Cargando…

A strategic decision framework using soft-computing for agri-food production: case study living lab in universities

Designing autonomous or semi-autonomous greenhouses that can supply food under extreme environmental conditions or restricted social distances is an endeavor that has to be considered under pandemic conditions such as COVID-19. However, generally advanced greenhouses have been designed using convent...

Descripción completa

Detalles Bibliográficos
Autores principales: Ponce, Pedro, Lugo, Esther, Bastida, Jose Hector, Fayek, Aminah Robinson, Molina, Arturo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Paris 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838512/
http://dx.doi.org/10.1007/s12008-022-01192-6
_version_ 1784869304039112704
author Ponce, Pedro
Lugo, Esther
Bastida, Jose Hector
Fayek, Aminah Robinson
Molina, Arturo
author_facet Ponce, Pedro
Lugo, Esther
Bastida, Jose Hector
Fayek, Aminah Robinson
Molina, Arturo
author_sort Ponce, Pedro
collection PubMed
description Designing autonomous or semi-autonomous greenhouses that can supply food under extreme environmental conditions or restricted social distances is an endeavor that has to be considered under pandemic conditions such as COVID-19. However, generally advanced greenhouses have been designed using conventional methodologies that are not integrated easily into reconfigurable designs. Moreover, those design methodologies are complex for novice product designers. This paper proposes a novel SDF (Strategic Decision Framework) to support reconfigurable agri-food production systems design. The framework proposed is based on the Integrated Product, Process, and Manufacturing System Development (IPPMD) reference model that uses reconfigurable manufacturing systems (RMS) and Fuzzy Cluster Mean (FCM) algorithms in its decision support system. As a result, the proposed methodology generates fuzzy clusters using degrees of membership that can describe the design constraints straightforwardly. Those fuzzy clusters support a hierarchical decision-making process, so the design process is easily implemented. Besides, the proposed methodology is deployed in a complex, highly non-linear system (a greenhouse) that has an internal ecosystem autonomously controlled by mechanical, electrical, digital, and telecommunication subsystems. Hence, an innovative design methodology implemented for advanced reconfigurable systems is presented. The results confirm that the proposed SDF can be implemented in complex reconfigurable design systems when the manufacturing decisions are unclear to decision-makers and designers. Thus, this methodology provides useful, coherent information regarding the design process that simplifies decision-making when designing a reconfigurable greenhouse. Besides, this research shows an entirely reconfigurable greenhouse as a living lab implemented at Tecnologico de Monterrey, Mexico City campus to validate the proposed SDF.
format Online
Article
Text
id pubmed-9838512
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer Paris
record_format MEDLINE/PubMed
spelling pubmed-98385122023-01-17 A strategic decision framework using soft-computing for agri-food production: case study living lab in universities Ponce, Pedro Lugo, Esther Bastida, Jose Hector Fayek, Aminah Robinson Molina, Arturo Int J Interact Des Manuf Original Paper Designing autonomous or semi-autonomous greenhouses that can supply food under extreme environmental conditions or restricted social distances is an endeavor that has to be considered under pandemic conditions such as COVID-19. However, generally advanced greenhouses have been designed using conventional methodologies that are not integrated easily into reconfigurable designs. Moreover, those design methodologies are complex for novice product designers. This paper proposes a novel SDF (Strategic Decision Framework) to support reconfigurable agri-food production systems design. The framework proposed is based on the Integrated Product, Process, and Manufacturing System Development (IPPMD) reference model that uses reconfigurable manufacturing systems (RMS) and Fuzzy Cluster Mean (FCM) algorithms in its decision support system. As a result, the proposed methodology generates fuzzy clusters using degrees of membership that can describe the design constraints straightforwardly. Those fuzzy clusters support a hierarchical decision-making process, so the design process is easily implemented. Besides, the proposed methodology is deployed in a complex, highly non-linear system (a greenhouse) that has an internal ecosystem autonomously controlled by mechanical, electrical, digital, and telecommunication subsystems. Hence, an innovative design methodology implemented for advanced reconfigurable systems is presented. The results confirm that the proposed SDF can be implemented in complex reconfigurable design systems when the manufacturing decisions are unclear to decision-makers and designers. Thus, this methodology provides useful, coherent information regarding the design process that simplifies decision-making when designing a reconfigurable greenhouse. Besides, this research shows an entirely reconfigurable greenhouse as a living lab implemented at Tecnologico de Monterrey, Mexico City campus to validate the proposed SDF. Springer Paris 2023-01-10 2023 /pmc/articles/PMC9838512/ http://dx.doi.org/10.1007/s12008-022-01192-6 Text en © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Ponce, Pedro
Lugo, Esther
Bastida, Jose Hector
Fayek, Aminah Robinson
Molina, Arturo
A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title_full A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title_fullStr A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title_full_unstemmed A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title_short A strategic decision framework using soft-computing for agri-food production: case study living lab in universities
title_sort strategic decision framework using soft-computing for agri-food production: case study living lab in universities
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838512/
http://dx.doi.org/10.1007/s12008-022-01192-6
work_keys_str_mv AT poncepedro astrategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT lugoesther astrategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT bastidajosehector astrategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT fayekaminahrobinson astrategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT molinaarturo astrategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT poncepedro strategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT lugoesther strategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT bastidajosehector strategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT fayekaminahrobinson strategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities
AT molinaarturo strategicdecisionframeworkusingsoftcomputingforagrifoodproductioncasestudylivinglabinuniversities