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The transportation problem is solved usually in three successive steps. These steps include: Finding the initial basic feasible solution, test of optimality, and moving towards optimality. There are many different methods that can be used to find the initial basic feasible solution. Out of these methods are: NorthWest Corner Method, Row Minima Meth...
Contexts in source publication
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... Repeat: Repeat steps 3 to 8 until the optimality condition is satisfied. Figures (1 -6) below provide the MATLAB scripts for every method covered in the Methodology section. Replicating this study's findings requires these scripts. ...
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... Optimality (MODI Method): The Modified Distribution (MODI) Method includes extra steps such as completing opportunity cost calculations, ensuring optimality, and making adjustments to allocations through a closed loop. The following Figures (1) to (6) illustrate the prepared MATAL scripts for the mentioned methods. ...
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... solved many example problems and case studies, with differences in the initial feasible basic solutions generated from each method using the prepared MATLAB scripts. Figures (1) to (6) (11) shows the MATLAB scripts outputs versus manually solved example for NWCM, Row Minima Method, Column Minima Method, Least Cost Method and Vogel's Approximation Method. Figure (12) compares the MATLAB output versus the LINGO code output for the same example. ...
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... (1) to (6) (11) shows the MATLAB scripts outputs versus manually solved example for NWCM, Row Minima Method, Column Minima Method, Least Cost Method and Vogel's Approximation Method. Figure (12) compares the MATLAB output versus the LINGO code output for the same example. As shown in Figures, we got the exact results for all methods. ...
Citations
Artificial neural networks (ANNs) have emerged as the technology that provides solutions to key issues arising in the field of automobile engineering regarding autonomous driving, predictive maintenance, energy control, and vehicle protection. This paper aims to present various uses of ANNs in car industry concerning data handling for continuous decision-making and adaptation. Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs) are all explored in relation to their ANN specific relevance to automobiles. The identified limitation also responds to issues associated with the integration of ANN such as data dependency, the computational load required, and questions related to the ethical use of AI decision making. This paper compares ANN techniques in an automotive context, explaining where they excel and where they could use improvement in terms of the tasks they are applied to. The strategies for phased implementation of the ANN framework, the performance evaluation for each stage of implementation, and the optimization methodologies are discussed below. Future direction highlights the future development of transformers, energy efficient models and raising concerns of ethical regulatory frameworks with regards to ANN driven systems. Thus, by such barriers overcoming, ANNs have a potential to significantly influence the further development of automotive engineering and make automobiles safer, more efficient and environmentally friendly. This study advances the discussion around intelligent mobility and provides the foundation on which future research in the field can build from.
