e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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A belief-rule-based inference method for aggregate production planning under uncertainty


Finding high-performance solutions for aggregate production planning (APP) poses a significant challenge for both academics and practitioners alike. In real-world problems, severe demand fluctuations make forecasts hardly reliable. Forecast errors can be biased and magnifying from immediate to distant periods, and unstable demands are usually forecast in uncertain forms. For APP under uncertain demands, a new hierarchical belief-rule-based inference (BRBI) method is proposed. As an expert system with a belief-rule structure, BRBI can assist decision-makers in planning production, workforce and inventory levels with corresponding information representation, causal inference and identification algorithms. Operational data and expert knowledge can be employed to construct, initialise, and adjust the belief-rule base (BRB). An inference engine algorithm is developed to handle both deterministic and interval inputs. In order to make the method applicable to both continuous and discrete production settings, continuous mode and switching mode for BRBI are proposed using different transformation techniques. To approximate hidden patterns in APP situations, simultaneous identification and two-step identification for structure and parameter of BRB are developed. The two-step identification contains a belief k-means (BKM) clustering algorithm extended from k-means and fuzzy c-means. BKM ensures that an optimal cluster can both facilitate human cognition and improve accuracy of identification and inference. A paint-factory example is utilised to conduct comparative studies and sensitivity analyses in deterministic forecast context, and an automotive production example is implemented to illustrate BRBI's advantage in interval forecast context and to contrast simultaneous identification and two-step identification.

  • CN
  • GB
  • Huazhong_Univ_Sci_&_Technol_HUST (CN)
  • Univ_Manchester (UK)
Data keywords
  • knowledge
  • knowledge based
  • reasoning
Agriculture keywords
    Data topic
    • information systems
    • semantics
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
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      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.