Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There i...Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.展开更多
To find the optimized levels of various casting parameters in the ductile iron casting, various casting defects and the rejection rate were observed from a medium scale foundry. The controlled values of different cast...To find the optimized levels of various casting parameters in the ductile iron casting, various casting defects and the rejection rate were observed from a medium scale foundry. The controlled values of different casting parameters such as pouring temperature, inoculation, carbon equivalent, moisture content, green compression strength, permeability and mould hardness were selected. Three different melts of metal with 0.4wt.%, 0.6wt.%, and 0.8wt.% inoculation （Fe-Si-Mg alloy and post inoculant） were produced using a 1-ton capacity coreless medium frequency induction furnace. L-27 orthogonal array with 3-level settings were chosen for the analysis. Responses for each run were observed. The signal-to-noise （S/N） ratio for each run was calculated using the Taguchi approach, and the optimized levels of different casting parameters were identified based on the SIN ratio. The analysis of variance for the casting acceptance percentage concludes that inoculation is the most significant factor affecting the castings＇ quality with a contribution percentage of 44%; an increase in inoculation results in a significant improvement in acceptance percentage of ductile iron castings. The experiment results showed that with the optimized parameters, the rejection rate was reduced from 16.98% to 6.07%.展开更多
文摘Due to the rapid development of logistic industry, transportation cost is also increasing, and finding trends in transportation activities will impact positively in investment in transportation infrastructure. There is limited literature and data-driven analysis about trends in transportation mode. This thesis delves into the operational challenges of vehicle performance management within logistics clusters, a critical aspect of efficient supply chain operations. It aims to address the issues faced by logistics organizations in optimizing their vehicle fleets’ performance, essential for seamless logistics operations. The study’s core design involves the development of a predictive logistics model based on regression, focused on forecasting, and evaluating vehicle performance in logistics clusters. It encompasses a comprehensive literature review, research methodology, data sources, variables, feature engineering, and model training and evaluation and F-test analysis was done to identify and verify the relationships between attributes and the target variable. The findings highlight the model’s efficacy, with a low mean squared error (MSE) value of 3.42, indicating its accuracy in predicting performance metrics. The high R-squared (R2) score of 0.921 emphasizes its ability to capture relationships between input characteristics and performance metrics. The model’s training and testing accuracy further attest to its reliability and generalization capabilities. In interpretation, this research underscores the practical significance of the findings. The regression-based model provides a practical solution for the logistics industry, enabling informed decisions regarding resource allocation, maintenance planning, and delivery route optimization. This contributes to enhanced overall logistics performance and customer service. By addressing performance gaps and embracing modern logistics technologies, the study supports the ongoing evolution of vehicle performance management in logistics clusters, fostering increased competitiveness and sustainability in the logistics sector.
文摘To find the optimized levels of various casting parameters in the ductile iron casting, various casting defects and the rejection rate were observed from a medium scale foundry. The controlled values of different casting parameters such as pouring temperature, inoculation, carbon equivalent, moisture content, green compression strength, permeability and mould hardness were selected. Three different melts of metal with 0.4wt.%, 0.6wt.%, and 0.8wt.% inoculation （Fe-Si-Mg alloy and post inoculant） were produced using a 1-ton capacity coreless medium frequency induction furnace. L-27 orthogonal array with 3-level settings were chosen for the analysis. Responses for each run were observed. The signal-to-noise （S/N） ratio for each run was calculated using the Taguchi approach, and the optimized levels of different casting parameters were identified based on the SIN ratio. The analysis of variance for the casting acceptance percentage concludes that inoculation is the most significant factor affecting the castings＇ quality with a contribution percentage of 44%; an increase in inoculation results in a significant improvement in acceptance percentage of ductile iron castings. The experiment results showed that with the optimized parameters, the rejection rate was reduced from 16.98% to 6.07%.
