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This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

Despite the presence of electronic medical records systems, traditional paper-based methods are often used in many countries to document data and eliminate medical record deficiencies. These methods waste patient and hospital resources. The purpose of this study is to evaluate the traditional deficiency management system and determine the requirements of an electronic deficiency management system in settings that currently use paper records alongside electronic hospital information systems.

This mixed-method study was performed in three phases. First, the traditional process of medical records deficiency management was qualitatively evaluated. Second, the accuracy of identifying deficiencies by the traditional and redesigned checklists was compared. Third, the requirements for an electronic deficiency management system were discussed in focus group sessions.

Problems in the traditional system include inadequate guidelines, incomplete procedures for evaluating sheets and subsequent delays in activities. Problems also included the omission of some vital data elements and a lack of feedback about the documentation deficiencies of each documenter. There was a significant difference between the mean number of deficiencies identified by traditional and redesigned checklists (p < 0.0001). The authors proposed an electronic deficiency management system based on redesigned checklists with improved functionalities such as discriminating deficiencies based on the documenter’s role, providing systematic feedback and generating automatic reports.

Previous studies only examined the positive effect of audit and feedback methods to enhance the documentation of data elements in electronic and paper medical records. The authors propose an electronic deficiency management system for medical records to solve those problems. Health-care policymakers, hospital managers and health information systems developers can use the proposed system to manage deficiencies and improve medical records documentation.

The possibility of integrating building information in an augmented reality (AR) environment provides an effective solution to all phases of a building's lifecycle. This paper explores the integration of building information modelling (BIM) and AR to effectively visualise building information models in an AR environment and evaluates the currently available AR tools.

A BIM model of a selected office room was created and superimposed to the actual physical space using two different AR devices and four different AR applications. The superimposing techniques, accuracy and the level of information that can be visualised were then investigated by performing a walk-through analysis.

From the investigation, it can be concluded that model positioning can be inaccurate depending on the superimposing method used and the AR device. Moreover, using the currently available techniques, only static building information can be superimposed and visualised in AR, showing a need to integrate data from Internet of Things (IoT) sensors into the current BIM-AR processes to allow visualisation of accurate and high-quality operational building information.

A practical process and method for visualising and superimposing BIM models in an AR environment have been described. Recommendations to improve superimposing accuracy are provided. The assessment of type, quality and level of detail that can be visualised indicates the areas that need improvement to increase the effectiveness of building information's visualisation in AR.