Advances in Operations Research
 Journal metrics
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Acceptance rate6%
Submission to final decision113 days
Acceptance to publication15 days
CiteScore3.500
Journal Citation Indicator0.170
Impact Factor1.2

Malmquist–Luenberger Productivity Index for a Two-Stage Structure in the Presence of Undesirable Outputs and Uncertainty

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Advances in Operations Research publishes original research and review articles contributing to the theory and methodology of operational research.

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Advances in Operations Research maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

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Review Article

A Review of Birth-Death and Other Markovian Discrete-Time Queues

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.

Research Article

Decision-Making Based on Predictive Process Monitoring of Patient Treatment Processes: A Case Study of Emergency Patients

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.

Research Article

Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements

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.

Research Article

PDCA from Theory to Effective Applications: A Case Study of Design for Reducing Human Error in Assembly Process

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.

Research Article

Application of FAHP Methodology to Rank Productivity-Affecting Factors in Blanket Factory: A Case Study

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.

Research Article

Benchmarking the Interactions among Green and Sustainable Vendor Selection Attributes

The primary motive of each and every organization or company is to sustain, strengthen, upgrade, and improve its position and standard in the highly dynamic, constantly changeable, aggressive, and competitive environment. There is a very urgent requirement to develop a framework for green and sustainable vendor selection in the organizations. The main aim of this research is to explore, identify, examine, and evaluate the important, applicable, green, and sustainable vendor selection attributes and to analyze and determine their interactions or relationships in the area of green and sustainable vendor selection. A total of ten important attributes have been determined through review reports and by the assessments of the group of professionals belonging to various organizations. A questionnaire has been prepared for these ten attributes and feedback was maintained from the judgments of the group of professionals in dairy industries. In this paper, a multicriteria decision-making technique namely interpretive structural modeling is implemented to look over the interactions and connections among various attributes and to put forward a constructional representation or digraph. Matriced Impacts Croise’s Multiplication Appliqúe and Classment inspection is used to identify which attributes are autonomous, dependent, independent, influencing or not influencing, and importance of one criterion over another according to their driving and dependence powers. It is analyzed numerically from this model that the cost attribute is a very remarkable attribute as it exists in the bottom or sixth level and the attributes economic growth, financial capacity, and research ability are at first or topmost level of this model. The interpretive structural modeling hierarchy constructional representation and Matriced Impacts Croise’s Multiplication Appliqúe and Classment inspection will support the company’s owner or decision maker for attaining a best decision.

Advances in Operations Research
 Journal metrics
See full report
Acceptance rate6%
Submission to final decision113 days
Acceptance to publication15 days
CiteScore3.500
Journal Citation Indicator0.170
Impact Factor1.2
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