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An analytical model for integrated logistic chains is developed and is used to analyze integrated logistic network with inventory control at all sites.

The integration of customer feedback is universally acknowledged as crucial in the product development process. Yet, traditional feedback collection methods employed by companies, such as interviews and surveys, have remained mainly unchanged and come with limitations. Interviews often fail to accurately capture customers' needs due to communication barriers, while surveys prompt only incremental changes instead of inspiring innovation. This challenge is compounded in the service industry, where feedback is intangible and more difficult to quantify. Text analysis presents a promising solution to delve into customer preferences more deeply, providing insights that can guide the development of new products and services. Our research advances the use of generative AI, specifically the GPT engine, beyond its conventional role as a chatbot. We innovate by adapting it to extract actionable insights from customer-service interactions, offering real-time, valuable data for decision-making and representing a significant leap forward in Voice of the Customer (VoC) analysis. [ABSTRACT FROM AUTHOR]

The interaction between inspection sequencing heuristics and part scheduling policies in a flexible manufacturing system were examined via simulations. The problem, which is actually an extension of the inspection location problem in a serial production system, was investigated with respect to the inspection cost rate, ratio of inspection time to machining operation time and probability of nonconformity. The results underscored the role of inspection plans on the selection of a part scheduling rule.

Malignancy is the tendency of a medical condition to progressively become fatal and generally refers to the presence of cancerous cells that can spread, invade, and destroy tissues. In the United States, a new cancer diagnosis is given every 30 s, and the rates of diagnosis are not indicating any slowdown. The number of cancer cases is projected to increase by 35% over the next decade. Increasing cancer cases have attracted researchers to find new approaches for their identification and characterization. Skin diseases are a significant healthcare burden, affecting millions of people globally. The early detection and diagnosis of skin diseases are essential to prevent adverse health consequences, including the development of malignant skin tumors. Dermoscopy-based diagnosis of skin diseases is a time-consuming process, particularly in resource-limited healthcare settings. Recent advancements in computer vision algorithms have led to the development of several smartphone-based applications for early skin disease prognosis. In this paper, we have utilized a set of 16 distinctive convolutional neural network models, based on deep learning, to process over 45,000 high-quality images from the HAM10000 dataset, encompassing seven categories of skin diseases. Our models were able to effectively identify and classify skin diseases using lesion images as input. Dermoscopy-based diagnosis of skin diseases is a time-consuming process, particularly in resource-limited healthcare settings. Our proposed application for early skin disease detection aims to prevent unintended health consequences, which can result in wastage of operating hours, resources, and, in some cases, compromised patient health. We have benchmarked our models against one another to evaluate their effectiveness and efficiency, using performance measures such as accuracy, precision, sensitivity, specificity, F-score, and G-Mean. Our results demonstrate that most deployed models achieve an accuracy of up to 99%. [ABSTRACT FROM AUTHOR]