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Operations Management in Agriculture
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Operations Management in Agriculture (Paperback)

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Synopsis

Operations Management in Agriculture bridges the knowledge gap on operations management for agricultural machinery. It complements traditional topics (cost of using and choosing machinery) with advanced engineering approaches recently applied in agricultural machinery management (area coverage planning and sequential scheduling). The book covers new technologies in bio-production systems (robotics, IoT) and environmental compliance by employing a systems engineering perspective with focuses on sub-systems, including advanced optimization, supply chain systems, sustainability, autonomous vehicles and IT-driven decision-making. It will be a valuable resource for students studying decision-making and those working to improve the efficiency, effectiveness and sustainability of production through machinery choice.

BusinessIndustry & industrial studiesPrimary industriesAgriculture & related industriesNatural World & The EnvironmentAgriculture & farmingAgricultural scienceTechnicalIndustrial chemistry & manufacturing technologiesIndustrial chemistryFood & beverage technology Publisher: Elsevier Science Publishing Co Inc Publication Date: 27/11/2018 ISBN-13: 9780128097861  Details: Type: Paperback Format: Books
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Dionysis D Bochtis is an Associate Professor in the Department of Engineering of Faculty of Science and Technology at Aarhus University, Denmark. He holds a PhD in Fleet management in bio-production systems, a MSc in Automation Control, and a B.Sc. in Exact Sciences (Physics). His primary research is industrial engineering focused on bio-production and related supply chain systems including activities relate to fleet management (for conventional and autonomous field machinery), field robots (high level control aspects: mission planning, path planning, task allocation), supply chain management for bio-energy bio-recourses and argi-food, field logistics (scheduling, area coverage planning, routing), and automation and decision Support Systems. Claus Gron Sorensen is the Head of the Operations Management unit in the Dept. of Engineering, Aarhus University. He holds a Ph.D. in Production and Operations Management and has over 25 year experience in production and operations management, information modelling in terms of decision processes, type of information, system analysis, and simulation and modelling of technology use in agriculture. Research topics have included resource analyses and optimisations, integration of technical management evaluations for whole farm analyses and optimisations, the feasibility of introducing robotic systems in agriculture and the development of management information systems (e.g. FP7, FutureFarm). Dimitris Kateris is currently an Adjunct Assistant Professor in Agriculture Engineering at the Technological Educational Institute of Epirus. He received his Ph.D. degree in Agricultural Engineering from Aristotle University of Thessaloniki in 2015, for his dissertation on "Intelligent system for fault diagnosis in agricultural tractor mechanical subsystems" He also holds M.Sc. degree from the Aristotle University of Thessaloniki in 2006 and two bachelor degrees in Agricultural Engineering and Water Resources from Aristotle University of Thessaloniki (2006) and Technological Educational Institute of Thessaly (1999), respectively. From 2012 to 2015 was an Adjunct Lecturer at the Department of Agriculture Technology, Technological Instute of Epirus. From 2002 to 2011 was an Adjunct Lecturer in Agriculture Engineering at the Department of Biosystems Engineering, Technological Instute of Thessaly. He concentrates his scientific research in the areas of Agricultural Machinery, Nondestructive testing and evaluation, Fault diagnosis techniques in mechanical subsystems, Vibration Analysis, Acoustic Emission Analysis, Machine Condition Monitoring, Neural Networks (NN), Machinery and Power systems, Safety and Reliability in Agricultural Machinery, Automation and New Technologies in Agricultural Machinery, Status check using Neural Networks and Self-Organizing Maps, Support Vector Machines (SVM).

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