DETERMINING ORDER DELIVERY DATE BY REVENUE APPROACH: A CASE STUDY WITH NONWOVEN TEXTILE MANUFACTURERS IN TRC1 REGION

Non-woven textile materials are used as intermediate raw materials in various sectors such as cleaning, healthcare and automotive. These products are produced based on demand because they are requested in different compositions, colors, and weights. To ensure that the company achieves its objectives, it is necessary to use the capacity efficiently in the non-woven textile technology since it has high investment costs and high production capacity. In this study, a decision support system has been developed for non-woven textile firms so that they can obtain more order revenue. This software application was developed to sort the orders in 7 different ways based on the Moora and linear functions. The total order revenues to be obtained from each ranking and the delivery dates of sorted jobs are calculated and presented to the user to help him/her in the decision-making process. In addition, this software can also record the operator's planned maintenance data. In the present study, the decision support system was run with 27 different production scenarios. In the scenarios, the Moora method and linear function methods put forward more total order revenues than FCFS (First Come First Served) and EDD (Earliest Due Date) methods. As a product that can be used by decision-makers, the present decision support system provides a different point of view to the literature -which generally consists of theoretical studies-on delivery date and order ranking

Determining Order Delivery Date by Revenue Approach: A Case Study with Non-Woven Textile Manufacturers in TRC1 Region

Non-woven textile materials are used as intermediate raw materials in various sectors such as cleaning, healthcare and automotive. These products are produced based on demand because they are requested in different compositions, colors, and weights. To ensure that the company achieves its objectives, it is necessary to use the capacity efficiently in the non-woven textile technology since it has high investment costs and high production capacity. In this study, a decision support system has been developed for non-woven textile firms so that they can obtain more order revenue. This software application was developed to sort the orders in 7 different ways based on the Moora and linear functions. The total order revenues to be obtained from each ranking and the delivery dates of sorted jobs are calculated and presented to the user to help him/her in the decision-making process. In addition, this software can also record the operator's planned maintenance data. In the present study, the decision support system was run with 27 different production scenarios. In the scenarios, the Moora method and linear function methods put forward more total order revenues than FCFS (First Come First Served) and EDD (Earliest Due Date) methods. As a product that can be used by decision-makers, the present decision support system provides a different point of view to the literature -which generally consists of theoretical studies-on delivery date and order ranking

Examination of Support Requests of University Students in Distance Learning during the COVID-19 Pandemic

The COVID-19 (SARS-CoV-2) pandemic required changes to be made, especially in formal education processes. In order for the students to stay safe and healthy, many educational institutions switched to the distance learning method for the rest of the semester and continued their education without any intermission. Due to this switch, there has been a rise in the usage of learning management systems, and as a result, students started to encounter technical problems, especially during the exam periods, or they simply wanted to obtain more information about academic or administrative issues. This study used frequency and percentage analysis methods and examined the support request tickets opened up by students during distance education. Resolving the encountered problems quickly and effectively is very important in order to protect the motivation of the learners and ensure their successes. It was seen in the study that the majority of the problems reported by the students were related to internet connection or to the problems occurred during exams. This study aimed to share experiences with the researchers and technical and administrative staff working in this field and to contribute to the current distance learning literature

Analyzing the User's Sentiments of ChatGPT Using Twitter Data

ChatGPT, an advanced language model based on artificial intelligence developed by the OpenAI, was released to internet users on November 30, 2022, and has attracted a great deal of attention. The feelings and thoughts of those who first experienced ChatGPT are valuable feedback for evaluating the success and positive and negative aspects of this technology. In this study, sentiment analysis of ChatGPT-themed tweets on Twitter was conducted to comprehensively evaluate the feelings and thoughts of users during the first two months following the announcement of ChatGPT. Approximately 788.000 English tweets were analyzed using the AFINN, Bing, and NRC sentiment dictionaries. The findings indicate that a large portion of the initial users of ChatGPT found the experience to be successful and was satisfied with ChatGPT. However, negative emotions such as fear and concern were also observed in some users. This study presents the most comprehensive sentiment analysis on ChatGPT. In future studies, specialized research can be conducted on the performance of ChatGPT in a specific field

Comparison of risk of malignancy indices; RMI 1-4 in borderline ovarian tumor

Purpose: The aim of this study was to evaluate prognostic values of the risk of malignancy index (RMI)/1-4 in patients with borderline ovarian tumors (BOTs). Methods: The study consisted of 50 patients with BOT diagnosed and treated between 2005-2010 and 50 patients with benign adnexal massses between 2009-2010 as a control comparison group in the retropsective study. Preoperative serum CA125, U score, tumor size (S), and menopausal status were recorded. The RMI 1-3 was calculated according to the formula; UxMxCA125 and RMI 4 formulation was; UxMxCA125xS. S equaled 1 for tumor size = 7 cm. The RMI 1-4 indices were calculated for all patients together with the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy (DA). The performances of RMI indices were evaluated by McNemar's test and determined the best score cutoff value by the receiver operating characteristic (ROC) curve. Results: The mean age, median value of CA 125, ultrasound score, menopausal status, median values of RMI 1-4 of BOTs were statistically higher than benign adnexal masses. The sensitivity of RMI 1-4 was 26, 36, 62, and 60% at cutoff 200 level, respectively. The areas under curve of RMI 1-4 were found to be 0.676, 0.665, 0.668 and 0.734, respectively. DA of RMI 1-4 was found to be 56, 59, 50, and 71, respectively. When RMI 1-4 indices were compared with each other RMI 4 was the best RMI for BOTs. Conclusion: RMI 4 was the best predictive RMI for preoperative discrimination of BOT at a cutoff level of 200