Advances in Operations Research

Network Data Envelopment Analysis (NDEA) models assess the processes of the underlying system at a certain moment and disregard the dynamic effects in the production process. Hence, distorted efficiency evaluation is gained that might give misleading information to decision-making units (DMUs). Malmquist–Luenberger Productivity Index (MPI) assesses efficiency changes over time, which are measured as the product of recovery and frontier-shift terms, both coming from the DEA framework. In this study, a form of MPI involving network structure for evaluating DMUs in the presence of uncertainty and undesirable outputs in two periods of time is presented. To cope with uncertainty, we use the stochastic p-robust approach and the weak disposability of Kuosmanen (American Journal Agricultural Economics 87 (4):1077–1082, 2005) proposed to take care of undesirable outputs. The proposed fractional models for stages and overall system are linearized by applying the Charnes and Cooper transformation. Finally, the proposed models are applied to evaluate the efficiency of 11 petroleum wells to identify the main factors determining their productivity, utilizing the data from the 2020 to 2021 period. The results show that the management of resource consumption, especially equipment and capital, is not appropriate and investment is inadequate. Although the depreciation rate of capital facilities in this industry is high, the purpose of the investment is not to upgrade the level of technology.

In this review article, we consider discrete-time birth-death processes and their applications to discrete-time queues. To make the analysis simpler to follow, we focus on transform-free methods and consider instances of non-birth-death Markovian discrete-time systems. We present a number of results within one discrete-time framework that parallels the treatment of continuous time models. This approach has two advantages; first, it unifies the treatment of several discrete-time models in one framework, and second, it parallels to the extent possible the treatment of continuous time models. This allows us to draw parallels and contrasts between the discrete and continuous time queues. Specifically, we focus on birth-death applications to the single server discrete-time model with Bernoulli arrivals and geometric service times and provide the reader with a simple rigorous detailed analysis that covers all five scheduling rules considered in the literature, with attention to stationary distributions at slot edges, slot centers, and prearrival epochs. We also cover the waiting time distributions. Moreover, we cover three Markovian models that fit the global balance equations. Our approach provides interesting insights into the behavior of discrete-time queues. The article is intended for those who are familiar with queueing theory basics and would like a simple, yet rigorous introductory treatment to discrete-time queues.

This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare.

Real-time monitoring systems are important for industry since they allow for avoiding unplanned system stops and keeping system availability high. The technical requirements for such systems include being both scalable and online, as the amount of generated data is increasing with time. Therefore, monitoring systems must integrate tools that can manage and analyze the data streams. The data stream management system is a stream processing tool that has the ability to manage and support operations on data streams in real-time. Several researchers have proposed and tested real-time monitoring systems which have the ability to search big data streams. In this paper, the research works that discuss the analysis of online data streams for fault detection in industry are reviewed. Based on the literature analysis, the industrial needs and challenges of monitoring big data streams are presented. Furthermore, feasible suggestions for improving the real-time monitoring system are proposed.

This article describes an efficient and effective way to apply the PDCA (Plan-Do-Check-Act) method in the design process to meet quality and stakeholders’ expectations. Through the case study of developing a smart workstation to train workers in the assembly process with a target to reduce the defects and improve the management task, the paper explores the main barriers and success factors for the PDCA cycle implemented in complex quality improvement projects. A prototype of the new workstation design is tested and shows significant benefits not only in defect reduction and management efficiency but also in newcomers’ learning process. This research can be used as a benchmark application of PDCA in quality improvement and engineering design processes with systematic and comprehensible guidance of the cycle.

Blanket factory as a textile industry is one of the manufacturing sectors in Ethiopia; however, the sector productivity is the main issue of the business owners. For the reason of improving the productivity of the sector, factors affecting productivity should be identified and prioritized since improvement is capital intensive measurement. In this research, a FAHP methodology has been developed to prioritize the identified productivity-affecting factors of the blanket factory. Productivity problem is sourced from different factors. However, the concept of productivity-affecting factors has been considered in previous literature, its integration with productivity of the blanket factory and the FAHP methodology has not been studied. For the sake of filling this gap, this research has been conducted using the following main steps: at the beginning, productivity-affecting factors have been identified from previous literature. Then, as there are many productivity-affecting factors in different manufacturing sectors, the list of potential productivity-affecting factors has been investigated to check which factors are most common in the blanket factory. Finally, a FAHP model has been applied to prioritize productivity-affecting factors. According to this model, the result showed that skilled employee and on and off job training, production process line balancing, and better technology and manufacturing system are the most important factors of productivity problem in the blanket factory. Based on the normalized weight, these factors scored 35.92%, 22.94%, and 17.06%, respectively. As the main implications, the research procedure and obtained results using the developed methodology can help industry managers, operation managers and practitioners, business owners, academicians, and researchers to determine productivity-affecting factors so that they can provide possible solutions to the blanket factory.