The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
A novel decomposition and distributed computing approach for the solution of large scale optimization models
Biomass feedstock production is an important component of the biomass based energy sector. Seasonal and distributed collection of low energy density material creates unique challenges, and optimization of the complete value chain is critical for cost-competitiveness. BioFeed is a mixed integer linear programming (MILP) problem model that has been developed and successfully applied to optimize bioenergy feedstock production system. It integrates the individual farm design and operating decisions with transportation logistics to analyze them as a single system. However, this integration leads to a model that is computationally demanding, leading to large simulation times for simplified case studies. Given these challenges, and in wake of the future model extensions, this work proposes a new computational approach that reduces computational demand, maintains result accuracy, provides modeling flexibility and enables future model enhancements. The new approach, named the Decomposition and Distributed Computing (DDC) approach, first decomposes the model into two separate optimization sub-problems: a production problem, focusing on on-farm activities such as harvesting, and a provision problem, incorporating the post-production activities such as transportation logistics. An iterative scheme based on the concepts from agent based modeling is adapted to solve the production and provision problems iteratively until convergence had been achieved. The computational features of the approach are further enhanced by enabling distributed computing of the individual farm optimization models. Simulation studies comparing the performance of the DDC approach with the rigorous MILP solution approach illustrated an order of magnitude reduction in computational time using the proposed DDC approach. Moreover, the solution obtained using the DDC approach was within +/- 5% of the rigorous MILP solution. This approach can be a valuable tool to solve complex supply chain optimization problems in other sectors where similar challenges are encountered. (C) 2011 Elsevier B.V. All rights reserved.
Inappropriate format for Document type, expected simple value but got array, please use list format