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The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.

A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.

From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.

The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.

To the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

The purpose of this article is to present a model to estimate and evaluate the operational costs of alternative layouts for large capacity warehouses or distribution centers with a large variety of goods.

The proposed model is based on time and resources studies per each of the basic activities on a warehouse operation. For validation purposes, the proposed model was applied on a perishable goods warehouse in Mexico. The output data was compared to actual data. Performance measures were operational costs and average picking time.

It was found that the proposed model is robust, flexible, simple and easy to be implemented. The model was used to evaluate two new alternatives of layout and operations of the same warehouse. It was found that the option with the layout with docks on long opposite sides of the warehouse and the operation without a separate picking zone minimizes operational costs.

The richness of the model is strongly supported by the information the warehouse has about its operation. With knowledge of the process, it is required to distinguish deterministic from stochastic basic activities and develop distance computations that depend on the layout being studied.

The approach used to model warehouse operations was to estimate the movements and resource consumption per commodity. This allows the model to be used in every operational context when the complexity of the system is strongly dependent on and proportional to the volume of operations. In addition, it is particularly adequate as a tool to compare average performance measures of different scenarios for the same system.

The model proposed here provides a simple way to estimate particularly operational resource consumptions and picking times as proxy measures for efficiency and efficacy of a warehouse. It uses distance computations, time information and unit occurrence frequencies of basic activities over a single commodity in the system.