This study addresses a critical chain project scheduling (CCPS) problem regarding the feeding buffer. The main contribution of this study lies in determining the critical chain when the feeding buffer is considered along with the project buffer, a less addressed issue in the critical chain literature. Using a mixed-integer nonlinear programming (MINLP) model, the critical chain of a project with no break-down and no overflow is found. Moreover, the impact of the feeding buffer on the criticality of activities is discussed. The problem is solved using the Lingo software package for validation in small-sized instances. Since the CCPS is known as an NP-hard problem, a genetic algorithm (GA) is also designed to solve large-scale instances. The algorithm's performance is confirmed using various project scheduling library test problems. Sensitivity analysis is implemented based on some crucial parameters, and the critical chain is analysed after conducting several experiments. It is shown how considering the feeding buffer makes different critical chains and how shortlisting activities and resources are optimally managed.

למכללה ללימודי NLP דרוש /ה פרילנסר /ית לעבודה לפי שעות מהבית + בונוסים.שכר שעתי.אם את /ה מחובר /ת לתחום ההתפתחות האישית, וורבלי /ת, מעולה בתקשורת עם מוטיבציה גבוהה ויש לך ניסיון במכירות או בייעוץ קורסים אז מקומך איתנו לתפקיד מלא סיפוק ומשמעות.מהן דרישות התפקיד?- חזרה טלפונית למתעניינים ומכירה של קורס NLP.- פולואפים וניהול רשימות הפונים.- עדכון משימות בטבלת אקסל.*התשלום עבור התפקיד הינו תמורת קבלה/חשבונית.

The NLP use of the best techniques from other sciences "as well as improvements" have lead to a great success. NLP managed to solve what Psychology failed to or what modern medicine can heal only with pills. The fact that NLP still progresses can only be good news for the future.

Check out this repository of more than 100 freely-accessible NLP notebooks, curated from around the internet, and ready to launch in Colab with a single click.

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Heightens performance of information workers in the digital workplace?

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.

How does AI extract meaning from text? It's not as simple—and definitely not as easy—as you might think.

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