Neural Network Modelling And Simulation Of The Scheduling

Rondon, Ricardo Lorenzo Avila; Carvalho, Adriano; Hernández, Guillermo Infante

doi:10.1007/978-0-387-09492-2_25

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The purpose of this research was to apply both ANN as well as multiple linear regression analysis to understand the relationship between entrepreneurial education and prior entrepreneurial exposure (as the two independent variables) and entrepreneurial action (as the dependent variable). A neural network is an artificial intelligence system, which is structurally designed to resemble the neural system and functionality of the human brain to address the kind of problems that the brain solves efficiently (Rondon et al, 2008;Ciampi & Gordini, 2013). Neural networks techniques include a wide category of flexible, non-linear, regression, discriminant and data reduction models, as well as non-linear dynamical systems.…”

Section: Methodology and Research Designmentioning

confidence: 99%

Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Botha

Pohl

Mubita

2020

Development Southern Africa

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Previous work on the relationships between entrepreneurship education, prior entrepreneurial exposure and entrepreneurial action has resulted in mixed findings. However, this work typically relies on linear models which may not adequately account for the relationships. Therefore, we explore artificial neural networks (ANN) to test non-linear relationships and compare these results with a linear regression model to understand the previous mixed findings. Data from 125 entrepreneurship graduates in Zambia revealed that a non-linear model best explained the variation in entrepreneurial action, whereby the relationship was cubic. These results explain some of the previously mixed findings and demonstrate the importance of educators, policy makers and scholars paying attention to non-linear relationships when aiming to promote and further understand entrepreneurship. Therefore, this paper has implications for educational initiatives aiming to augment entrepreneurship education, while also contributing to the development of theory explicating the relationship between entrepreneurial exposure, education and action.

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Section: Methodology and Research Designmentioning

confidence: 99%

Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Botha

Pohl

Mubita

2020

Development Southern Africa

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“…The option of automatic architecture selection was chosen. The percentages for training and validation of the network are set based on the values implicit in the Software, and the use of equal percentages by other researchers [45][46][47]. It was decided to accept only one step after the error increase, and 0.0001 of the minimum change relative to the training error, as a criterion for stopping the training.…”

Section: Selection Of Study Variablesmentioning

confidence: 99%

Management and Production Engineering Review

Perez-Campdesuner¹,

Miguel-Guzan²,

García-Vidal³

et al. 2020

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Labor absenteeism is a factor that affects the good performance of organizations in any part of the world, from the instability that is generated in the functioning of the system. This is evident in the effects on quality, productivity, reaction time, among other aspects. The direct causes by which it occurs are generally known and with greater reinforcement the diseases are located, without distinguishing possible classifications. However, behind these or other causes can be found other possible factors of incidence, such as age or sex. This research seeks to explore, through the application of neural networks, the possible relationship between different variables and their incidence in the levels of absenteeism. To this end, a neural networks model is constructed from the use of a population of more than 12,000 employees, representative of various classification categories. The study allowed the characterization of the influence of the different variables studied, supported in addition to the performance of an ANOVA analysis that allowed to corroborate and clarify the results of the neural network analysis.

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“…In the subsequent iterations, the weights are modied and weighted processing times recalculated and later the same heuristic is applied. Rondon et al [13] analyzed the scheduling process on single machine supported by neural networks. The operating variables studied included e.g.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction

Rosłon

Kulejewski

2019

Open Engineering

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Practical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.

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Neural Network Modelling And Simulation Of The Scheduling

Cited by 4 publications

References 10 publications

Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Modelling the relationship between prior entrepreneurial exposure, entrepreneurship education and entrepreneurial action using neural networks

Management and Production Engineering Review

A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction

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