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Design and Modelling Approaches for Advanced Agricultural Fleet Management Systems

Design and Modelling Approaches for Advanced Agricultural Fleet Management Systems
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Author(s): Dionysis D. Bochtis (University of Aarhus, Denmark), Claus G. Sørensen (University of Aarhus, Denmark)and Stavros G. Vougioukas (Aristotle University of Thessaloniki, Greece)
Copyright: 2011
Pages: 20
Source title: Agricultural and Environmental Informatics, Governance and Management: Emerging Research Applications
Source Author(s)/Editor(s): Zacharoula Andreopoulou (Aristotle University of Thessaloniki, Greece), Basil Manos (Aristotle University of Thessaloniki, Greece), Nico Polman (Wageningen University, The Netherlands)and Davide Viaggi (University of Bologna, Italy)
DOI: 10.4018/978-1-60960-621-3.ch008

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Abstract

Knowledge based management of the production is the key to future sustainable and rational agricultural production. The maximisation of the agricultural machine productivity is an important element in the continued efforts of planning and controlling resource input in agriculture. Current consensus depicts agricultural fleet management must adapt a hybrid architecture for the planning efforts with a combination of deliberative and reactive approaches. The transfer of methodologies from the established operations research area has proved beneficial to the planning of agricultural operations as part of a fleet management system. By using deliberated abstractions and representations, the specific planning of agricultural operations can be cast as instances of well-known operational research problems. In this way, the established well proved solution methodology from these problems can be seen to enhance agricultural field operations planning. Key requirements for an envisioned future fleet management system include a positive cost-benefit, flexibility in relation to specific applications of the system, a simple user interface requiring no heavy learning efforts, automatic data logging and data storage, integrations options with other internal management tools, and effective planning and optimisation algorithms adaptive to the actual on-the-farm conditions.

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