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As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.

The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.

After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.

Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.

The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.

Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

The purpose of this paper is to investigate the application of the postponement strategy by wineries in the state of Minas Gerais (Southeastern Brazil), in order to identify the types of postponement adopted by these companies, the implementation process and the results obtained after their adoption.

Twelve exploratory case studies were conducted in wine-producing companies, as well as on-site visits and semi-structured interviews with the managers of the companies surveyed.

The adoption of form postponement was verified in the companies studied mainly for table wine production, occurring most commonly during the bottling and labeling stages.

This paper analyzed the application of the postponement strategy in Southeast Brazil. Future research should analyze the application of this strategy in other regions of the country and abroad.

The information acquired in this research can contribute to a more adequate practical application of the postponement strategy in a little-known industry sector.

In addition to discussing and verifying the application of the postponement strategy in the wine industry, this research presents information to assist in its implementation, use and consolidation.

An interdisciplinary approach for computing the point‐to‐point message delay, a measure of congestion in a computer communication network, is derived by the inclusion of a queueing theory result in a probabilistic automaton model. This network model is structurally composed of the interconnection of two distinct repetitive probabilistic automatons. The formulation of the solution is based on the network probability transition matrix which represents the state transitions for the number of customers in the GI/G/1 queue at each computer node and the intranode and internode port routing.

Deployment of optimal size of resources is a key issue in repetitive construction projects. This paper describes a simulation model based on queuing theory for the resource scheduling of a real repetitive housing project involving 320 dwelling units constructed in East Delhi, India. The optimal size of resources, defined as the minimum size required to keep the project duration a minimum, has been identified from the results of a series of sensitivity analyses in which the size of the resources was varied one at a time. The duration of the project, the period of utilization of the resources, and the queue length of activities waiting for service are also reported in this paper. It has been shown that reduction in size of resources is achievable without increasing the duration of the project and queue length of activities. Increase in the size of some specialised crews is also proved advantageous.

The purpose of this paper is to focus on the contributions of queueing theory to hospital capacity management to improve organizational performance and deal with increased demand in the healthcare sector.

Models were applied to six months of inpatient records from a university hospital to determine operation measures such as utilization rate, waiting probability, estimated bed capacity, capacity simulations and demand behavior assessment.

Irrespective of the findings of the queueing model, the results showed that there is room for improvement in capacity management. Balancing admissions and the type of patient over the week represent a possible solution to optimize bed and nurse utilization. Patient mixing results in a highly sensitive delay rate due to length of stay (LOS) variability, with variations in both the utilization rate and the number of beds.

The outcomes suggest that operational managers should improve patient admission management, as well as reducing variability in LOS and in admissions during the week.

The queueing theory revealed a quantitative portrait of the day-by-day reality in a fast and flexible manner which is very convenient to the task of management.

Introduction Operations research, i.e. the application of scientific methodology to operational problems in the search for improved understanding and control, can be said to have started with the application of mathematical tools to military problems of supply bombing and strategy, during the Second World War. Post‐war these tools were applied to business problems, particularly production scheduling, inventory control and physical distribution because of the acute shortages of goods and the numerical aspects of these problems.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.