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Selection of a suitable location for a quarantine infrastructure represents a complex decision problem, which requires a systematic appraisal of myriads of factors. Quarantine facility in this study is a facility that intends to harbour and treat individuals diagnosed with COVID-19 to prevent the widespread of the virus. COVID-19 is a very contagious pandemic disease, hence, the establishment of critical factors that will embrace the selection of a suitable quarantine facility is of high importance. This paper aims to ascertain the vital few factors that must be considered by decision makers in selecting a suitable quarantine facility.

The aim of this study was achieved through the numerical assessment of identified quarantine location selection factors using the analytical hierarchy process (AHP) and Pareto techniques. The factors, which influences the selection of a suitable quarantine facility for COVID-19 patients were first identified from the literature followed by the pairwise comparison of the factors and random consistency analyses, as well as the ranking of the alternatives based on facility location experts’ opinions.

The study revealed that security, skills availability, cost, readiness, proximity to necessary medical facilities and distance to border, with percentage weight scores of 18%, 16.7%, 15.6%, 10.3%, 9.8% and 6.6% were the critical factors that must be considered during the selection of a quarantine facility for COVID-19 patients.

The results of this paper will help the government and decision makers in locating the quarantine sites for people who tested positive for the COVID-19 virus.

The present study focuses on the application of the decision technique to ascertain critical factors that embrace suitable quarantine facility selection. Combination of AHP and Pareto techniques for prioritization of conflicting factors to be considered in selecting the most suitable location for a quarantine facility has not been reported by existing literature.

The determination of the appropriate site for the location of a research institute represents a multi-criteria problem which requires a scientific approach for decision-making. The research centre in this study is an institute that intends to carry out the state-of-the-art research activities and provide the requisite skills to expedite and optimize the manufacturing of rail cars in South Africa. Hence, the selection of a suitable and conducive location capable of achieving these aforementioned objectives in an effective manner is a problem which requires scientific justification for the allocation of the weights and biases. In light of this, using various decision techniques, this paper aims to establish a suitable framework for the location selection of the research institute which is capable of meeting the short- and long-term objectives of the institute.

This aim was achieved by ascertaining the suitability of potential location alternatives using the factor rating (FR) and centre of gravity (CoG) technique.

The CoG revealed that any location within the longitude of 28.28 and latitude of −25.75 (with a Cartesian coordinate position of 5053.62; 2718.69) is suitable for the research institute, while the result of the FR/weighted score matrix revealed that location J₃ with a weighted score of 72.6% is the most suitable location for the research institute with the longitude of 5053.62 and latitude of 2718.69.

The results of this paper helped decision-makers in locating the given research institute which is currently operational.

The present study is focussed on the application of location decision techniques in the research institute scenario. The combination of FR and CoG techniques for the selection of the most suitable location for a research institute amidst conflicting criteria has not been widely reported by the existing literature.

Reconfigurable vibrating screen (RVS) is an innovative beneficiation machine designed at Tshwane University of Technology, Republic of South Africa (RSA); with adjustable screen structure to ensure sorting, sizing and screening of varying mineral particles (sizes and quantities) demanded by the customers in a cost-effective manner through the screen structure geometric transformation. In order to ensure that this machine is optimally maintained and managed when utilized in surface and underground mining industries, there is a need to establish or ascertain the best maintenance practices that would be used in optimally managing the RVS machine using decision making techniques. In view of this, the purpose of this paper is to ascertain the best maintenance practices that would be used to optimally maintain and manage the RVS machine when used in surface and underground mines.

Decision making techniques such as weighted decision matrix (WDM) and analytical hierarchy process (AHP) were used in this research work to establish the best maintenance practice for optimally maintaining and managing the RVS machine using relevant literature survey on maintenance management systems as well as the different maintenance criteria decision indices obtained from different conventional vibrating screen machine manufacturers and maintenance experts.

Based on the results obtained from the WDM analysis, it was anticipated that e-maintenance (e-M) system embedded with diagnosing and prognosing algorithms; with a cumulative weight score of 2.37 is the best maintenance practice for managing the RVS machine when used in surface mines, while AHP with deeper decision making analysis anticipated that the robotic-driven maintenance (RM) system with an important decision criteria; safety, and a cumulative hierarchy score of 28.6 percent, supported by e-M management system with a cumulative hierarchy score of 17.6 percent are the best maintenance mix that could be used in optimally maintaining and managing the RVS machine, when used in a craggy and hazardous underground mining environment.

To this effect, it could be anticipated that e-M management system (endowed with the ability to detect fault on the machine, diagnose and prognose the different subsystems of the RVS machine and ascertain the reconfiguration time and process of the RVS machine in recovering production loss during the maintenance of the machine as well as meeting customers demand, etc.) is the best maintenance practice for optimally maintaining the RVS machine when utilized in surface mines while both e-M management system and RM management system (endowed with the ability to carry out automated maintenance tasks achievement under little or no maintenance manager intervention) are also anticipated as the best customized maintenance practices mix that could be used in optimally maintaining the RVS machine, when used in dangerous and hazardous underground mining environment.

This maintenance management system evaluation and selection for optimal RVS machine functionality will serve as a useful information to different mining machines (and other related machines) maintenance managers, in selecting the best maintenance management system for ensuring optimal functionality, reliability and maintainability of machines used in their industries.