423 research outputs found
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting
Demand functions for goods are generally cyclical in nature with
characteristics such as trend or stochasticity. Most existing demand
forecasting techniques in literature are designed to manage and forecast this
type of demand functions. However, if the demand function is lumpy in nature,
then the general demand forecasting techniques may fail given the unusual
characteristics of the function. Proper identification of the underlying demand
function and using the most appropriate forecasting technique becomes critical.
In this paper, we will attempt to explore the key characteristics of the
different types of demand function and relate them to known statistical
distributions. By fitting statistical distributions to actual past demand data,
we are then able to identify the correct demand functions, so that the the most
appropriate forecasting technique can be applied to obtain improved forecasting
results. We applied the methodology to a real case study to show the reduction
in forecasting errors obtained
A Greedy Double Swap Heuristic for Nurse Scheduling
One of the key challenges of nurse scheduling problem (NSP) is the number of
constraints placed on preparing the timetable, both from the regulatory
requirements as well as the patients' demand for the appropriate nursing care
specialists. In addition, the preferences of the nursing staffs related to
their work schedules add another dimension of complexity. Most solutions
proposed for solving nurse scheduling involve the use of mathematical
programming and generally considers only the hard constraints. However, the
psychological needs of the nurses are ignored and this resulted in subsequent
interventions by the nursing staffs to remedy any deficiency and often results
in last minute changes to the schedule. In this paper, we present a staff
preference optimization framework which is solved with a greedy double swap
heuristic. The heuristic yields good performance in speed at solving the
problem. The heuristic is simple and we will demonstrate its performance by
implementing it on open source spreadsheet software
Predicting book sales trend using deep learning framework
A deep learning framework like Generative Adversarial Network (GAN) has gained popularity in recent years for handling many different computer visions related problems. In this research, instead of focusing on generating the near-real images using GAN, the aim is to develop a comprehensive GAN framework for book sales ranks prediction, based on the historical sales rankings and different attributes collected from the Amazon site. Different analysis stages have been conducted in the research. In this research, a comprehensive data preprocessing is required before the modeling and evaluation. Extensive predevelopment on the data, related features selections for predicting the sales rankings, and several data transformation techniques are being applied before generating the models. Later then various models are being trained and evaluated on prediction results. In the GAN architecture, the generator network that used to generate the features is being built, and the discriminator network that used to differentiate between real and fake features is being trained before the predictions. Lastly, the regression GAN model prediction results are compared against the different neural network models like multilayer perceptron, deep belief network, convolution neural network
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