Multivariate Ewma Models and Monitoring Health Surveillance during a Pandemic

We examine a common problem is biological analytics and surveillance in health care. These methods can improve greatly the process of monitoring health data to assess changes in the likelihood of Pandemics and disease incidence in a world where medical knowledge is still largely in an embryonic period. Based on an illustration, we suggest that multivariate exponential moving-average (MEWMA) control charts are suitable in many cases where detection and inspection of several or more variables over a lengthy period of testing provide for the best analysis of data leading to pre-­diagnostic and diagnostic therapy. Though these methods came from the control of quality and continuous improvement in lean manufacturing and service operations, these methods are useful if not a vital application in the analysis of health care and therapeutic data. The indications from this study corroborate earlier findings by others that MEWMA methods fit the diagnostic activity under study. Unfortunately Pandemic Analysis is using oversimplified techniques in analyzing data secure by diagnostic tests which can easily be improved especially in the use modern day analytics based on quality control methods used in other disciplines

Economic Design of X-bar Control Chart by Ant Colony Optimization

Control charts are widely employed to monitor and maintain the statistical control of a process. Designing a control chart involves selecting a sample size, sample frequency and control limits for the control chart. The costs of sampling and testing, cost of detecting the out-of-control signal and possibly correcting it, and the cost of non-conforming items reaching the consumer give the control chart an economic aspect. In 1956, Duncan developed a Loss Cost Function for X-bar control chart with single assignable cause. The function has to be optimized using a non-conventional optimization technique. In this project, Ant Colony Optimization (ACO) has been employed to optimize Duncan’s Loss Cost Function. Ants while searching for food deposit a chemical pheromone on their way back. The amount of pheromone deposited will be dependent on the quality and quantity of food. As the time progresses the ants become selective in choosing the path depending upon the pheromone deposited. Eventually, this leads the ants to choose the best possible path. An algorithm based on the traditional Ant Colony Optimisation technique developed by Niaki and Ershadi has been applied to the economic model of Duncan. The results were found to be on par with the results obtained by employing other non-conventional optimization techniques such as Genetic Algorithm

Economic Design of Control Charts Using Metaheuristic Approaches

Statistical Process Control (SPC) is a collection of problem solving tools useful in achieving process stability and improving capability through the reduction of variability using statistical methods. It can help industries in reduction of cost, improvement of quality and pursuit of continuous improvement. Among all the SPC tools, the control chart is most widely used in practice. Out of all the control charts, chart is the simplest to use and hence most popularly used for monitoring and controlling processes in an industry.A process may go out-of-control due to shift in process mean and/or process variance. To detect both types of shifts, R chart is often used along with chart. The design of chart refers to selection of three design variables such as sample size (n), sampling interval (h) and width of control limits (k). On the other hand, the joint design of and R charts involves four design variables i.e., sample size (n), sampling interval (h), and widths of control limits for both charts (i.e., k1 and k2). There are four types of control chart designs, namely (i) heuristic design, (ii) statistical design, (iii) economic design, and (iv) economic statistical design. In heuristic design, the values of design variables are selected using some thumb rules. In statistical design, the design variables are selected in such a way that the two statistical errors, namely Type-I error ( ), and Type-II error ( ) are kept at minimum values. In economic design, a cost function is constructed involving various costs like the cost of sampling and testing, the cost of false alarm, the cost to detect and eliminate the assignable cause(s), and the cost of producing non-conforming products when the process is operating out-of-control. The design parameters of the control chart are then selected so that this cost function is minimized. The design based on combined features of statistical design and economic design is termed as economic statistical design where the cost function is minimized while satisfying the statistical constraints. The effectiveness of economic design or economic statistical design depends on the accuracy of minimization of cost function. So, use of effectively designed control charts is highly essential for ensuring quality control at minimum cost. Most of the researchers have used either approximate or traditional optimization techniques for minimizing the cost function. With time, more and more efficient optimization methods have been utilized for this purpose. There are a number of metaheuristic algorithms reported in literature for optimization in various types of design problems. Out of them one each from two different groups are selected for the present work i.e., simulated annealing (SA) and teaching-learning based optimization (TLBO). SA is a point to point based metaheuristic technique, whereas TLBO is population based technique. SA is one of the oldest metaheuristic algorithms and proved to be the most robust one, whereas TLBO is one of the most recent and promising techniques. The present work requires optimization techniques that can solve non-linear, non-differentiable, multi-variable, unconstrained as well as constrained type of objective function. Both the above techniques are capable of optimizing this type of objective function. However, from literature review it is observed that neither of these two metaheuristic approaches has been applied in economic or economic statistical design of any type of control chart. In this work, both these metaheuristic techniques (i.e., SA and TLBO) have been applied for minimization of cost function for economic as well as economic statistical design point of view for individual chart, and by taking and R charts jointly in case of continuous as well as discontinuous process. Thus, a total of the following eight distinct design cases have been considered for their optimization. 1. Economic design of chart for continuous process 2. Economic design of chart for discontinuous process 3. Economic statistical design of chart for continuous process 4. Economic statistical design of chart for discontinuous process 5. Joint economic design of and R charts for continuous process 6. Joint economic design of and R charts for discontinuous process 7. Joint economic statistical design of and R charts for continuous process 8. Joint economic statistical design of and R charts for discontinuous process All the above designs are illustrated through numerical examples taken from literature using two metaheuristics i.e., SA and TLBO separately. These two independent techniques are used to validate their results with each other. Their results are found to be superior to that reported earlier in the literature. Thus, eight types of methodologies based on SA or TLBO approach are recommended in this thesis for designing control charts from economic point of view. Sensitivity analysis has been carried out using fractional factorial design of experiments and analysis of variance for each of the eight design cases, to examine the effects of all the cost and process parameters on all the output responses such as sample size, sampling interval, width of control limits and expected loss costper unit time. The process parameters which significantly affect the output responses are identified in each of the eight design cases. These results are expected to be helpful for quality control personnel in identifying the significant factors and thereby taking utmost care in choosing their values while designing the control charts on economic basis

Dynamic warehouse optimization using predictive analytics.

The forward area is a small area of a warehouse with a low picking cost. Two approaches that are investigated for selecting the SKUs of this area and the allocated space are the static and the dynamic approaches. In the case that decisions about the forward area are made periodically (e.g. yearly) and the products\u27 demand patterns are completely ignored, the FRP is static. We developed two heuristics that solve the large discrete assignment, allocation, and sizing problem simultaneously. Replenishing the same product in the same place of the forward area brings about a ``Locked layout of the fast picking area during the planning horizon. By using a dynamic slotting approach, the product pick locations within the warehouse are allowed to change and pick operations can accommodate the variability in the product demand pattern. A dynamic approach can introduce the latest fast movers to the forward area, as an opportunity arises. The primary objective of this dissertation is to formally define the dynamic FRP. One main mission of this research is to define a generic dynamic slotting problem while also demonstrating the strengths of this approach over the static model. Dynamic slotting continuously adjusts the current state of the forward area with real-time decisions in conjunction with demand predictive analytics. Applying different order data with different demand volatility, we show that the dynamic model always outperforms the static model. The benefits attained from the dynamic model over the static model are greater for more volatile warehouses

Análisis bibliométrico de la producción científica sobre Economía Experimental

La Economía Experimental (EE) es un método de trabajo de la Economía del comportamiento que desarrolla modelos teóricos de comportamiento humano en ámbitos económicos. Los experimentos económicos tienen ya una larga tradición, y han proporcionado resultados espectaculares y conclusiones ampliamente admitidas sobre la dinámica de mercados y el efecto de las instituciones económicas. Las nuevas tecnologías facilitan la realización y el análisis de estos experimentos. El objetivo principal de este estudio es la revisión sistemática de la producción científica sobre Economía Experimental, desde el año 1990 hasta finales de 2021, en las bases de datos de Web of Science Core Collection y Scopus. El análisis descriptivo de datos se realizó con el software Rstudio, mientras que el análisis de redes se hizo con el software Vosviewer. El estudio muestra, entre otras cosas, que la producción bibliográfica en este campo se ha intensificado exponencialmente; así como, que el país con más investigaciones es Estados Unidos y el autor más citado es Urs Fischbacher.Experimental Economics (EE) is a working method of behavioral economics that develops theoretical models of human behavior in economic settings. Economic experiments have a long tradition, and have provided spectacular results and widely accepted conclusions about market dynamics and the effect of economic institutions. New technologies facilitate the conduct and analysis of these experiments. The main objective of this study is the systematic review of the scientific production on Experimental Economics, from 1990 to the end of 2021, in the Web of Science Core Collection and Scopus databases. Descriptive data analysis was performed with Rstudio software, while network analysis was performed with Vosviewer software. The study shows, among other things, that the bibliographic production in this field has intensified exponentially; as well as, that the country with the most research is 2 the United States and the most cited author is Urs Fischbacher.Universidad de Sevilla. Doble Grado en Matemáticas y Estadístic