Minimizing delay of ships in bulk terminals by simultaneous ship scheduling, stockyard planning and train scheduling S o u d a g a r A . K . I r f a n B a b u a , S a u r a b h P r a t a p a , G e e t L a h o t i a , K i r a n J . F e r n a n d e s b , M a n o j K . T i w a r i a , M a t t h e w M o u n t c a n d Y u X i o n g d A b s t r a c t Because of an increase in population, the demand for coal has drastically risen with millions of tons of coal being imported annually through Indian ports. To accommodate with this rise in demand, there has been an increase in the concern over proper ship scheduling and effective stockyard management. This article focuses on these aspects, as well as train scheduling, in the context of coal imports in port terminals. The article employs two heuristic-based greedy construct algorithms to improve port terminal throughput capacity by minimizing the delay of ships in port terminal. Applicability and validity of the model is tested on the database of a port located along the east coast of India.
A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However, final assemblies are localized to the respective markets. The decision making challenge is whether to manufacture the common component centrally or locally. To formulate the underlying setting, a newsvendor modeling based approach is considered. The developed model is solved using Frank-Wolfe linearization technique along with Benders' decomposition method. Further, the propensity of decision makers in each market to make suboptimal decisions leading to bounded rationality is considered. The results obtained for both the cases are compared.
Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable.
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