Machinery management in bio-production systems: planning and scheduling aspects

 D. D. Bochtis(Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Aarhus, Blichers Alle´ 20, P.O. box 50, Greece) Abstract: Most operations in bio-production systems involve a number of highly interconnected tasks executed by co-operating machinery systems operating in series or in parallel.  An envisioned future team of identical field-robots could represent an example of the former case, while machinery systems including a number of primary units supported by a number of service (mainly transport) units involved in “output material flow” operations, such as harvesting, as well as in “input material flow” operations, such as spraying and fertilising, could represent examples of the later. the efficient execution of such operations requires considerable efforts in terms of scheduling and planning.  Here, a classification scheme for the management task of planning and scheduling for bio-production machinery systems is proposed, as a first step towards implementing appropriate management tools used in industrial management domain.  The identifications of the characteristics of the decision problems related to the management of these systems can provide the basis for their mapping to the appropriate operational research approaches.Keywords: agricultural machinery management, field logistics, B-patterns, biomass supply chain, farm management, farm machinery Citation: Bochtis D D.  Machinery management in bio-production systems: Planning and scheduling aspects.  Agric Eng Int: CIGR Journal, 2010, 12(2): 55-63. &nbsp

Field Operation Planning for Agricultural Vehicles: A Hierarchical Modeling Framework

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Field Operation Planning for Agricultural Vehicles: A Hierarchical Modeling Framework. Manuscript PM 06 021. Vol. IX. February, 2007

Optimal Dynamic Motion Sequence Generation for Multiple Harvesters

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 9 (2007): Optimal Dynamic Motion Sequence Generation for Multiple Harvesters. Manuscript ATOE 07 001. Vol. IX. July, 2007

Biomass supply chain event management

 D. K. Folinas1, D. D. Bochtis2, C. G. Sørensen2, P. Busato3(1. ATEI Thessaloniki, Department of Logistics, Greece; 2. Department of Biosystems Engineering, Faculty of Agricultural Sciences, Aarhus University, Blichers Alle´ 20, P.O. box 50;   3. DEIAFA Department,Faculty of Agriculture, University of Turin, Via Leonardo da Vinci 44, 10095, Grugliasco, Turin, Italy) Abstract: The biomass supply chain constitutes a system that is highly dynamic and stochastic.  The developed and proposed systems architectures for the management of the supply chains of typical industrial products do not directly apply to the case of the biomass supply chain

Towards a swarm robotic system for autonomous cereal harvesting

Swarm robotics is an emerging technology that has the potential to revolutionise precision agriculture by coordinating fleets of small autonomous vehicles to minimise soil damage, increase farming resolution, lower the cost of automation, and provide solutions that are intrinsically safer and more sustainable than large monolithic systems. Here, we propose a novel swarm robotic system for autonomous harvesting of cereal crops such as wheat and barley. In contrast to existing agricultural swarm robotic systems, we intend to use small autonomous versions of traditional agricultural vehicles, in an attempt to narrow the skills gap for future end-users

Voice-driven fleet management system for agricultural operations

Food consumption is constantly increasing at global scale. In this light, agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products. However, due to by environmental and biological factors (e.g. soil compaction) the weight and size of the machinery cannot be further physically optimized. Thus, only marginal improvements are possible to increase equipment effectiveness. On the contrary, late technological advances in ICT provide the ground for significant improvements in agri-production efficiency. In this work, the V-Agrifleet tool is presented and demonstrated. V-Agrifleet is developed to provide a “hands-free” interface for information exchange and an “Olympic view” to all coordinated users, giving them the ability for decentralized decision-making. The proposed tool can be used by the end-users (e.g. farmers, contractors, farm associations, agri-products storage and processing facilities, etc.) order to optimize task and time management. The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations. Its vendor-independent architecture, voice-driven interaction, context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system

Optimal route planning of agricultural field operations using ant colony optimization

Farming operations efficiency is a crucial factor that determines the overall operational cost in agricultural production systems.  Improved efficiency can be achieved by implementing advanced planning methods for the execution of field operations dealing, especially with the routing and area coverage optimisation aspects. Recently, a new type of field area coverage patterns, the B-patterns, has been introduced.  B-patterns are the result of a combinatorial optimisation process that minimizes operational criterions such as, the operational time, non-working travelled distance, fuel consumption etc.  In this paper an algorithmic approach for the generation of B-patterns based on ant colony optimisation is presented.  Ant colony optimization metaheuristic was chosen for the solution of the graph optimisation problem inherent in the generation of B-patterns.  Experimental results on two selected fields were presented for the demonstration of the effectiveness of the proposed approach. Based on the results, it was shown that it is feasible to use ant colony optimization for the generation of optimal routes for field area coverage while tests made on the resulting routes indicated that they can be followed by any farm machine equipped with auto-steering and navigation systems

A Diagnostic System for Improving Biomass Quality Based on a Sensor Network

Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures

Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p