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

Abstract

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.