Amount Of Workload Research Articles

Analisis Beban Kerja Teknisi Pesawat Pada Maintenance Phase 1 King Air B200 di Balai Besar Kalibrasi Fasilitas Penerbangan

In aircraft maintenance, it is necessary to calculate the workload, to find out how much manpower is needed for a job. Determining manpower requirements to ensure maintenance work is carried out in an efficient manner and at a high level of quality. The Aviation Facilities Calibration Center (BBKFP) still applies a conventional approach in carrying out maintenance, with crowds without a clear manpower estimate for the work carried out by technicians. The method for calculating workload on Maintenace Phase 1 King Air B200 series is the Full Time Equivalent (FTE) method. The purpose of this research is to find out the amount of workload in Maintenance Phase 1 of the KingAir B200 series, to find out the suitability of the optimal number of manpower in Maintenance Phase 1 of the King Air B200 Series, and to find out the number of technicians that corresponds to the workload from the FTE calculations in Maintenance Phase 1 of the King Air B200 Series. The results of this research showed that 63.63% of the sections were still in the Overload condition, 9.09% were in the Underload category and 27.27% were in the Fit / Normal Category.

Workload analysis with the full time equivalent method approach to determine workforce required

This research aims to to analyse the workload using the Full Time Equivalent (FTE) method to produce the optimal number of human resources. The post-pandemic economic recovery has led to increased productivity in various sectors, including PT Mada Wikri Tunggal which has experienced an increase in sales in the last three years. However, this increase in sales is inversely proportional to the number of human resources in the office sector of the company. Excessive or too minimal workload will cause inefficiency in the productivity of a company. The amount of workload that is too heavy indicates that the amount of workforce available is not balanced with the workload given, so it can cause physical and mental fatigue which results in a decrease in productivity. The data analysis method used in this research is the Full Time Equivalent (FTE) method. FTE can be assumed as a measure of the time required for the workforce to completely carry out work activities.

A convertible neural processor supporting adaptive quantization for real-time neural networks

This paper presents Stimpack, a responsive approach toward adaptive neural processing unit (NPU), aiming to satisfy service-level objectives (SLOs) under highly massive loads of neural network (NN) inference. Initially, Stimpack operates in a base-mode processing and computes NNs similarly to a conventional NPU accelerator. During base-mode processing, if SLO violation is likely to occur soon, it changes the operation mode to a burst-mode processing. In the burst-mode processing, Stimpack computes quantized networks instead of the original ones, enabling computational throughput to be scaled up to two times higher than the base-mode processing. This switchable processing is facilitated by three hardware/software schemes. First, a reconfigurable core is adopted to support two different precisions and boost processing throughput for avoiding SLO violations. As computation resources can be shared between the two operation modes, the area overhead of a reconfigurable core is negligible. The second is an on-chip quantization unit that mitigates the data transfer overhead incurred by mode changing. It quantizes parameters stored in on-chip memory on the fly, instead of bringing quantized parameters from off-chip memory. Third, Stimpack leverages a scheduler that determines mode switching based on the amount of workloads in the server. By monitoring ongoing and queued requests of NPU, the scheduler conservatively activates burst-mode processing to minimize accuracy loss. Our analysis shows that, compared to a state-of-the-art NPU, Stimpack achieves 48.4% speedup and allows a 41.4% large load on average while satisfying SLO and near-ideal accuracy.

Profile of prosthodontics program directors in North America.

To develop a current profile of prosthodontics program directors in United States dental schools and describe the current challenges and responsibilities of those serving in the position and assess the professional needs of the program directors to better support them in their roles. A survey was sent to 51 participants who were identified as prosthodontics program directors in North America via Qualtrics in 2019 to assess the professional needs of program directors to better support their roles. The survey consisted of 29 items organized into six sections: 1) demographic information; 2) hours and time spent in various areas of the job; 3) greatest challenges; 4) perceived importance of specific competencies; 5) opinions on the greatest rewards of the position, and 6) their needs for support from the American College of Prosthodontists and advice for the future program director. The response rate for this survey was 88.3%. Of the 45 respondents, the majority were white/non-Hispanic, followed by Asian/Pacific Islanders. The two largest and equal numbers of participants who worked for more than 10 years and between 1 to 3 years were observed with approximately 29% each, and program directors who worked less than a year occupied the least amount. 34% of program directors are on the tenure track and 80% of those are tenured. Teaching and clinical services took away the greatest time with approximately 42% and 22.1% respectively. Thirty-five (78%) program directors listed the greatest challenge they encountered was the workload of the role. Skills listed in leadership and personnel competencies were all rated more important than most of the skills listed in management. There is a wide gender gap and a lack of racial diversity among the prosthodontics program directors. The biggest challenge faced by them is the amount of workload followed by budget and fiscal constraints. And for their improved efficiency, they gave emphasis on learning time management and continuous clinical training of newer advances. This article is protected by copyright. All rights reserved.

IMPLEMENTATION OF HEALTH-SAVING TECHNOLOGIES IN SWIMMING LESSONS

The article defines the peculiarities of the implementation of health-saving technologies in swimming lessons in higher education institutions. Modern health-saving technologies used in the field of physical education of student youth are disclosed. The current state of health and physical fitness of student swimmers was analyzed, a set of problems related to the development of sports and mass swimming in Ukraine was identified, in particular, regarding the training of the country's population and its conscript contingents in the ability to swim. The role and place of swimming in the system of physical fitness of student youth and ensuring their adherence to the basics of a healthy lifestyle in the context of the implementation of health-saving technologies are determined.
 Swimming training is carried out in the form of practical classes, which take up one training pair (2 academic hours).
 Methodical planning and structuring of the training process, which includes the logical and systematic ordering of several training variables (intensity, volume, frequency, recovery period and exercises) in an integrative way aimed at optimizing specific performance results at predetermined points in time.
 The main characteristics of such approaches are systematic variations in the content of the intensity of classes and the amount of workload throughout the entire program.
 However, this should not be seen as a simple strategy of training variation, but as an appropriate sequence and integration of different training variables, including not only volume and intensity, but also frequency, recovery periods, density and selection of exercises.
 The duration of each session may increase. The total training contribution of strength work on land is a product of intensity, frequency and duration of training. The general goal is the uniform development of all muscle groups.
 Land exercises are used to train muscular strength and endurance, and they complement the improvements in muscular endurance that result from swimming training. The exercise program should be simple (that is, two exercises for each of the main muscle groups) so that it corresponds to the total training time for one session.

Трудовая мобильность молодежи: потенциал и факторы его активизации в современных российских условиях

The article substantiates the author’s interpretation of labor mobility potential and the factors of its activation in line with the subjective mobility concept. Its empirical verification is carried out on the basis of the analysis of the mass survey data and in-depth interviews conducted by the author among the young people employed in the economy of Kursk region, and their comparison with the results of all-Russian and regional studies. The research reveals a relatively high potential for labor mobility of working youth, expressed in the readiness to change jobs in the near future. About half of the young people employed in the economy can be attributed to the social base of potential labor mobility, within which there are two significant groups: some of them have a clearly defined attitude to change jobs in the near future, the second one allows such changes in their employment status. It determines the key role of two groups of factors in activating the potential of youth labor mobility. The first of them includes the intentions of young people to realize themselves and their professional potential in labor activity and career in a new job; the second one is represented by the main reasons for dissatisfaction with the working conditions at the current job – the level of wages and the amount of workload. The research establishes the prevalence in the youth environment of employment not according to the profile of the received profession (specialty), and young people’s lack of assessment of the discrepancy between work and profession as a causal factor for changing jobs and as an obstacle to their career.

Professional deficits of teachers in the field of ICT competencies, manifested in the conditions of digital transformation of education

Introduction. The relevance of the study is determined by the fact that the digital transformation of the educational space has led to the emergence of a new problem area associated with the willingness of teachers to use modern digital tools that are an integral part of the digital transformation of the educational space. The purpose of the article is to analyze and summarize the monitoring of the existing professional deficits of teachers in the field of ICT competencies, manifested in the conditions of digital transformation of education. Materials and methods. The survey was attended by 2,451 people: 707 math teachers, 1,110 Russian language teachers and 634 social studies teachers from 7 regions of the Russian Federation. The survey took into account the characteristics of respondents: the subject taught; the amount of workload; place of work; age; teaching experience. The results of the study. The study showed that there are the following professional deficits of teachers in the field of ICT competencies, manifested in the conditions of digital transformation of education: insufficient level of proficiency of teachers with digital technologies and tools, the level of equipment of schools with computer and communication equipment, the quality of Internet connection as an important factor in the use of modern digital technologies. A potentially problematic area is the ability of teachers to organize the effective use of digital technologies in the educational process and to prepare students for the use of digital tools in educational activities. The study showed that 92% of the teachers surveyed possess basic digital literacy skills, about 30% of respondents possess professional ICT competencies, 75% contribute to students' understanding of the risks and threats of the digital environment. Сonclusion. Based on the results of the survey of teachers, recommendations are given in the following areas: it is necessary to organize a process of continuous training of teachers in the field of digital technologies, which should include such activities as advanced training courses in the field of digital technologies; organize work on conducting methodological seminars (webinars) on the use of digital technologies to solve specific pedagogical tasks; to introduce the practice of conducting master classes with the best solutions in the field of using digital technologies in the educational process into the work of methodological associations of teachers; organize assistance to teachers in the development of digital technologies by digital volunteers; it is necessary to create conditions for self–training of teachers in the field of ICT competencies.

Subject matter and methodological competencies of mathematics teachers revealed in the digital transformation of education

Introduction. Creating conditions and opportunities for self-realization and reveal of each person’s talent in the conditions of digital transformation actualized the problems of improving the quality and competitiveness of Russian education in the modern world. The priority of developing the competencies of a teacher of general education organization at different stages of professional career was also determined. The purpose of this article is to monitor and analyze the formation of subject matter and methodological competencies of teachers of mathematics, revealed in the process of teaching activities in the conditions of digital transformation of continuing teacher education. Materials and methods. The survey involved 919 mathematics teachers from 7 regions of the Russian Federation. The survey considered such characteristics of the respondents as place of work, amount of workload, gender, age, teaching experience. Results of the study. The research showed the following professional deficits of the surveyed mathematics teachers in the area of subject matter and methodical competencies, manifested in the conditions of digital transformation of education: from 20% to 55% of teachers have difficulties in organizing subject teaching in different conditions of transferring typical algorithms to a new situation, about 25% have problems when analyzing educational and life situations that require application of mathematical apparatus, 34% do not know the ways of criterial evaluation of learning outcomes About 20% of the teachers with up to 10 years of experience show insufficient ability to select subject and meta-disciplinary content of the learning material required to solve mathematical problems, as well as the ability to build logical reasoning in mathematical contexts together with students, which is a potentially problematic area. Conclusion. Several recommendations were given based on the results of the conducted survey of teachers. Monitoring of the formation of professional subject matter and methodological competencies of teachers of mathematics should be systematic in nature and have clear range of tools. It is advisable to conduct a separate monitoring of the formation of professional competencies of students of the 3rd-5th courses, studying on the basic educational programs of training future teachers at the bachelor level. It is necessary to conduct a more in-depth analysis of problematic topics in terms of the identified professional deficits and to develop and implement individual and group programs of methodological support for teachers based on this analysis, including professional development and professional retraining, in order to eliminate the identified deficits with the objectives of digital transformation of continuing teacher education. It is necessary to actively implement comprehensive programs to develop the system of teaching mentoring at the regional and general education levels.

Reliability Aware Medical Resource Allocation for Health Care Industrial Internet of Things (IIoT) Using Tabu Search and Alo Algorithm

The medical data integrating system allows the hospital’s resource constraints to be more effectively utilized. Moreover, by improving the resource management and allocation method, the hospital’s operations may be more organized, and the effectiveness of healthcare can be improved without breaking the medical agreements. Significant catastrophes frequently result in a scarcity of important medical resources, hence resource allocation must be optimized to enhance the performance of relief operations. The two main requirements for healthcare industrial applications are timeliness and reliability. Therefore, in the architecture of a smart healthcare industry these two criteria should be thought carefully. A well-known approach for the security and timeliness in the intelligent healthcare industry is to utilize hybrid IoT and Cloud technologies. Yet it is not enough to protect their hard deadlines for tight time-sensitive applications utilizing cloud. A potential way to cope with efficiency and latency criteria for strict time-sensitive applications is the deployment of intermediate processing layer IoT that can be linked between healthcare industrial plant and cloud. The purpose of this article is to develop a healthcare Industrial IoT system that include a medical resource allocation scheme for dividing a certain amount of workload between those multiple computing layers which are dependable and time consuming. IOT is integration of microprocessors and controller Workload partitioning can give us important design decisions to specify how many computing resources are needed in cooperation with IoT to develop a local private cloud. Ant lion optimization (ALO) and TABU Look for the right route. The simplest method of deciding the distance to a destination is to choose an OLSR routing protocol depending on the meaning or measure it requires. The method proposed in the distribution and data storage of medical resources is very efficient.

Students' Interest in Online Learning in Higher Education During the Covid-19 Pandemic

The Covid-19 pandemic in various countries has had an impact on changes in the education system. The implementation of learning has changed from classroom to online learning. The implementation of online learning certainly affects the learning interest of students. This study was conducted to determine student interest in learning during the Covid-19 pandemic. The subjects in this study were students of the Mathematics Education Study Program at a private university in Yogyakarta as many as 72 students who were selected using the simple random sampling method. The data in this study were collected through Google Form questionnaire. The questionnaire contains 15 items of multiple choice and each item contains “why” question to obtain in-depth data about the answers given by respondents. The data of questionnaire analyzed descriptive qualitatively. The results indicated that students' interest in online learning needed to be insreased. It can be seen from students’ responses to the questionnaire that only 15.3% of students gave a positive response to online learning and 32% to online exams. In general, students prefer classroom learning compared to online learning in terms of student attendance, interaction of lecturer-students, and technical in learning. Online learning tends to reduce student interest in attending lectures. Limitations of internet access, lecturer delivery methods and the amount of workloads are factors causing the decline in student interest. By knowing students' interest in learning and the factors that cause low students’ interest in learning, lecturers can improve the quality of learning to increase student interest in online learning.

AUTOMATED WEED REMOVAL SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

Weeds are very annoying for farmers and also not very good for the crops. Its existence might damage the growth of the crops. Therefore, weed control is very important for farmers. Farmers need to ensure their agricultural fields are free from weeds for at least once a week, whether they need to spray weed herbicides to their plantation or remove it using tools or manually. The aim of this research is to build an automated weed control system. The system consists of motors, Raspberry pi and a camera which we use to capture the image of the crops and weeds. An automated image classification system has been designed to differentiate between weeds and crops. For the image classification method, we employ the convolutional neural network algorithm to process the image of the object. Deep learning is used to analyze the relevant features from the agricultural images. The dataset is trained for the classification of weed and crop. Therefore, by the use of technology, farmers can reduce the amount of workload and workforce they need to monitor their plantation. In addition, this technology also can improve the quality of the crops.

WOMEN IN THE WORK PLACE – AN EMPIRICAL ANALYSIS OF THE CHALLENGE OF WORK LOAD

Female employment has witnessed a drastic paradigm shift. Several factors like improving literacy, change is the socio-economic scenario, zest for financial independence and ambition; are responsible for this changing trend. Being the largest employer of women in India, education sector is witnessing drastic changes. The present study examines Work-Life Balance of women working in various universities of J&K with emphasis on work pressure the females face in the course of trying to balance their professional life with their personal life.
 Investigation shows that most working women are upset with the amount of work-load in the universities. Women who work for longer hours tend to be more dissatisfied with their Work-Life Balance. The study strongly suggests that academic expectations must be attuned with the demands of a woman’s personal life or else a woman can never give her best.

Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning

Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.

Working conditions and employee’s productivity: Evidence from a health centre in Ghana

The purpose of the study was to identify the types of working conditions at the Beposo Health Centre, the nature of their working conditions and the effects of hygiene factors on employees’ productivity. Data were drawn from 33 employees of the Health Centre using a questionnaire and analyzed using Predictive Analytical Software to determine the frequency and mean. The study found that employees at the health centre were provided with job security, work-life balance, occupational health and safety and workload. It also ascertained that the physical work environment was not comfortable for the employees although they were provided a considerable amount of workload and high job security. Also, it was revealed that noisy environment, stress and absence of work-life balance were some hygiene factors that affected employees’ productivity. This study has implications for the development of procedures and measures to improve employees working conditions and productivity in the developing country context.

A Semantic Model for Interchangeable Microservices in Cloud Continuum Computing

The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dynamically change their geographic location. In order to exploit the true potential of cloud continuum computing applications, it is essential to use a comprehensive set of various intricate technologies together. This complex blend of technologies currently raises data interoperability problems in such modern computing frameworks. Therefore, a semantic model is required to unambiguously specify notions of various concepts employed in cloud applications. The goal of the present paper is therefore twofold: (i) offering a new model, which allows an easier understanding of microservices within adaptive fog computing frameworks, and (ii) presenting the latest open standards and tools which are now widely used to implement each class defined in our proposed model.

Evaluation of Deep Learning-Based Auto-Segmentation of Target Volume and Organs-at-Risk in Breast Cancer Patients

In breast cancer patients receiving radiotherapy (RT), lower radiation doses to the nearby normal organs can reduce treatment-related toxicities. Also, the importance of meticulous target delineation is increasing due to the advancement of RT modalities such as intensity-modulated RT. Subsequently, accurate target volume and organs-at-risk (OAR) segmentation has become imperative, adding considerable amount of workload in the clinical field. Thus, deep learning-based auto-segmentation can be an expedient tool for target and OAR segmentation. Here, we evaluated the deep learning-based auto-segmented contours compared to manually delineated contours in breast cancer patients. Clinical target volumes (CTV) for bilateral breasts and regional lymph node and OARs including heart, lung, esophagus, spinal cord and thyroid were manually delineated on a planning computed tomography scans of 61 breast cancer patients who received breast-conserving surgery. In addition, cardiac substructures including right coronary artery (RCA), left anterior descending artery (LAD), atriums and ventricles were manually delineated. Afterwards, a two-stage convolutional neural network (CNN) algorithm was conducted to effectively segment the organs. Quantitative metrics, including dice similarity coefficient (DSC) and Hausdorff distance (HD), and qualitative scoring by expert and non-expert panel were used for analysis. Inter-observer variability was also assessed for contours by three radiation oncologists on a randomly selected case. The correlation between the auto-segmented and manual contours was good for OARs including heart, lung, esophagus, spinal cord, thyroid and bilateral atriums and ventricles with mean DSC higher than 0.80. In addition, CTVs also showed favorable results with mean DSC higher than 0.70 for all breast and regional lymph node CTVs. However, auto-segmented contours of RCA and LAD showed reduced performance with mean DSC lower than 0.5 and mean HD higher than 20 mm. Qualitative subjective scoring by physicians showed good results for all CTV and OARs. As for inter-observer variability, only heart showed DSC of 0.91, whereas the other OARs showed DSCs lower than 0.80. For CTV, although breast CTV showed an acceptable mean DSC of 0.85, other CTVs for regional lymph nodes showed poor results with mean DSC ranging from 0.45 to 0.75. The feasibility of deep learning-based auto-segmentation was shown in this study. Although deep learning-based auto-segmentation cannot be a substitution for radiation oncologists, it can be an expedient tool in clinic, by assisting radiation oncologists and consequently enhancing the quality control of radiotherapy.

Incorporating shifting bottleneck identification in assembly line balancing problem using an artificial immune system approach

The manufacturing industry has evolved in the past few years due to the competitive global economy where the performance of its assembly line operations is primarily dependent upon optimum resource utilization. The assembly line operations are balanced among the available resources to obtain an equal amount of workload to achieve optimum resource utilization, called the assembly line balancing (ALB) problem. Various approaches have been proposed to solve the ALB problem, which is broadly categorized as exact, heuristic, and meta-heuristic approaches. Although solving the ALB problem is crucial, a bottleneck may still occur over the next operation stages. By using problem-specific information (bottleneck identification), it is expected to improve the solution quality of the ALB problem. As such, the contribution of this study is the computational method, namely as the swarm of immune cells with bottleneck identification (SIC+) approach, where both the ALB and bottleneck identification problems are addressed. In addition to the flexible problem representation, the SIC+ approach is equipped with a discrete bottleneck simulator to simulate the bottleneck scenario and bottleneck-specific operators to redistribute the machine workload of the identified bottleneck machine. The approach was tested on 24 benchmark data sets of the ALB problem, and the impact of incorporating bottleneck identification was illustrated. The experimental results show that the proposed SIC+ approach has achieved a total of 66.12% optimal solution over all instances of the benchmark data sets and has been compared with approaches from the literature where high-quality solutions were statistically justified.

Motivation for Parenthood among LGBTIQ People in Croatia: Reasons for (not) Becoming a Parent

Parenting desires, intentions, and the underlying motivation for parenthood are well documented in the context of heterosexual couple parenthood, while among lesbian, gay, bisexual, transgender, intersex and queer (LGBTIQ) people research is limited. The main goal of this study was to explore parenting desire and different reasons to become a parent or remain childfree among LGBTIQ people in Croatia. 486 childless LGBTIQ people participated in an on-line survey. In the quantitative part of the study, parenting desire and reasons for and against parenthood were measured, while the qualitative part analysed the answers to open-ended questions about additional reasons that influence the desire to want or not to want children. The results showed that 46% of the participants want to become parents, 35% did not know, and 19% reported they do not want to have children. The main reasons for parenthood among the participants who want children were internal – the desire to give love, share knowledge, and develop a special bond with a child. The participants who do not want to have children also stressed internal reasons against parenthood, such as restricted personal freedom, high responsibility, and the amount of workload they perceive as a part of parenthood. Several additional reasons for and against parenthood emerged from the qualitative data. Some reasons reflected universal issues unrelated to sexual orientation or gender identity, while others conveyed concerns related to social and legal barriers that LGBTIQ people face when it comes to parenthood.

Determinants of Academic Staff Retention in Zimbabwean Universities: A Case Study of Selected Universities in Matabeleland Region

The issue of academic staff retention has attracted major interest in many countries and Zimbabwe is not an exception. The purpose of this study was to establish the determinants of academic staff retention in Zimbabwean universities with particular reference to two universities in Matabeleland region. Specifically, the objectives of the study were to establish whether demographic factors (age, sex and marital status), remuneration, career advancement opportunities, training and development, educational qualifications and amount of workload have an influence on academic staff retention. The study was quantitative and it employed the survey design. The total population of the academic staff in the two universities covered by the study was 491. Stratified random sampling was used to select a sample of 119 from the two universities. Data was analysed using Eviews version 10 and SPSS Version 21 software. The study employed the logit model to estimate the results. The study revealed that marital status, educational level, training and development, workload, remuneration and career advancement opportunities significantly affect academic staff retention in Zimbabwean universities. Age and sex do not have a significant effect on academic staff retention. The study recommended the need for Zimbabwean universities to provide staff exchange programmes so that members of staff can share latest practices with staff members from other universities. It also recommended academic institutions to negotiate with banks to offer housing and vehicle loans to academic staff at reasonable interest rates. Another recommendation was that universities should conduct exit interviews with quitting members of staff so as to identify the major reasons for quitting. In addition, universities should provide continuous professional development programmes to their staff, so that they can obtain relevant training and appropriate skills.

A branch-and-bound approach to schedule a no-wait flow shop to minimize the CVaR of the residual work content

The aerospace industry ranks among the largest manufacturing industries in the world facing a significant growing phase as well as an increased competition. This paper addresses the scheduling of a set of jobs in a paced assembly line in presence of uncertainty affecting the availability of production resources, stemming from the assembly process in the aircraft manufacturing industry. The production problem is modeled as a no-wait paced permutation flow shop and solved providing a robust scheduling solution minimizing the conditional value-at-risk (CVaR) of the residual work content, i.e., the amount of workload that cannot be completed during the cycle time in the stations, due to a lack of available resources. A branch-and-bound approach is developed and applied to randomly generated instances as well as to an industrial problem related to the production of aircrafts.