Operations Scheduling of Sugarcane Production Using Classical Gert Method (Part Ii: Preserve Operations, Harvesting and Ratooning)

Abstract

Graphical Evaluation and Review Technique (GERT) is a systems analysis technique for project management. GERT provides a visual picture of the system and helps to analyse the system in a less inductive manner. Therefore, the purpose of this paper is studying the application of project scheduling in agriculture, for operations scheduling of sugarcane production (preserve operations, harvesting and rationing) using classical GERT method in Khuzestan province of Iran. Results showed that the network model was able to answer any statistic all the questions concerning the project. GERT networks are increasingly becoming a powerful tool for modelling, scheduling, planning, controlling and analysing of agricultural mechanization projects.

Programación o planeación de actividades o recursos en la agricultura. Una revisión de literatura

“…Así mismo, se encuentra autores como Ferrer, et al (2008) y Arnaout y Maatouk (2010), que centran sus estudios en el cultivo de uva, en donde no solo tienen en cuenta la optimización de las operaciones y la reducción de costos, si no también incluyen la calidad del fruto en el momento de la recolección, ya que esto afecta significativamente la calidad del vino producido; Monjezi, et al (2016), también desarrolla su investigación en la agricultura, pero lo propone desde la programación de proyectos de producción de caña de azúcar. Bochtis, et al (2013), desarrolla una investigación en la que se centra en la planificación de operaciones secuenciales, pero incluye en su estudio la incertidumbre de la predicción de tiempos en el trabajo, lo que genera unos resultados más asertivos y aplicables a la industria agrícola.…”

Machine Learning Approach for Graphical Model-Based Analysis of Energy-Aware Growth Control in Plant Factories

“…10 shows the subgraphs under various λ in the corresponding graph structure shown in Fig. 9, focusing on the node subset {v 13 29 are used as explanatories for v 21 under any λ value; the result implies that these three variables are essentially important for explaining v 21 . The directed links consistently existing under different λ support the fact that there are very clear relationships among these variables.…”

“…The control of the production environment based on information processing [1], [2], [16] utilizing the measurement of crop conditions [17], [18] has been discussed well from the viewpoint of automated crop production [3], [19] since the late 1970s. Various applications of artificial intelligence technology and machine learning approaches have been studied to realize data-driven operation and provide decision making support to the plant factories; e.g., [20] proposed a classification algorithm for the detection of fungal diseases in crops, and [21] introduced the essence of fuzzy theory into the operation scheduling of crop production (see [22] for a comprehensive review of machine learning approaches applied to the agricultural domain).…”