected] experimental Agent-based platform, dedicated to supply chain Planning for the lumber industry.

D.b. Simmons - One of the best experts on this subject based on the ideXlab platform.

  • Software Project Planning Associate (SPPA): a knowledge-based approach for dynamic software project Planning and tracking
    Proceedings 24th Annual International Computer Software and Applications Conference. COMPSAC2000, 2000
    Co-Authors: Ching-seh Wu, D.b. Simmons
    Abstract:

    Software project Planning can be one of the most critical activities in the modern software development process. Without a realistic and objective software project plan, the software development process cannot be managed in an effective way. Over-runs of 100-200% are common. Some software projects never deliver anything. Managers have difficulty understanding and visualizing the software development process defined in a software project plan. The Software Project Planning Associate (SPPA), developed in the Java programming language, is accessed through standard World Wide Web browsers and designed to assist a software project manager in objectively initializing a software project plan, refining/improving a plan, organizing, staffing, scheduling, measuring, visualizing, controlling, tracking, predicting and data collecting. The resources, tasks, schedules and milestones of the software project are described in the plan. As software development process evolves, measurements are unobtrusively gathered and compliance to the software project plan is reported. Software process effectiveness predictions are made and recommendations are dynamically reported suggesting the software development that should be executed to best comply with the software project plan. The SPPA was developed according to a knowledge-base plan model to allow the manager to keep track of the software plan component. SPPA, with the assistance of a software project Planning Agent, reports problems and suggests problem solutions to the manager. SPPA helps managers assure that a project is within budget, on time and to customer satisfaction.