Search results

The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”.

The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm.

When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system).

The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution.

Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally.

The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Este trabajo propone una metodología basada en modelos de inventarios con demanda aleatoria y estructura de dependencia para un conjunto de materias primas, denominadas “componentes”, usadas por servicios de alimentación que producen raciones alimenticias denominadas “menús”.

Los margen de contribución de servicios de alimentación que producen menús son optimizados empleando modelos de inventarios con demandas aleatorias dependientes. La dependencia estadística entre demandas de componentes y/o menús es incorporada en el modelado mediante la distribución gaussiana (o normal) multivariada. La optimización de los márgenes de contribución se logra usando modelos de inventarios probabilísticos para cada componente y programación estocástica mediante el algoritmo de evolución diferencial.

El margen de contribución esperado (promedio) aumenta en un 18,32% usando modelos de inventario de revisión continua para abarrotes y modelos uniperiódicos para componentes perecederos (sistema optimizado), en relación al sistema no optimizado usado anteriormente por la compañía.

La metodología multivariada propuesta permite describir estructuras de dependencia de la demanda mediante modelos de inventario aplicables a componentes usados para producir menús en servicios de alimentación.

Una administración profesional de la gestión de la cadena de suministros permite registrar datos de la identificación del componente, su demanda y sus niveles de stock para ser usados posteriormente con la metodología propuesta, la que debe estar implementada computacionalmente.

El modelado multivariado puede ser mejorado (a) utilizando distribuciones probabilísticas univariadas (marginales) distintas a la gaussiana, mediante métodos de cópulas que recojan estructuras de dependencia estadística más complejas; (b) considerando demandas de componentes con valores iguales a cero, mediante distribuciones probabilísticas infladas en cero; (c) usando dependencia temporal, mediante estructuras de series de tiempo autorregresivas y de media móvil, y (d) modelando el lead-time en forma aleatoria.

Dementia in older persons with learning disabilities is a growing concern for all those involved in their care. There is at present no effective treatment, but the importance of current drug treatments is reviewed. Developments in drug treatments for dementia remain an active area of ongoing research.

This paper aims to discuss the impacts of sports events, considering these: from the perspectives of residents (in terms of self-esteem, increase of social problems, car traffic, cultural self-development and entertainment); and for the host territory (in terms of economic development, price level, destination image and environment). The paper analyzes the case study of a hallmark event of the European Rally Championship – the Azores Rally.

During the Azores Rally of 2014, 669 inhabitants on São Miguel Island answered a survey. The questionnaire assessed 12 items that are ex ante quality of life (QOL) indicators, ten items of event attributes and overall satisfaction with the event and nine items of event impact perceptions.

The satisfaction with the event is positively influenced by the host destination’s overall QOL, particularly by the entertainment and local gastronomy and is reinforced by the event-management mix. On other hand, the QOL attributes rather than the event performance indicators determine the perceptions of the promotion of image destination and economic development. The event spectacle level has a particular influence on the residents’ self-esteem.

The paper discusses the implications for public decision-makers, tour operators and event managers, focusing on the community as an important driver for placemaking.

This type of event has an impact on residents’ self-esteem, thus enhancing their QOL and hospitality.

This study supports the role of a peripheral island’s community as an active stakeholder, which interacts with the event organization’s key success factors.

Purpose: Risk is a multifaceted concept, and its identification requires complex approaches that are often misunderstood. The consequence is that decisions are based on limited perception rather than the full value and meaning of what risk is, as a result, the way it is being tackled is incorrect. The individuals are often limited in their perceptions and ideas and do not embrace the full multifaceted nature of risk. Regulators and individuals want to follow norms and checklists or overuse models, simulations, and templates, thereby reducing responsibility for decision-making. At the same time, the wider use of technology and rules reduces the critical thinking of individuals. We advance the automation process by building robots that follow protocols and forget about the part of risk assessment that cannot be programed. Therefore, with this study, the objective of this study was to discover how people define risk, the influencing factors of risk perception and how they behave toward this perception. The authors also determine how the perception differed with age, gender, marital status, education level and region. The novelty of the research is related to individual risk perception during COVID-19, as this is a new and unknown phenomenon. Methodology: The research is based on the analysis of the self-administered purposely designed questionnaires we distributed across different social media platforms between February and June 2020 in Europe and in some cases was carried out as a interview over communication platforms such as “Skype,” “Zoom” and “Microsoft Teams.” The questionnaire was divided into four parts: Section 1 was designed to collect demographic information from the participants; Section 2 included risk definition statements obtained from literature and a preliminary discussion with peers; Section 3 included risk behavior statements; and Section 4 included statements on risk perception experiences. A five-point Likert Scale was provided, and participants were required to answer along a scale of “1” for “Strongly Agree” to “5” for “Strongly Disagree.” Participants also had the option to elaborate further and provide additional comments in an open-ended box provided at the end of the section. 466 valid responses were received. Thematic analysis was carried out to analyze the interviews and the open-ended questions, while the questionnaire responses were analyzed using various quantitative methods on IBM SPSS (version 23). Findings: The results of the analysis indicate that individuals evaluate the risk before making a decision and view risk as both a loss and opportunity. The study identifies nine factors influencing risk perception. Nevertheless, it must be emphasized that we can continue to develop models and rules, but as long as the risk is not understood, we will never achieve anything.

This paper aims to present a unified motion control scheme for quadcopters which not only solves the point stabilization and trajectory tracking problems but also the path following problem.

The control problem is solved based on the kinematic model of the unmanned aerial vehicles (UAV). Next, a dynamic compensation controller is considered through of a quadcopter-inner-loop system to independently track four velocity commands: forward, lateral, up/downward and heading rate. Stability and robustness of the whole control system are proved through the Lyapunov’s method. To evaluate the controller’s performance, a multi-user application which allows bilateral communication between a ground station and the Phantom 3 PRO quadrotor is developed.

The performance of the proposed unified controller is shown through real experiments for the different motion control objectives: point stabilization, trajectory tracking and path following. The experiments confirm the capability of the unified controller to solve different motion problems by an adequate selection of the control references.

This work proposes the design of three types of motion controllers, which can be switched to comply a task in outdoor. Based on the software development kit provided by the company DJI, an application to get and send data to the UAV is developed. By means of this application, the three tasks are tested and the robustness of the controllers is proved.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This paper aims to explore the optimization process involved in the aircraft maintenance allocation and packing problem. The aircraft industry misses a part of the optimization potential while developing maintenance plans. This research provides the modeling foundation for the missing part considering the failure behavior of components, costs involved with all maintenance tasks and opportunity costs.

The study models the cost-effectiveness of support against the availability to come up with an optimization problem. The mathematical problem was solved with an exact algorithm. Experiments were performed with real field and synthetically generated data, to validate the correctness of the model and its potential to provide more accurate and better engineered maintenance plans.

The solution procedure provided excellent results by enhancing the overall arrangement of the tasks, resulting in higher availability rates and a substantial decrease in total maintenance costs. In terms of situational awareness, it provides the user with the flexibility to better manage resource constraints while still achieving optimal results.

This is an innovative research providing a state-of-the-art mathematical model and an algorithm for efficiently solving a task allocation and packing problem by incorporating components’ due flight time, failure probability, task relationships, smart allocation of common preparation tasks, operational profile and resource limitations.

An extensive research base supports the positive effects of self-monitoring interventions on a number of student outcomes, both academic and behavioral. While the vast majority of this research base relied on traditional paper-and-pencil forms of self-monitoring, advances in technology have created significant opportunities to develop technology-based self-monitoring (TBSM) systems that may offer a number of benefits in terms of efficiency and data management, storing, and graphing. Technology-based self-management applications have evolved and been studied extensively in health-related fields, but research and development for such applications is only beginning in the field of education. In this chapter we (1) provide a brief overview of the literature on traditional forms of self-monitoring, (2) examine how educators and educational researchers may apply lessons learned about TBSM from the medical field, (3) summarize emerging literature on TBSM for students with or at risk for emotional/behavioral disorders in particular, and (4) offer suggestions for future research and development in TBSM.

The purpose of this paper is to deal with the Bayesian and non-Bayesian estimation methods of multicomponent stress-strength reliability by assuming the Chen distribution.

The reliability of a multicomponent stress-strength system is obtained by the maximum likelihood (MLE) and Bayesian methods and the results are compared by using MCMC technique for both small and large samples.

The simulation study shows that Bayes estimates based on γ prior with absence of prior information performs little better than the MLE with regard to both biases and mean squared errors. The Bayes credible intervals for reliability are also shorter length with competitive coverage percentages than the condence intervals. Further, the coverage probability is quite close to the nominal value in all sets of parameters when both sample sizes n and m increases.

The lifetime distributions used in reliability analysis as exponential, γ, lognormal and Weibull only exhibit monotonically increasing, decreasing or constant hazard rates. However, in many applications in reliability and survival analysis, the most realistic hazard rate is bathtub-shaped found in the Chen distribution. Therefore, the authors have studied the multicomponent stress-strength reliability under the Chen distribution by comparing the MLE and Bayes estimators.