IDEAS home Printed from https://ideas.repec.org/a/mth/jfsjnl/v8y2019i1p24.html
   My bibliography  Save this article

ABRAΗAM, a Semiautomatic Trainable Connoisseur Lab, as a Big Data Collection Platform

Author

Listed:
  • Michael Agrafiotis
  • Athanasios Zisopoulos
  • Konstantinos Spinthiropoulos

Abstract

The main idea of the invention is an expert system to execute a specific recipe through a food container and a moving and tilting pot with gases and visible cooking analysis. Details used to achieve professional results are- food preprocessing and weighted, normal warehouse, fridge, and deep fridge, overall positioning, computer control, monitoring the cooking gases and light spectrum and trainability. The machinery line has been designed for the semi-professional market but it has capabilities for catering, military operation, gourmet cooking and serving. The modular design permits an entry system with a few food containers, two pots, an oven and the base software running on a single PC while the advanced option uses thousands of food boxes, tenths of pots, pans, ovens and high expert systems intelligence running on real time industrial computers. All these give unlimited futuristic capabilities covering the 10-year time for Return on Investment. The cooking on this machine is a statistical Big Data cooking with Data Science principles.

Suggested Citation

  • Michael Agrafiotis & Athanasios Zisopoulos & Konstantinos Spinthiropoulos, 2019. "ABRAΗAM, a Semiautomatic Trainable Connoisseur Lab, as a Big Data Collection Platform," Journal of Food Studies, Macrothink Institute, vol. 8(1), pages 1-24, December.
    • Handle: RePEc:mth:jfsjnl:v:8:y:2019:i:1:p:24
    as

    Download full text from publisher

    File URL: https://www.macrothink.org/journal/index.php/jfs/article/download/14302/11317
    Download Restriction: no
    File URL: https://www.macrothink.org/journal/index.php/jfs/article/view/14302
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicolas, Lenoble & Yannick, Frein & Ramzi, Hammami, 2018. "Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data," European Journal of Operational Research, Elsevier, vol. 267(3), pages 958-976.
    2. Lenoble Nicolas & Frein Yannick & Hammami Ramzi, 2018. "Order batching in an automated warehouse with several vertical lift modules: Optimization and experiments with real data," Post-Print hal-01999890, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Li & Zhiyang Li, 2022. "Shuttle-Based Storage and Retrieval System: A Literature Review," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    2. Boysen, Nils & Schwerdfeger, Stefan & Stephan, Konrad, 2023. "A review of synchronization problems in parts-to-picker warehouses," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1374-1390.
    3. Çağla Cergibozan & A. Serdar Tasan, 2022. "Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 137-149, January.
    4. Pardo, Eduardo G. & Gil-Borrás, Sergio & Alonso-Ayuso, Antonio & Duarte, Abraham, 2024. "Order batching problems: Taxonomy and literature review," European Journal of Operational Research, Elsevier, vol. 313(1), pages 1-24.
    5. Qingfei Tong & Xinguo Ming & Xianyu Zhang, 2023. "Construction of Sustainable Digital Factory for Automated Warehouse Based on Integration of ERP and WMS," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mth:jfsjnl:v:8:y:2019:i:1:p:24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Macrothink Institute (email available below). General contact details of provider: http://jfs.macrothink.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.