Operations Research Methods for Optimization in Radiation Oncology

BackgroundBusiness process modelling is increasingly used not only by the companies’ management but also by scientists dealing with process models. Process modeling is seldom done without decision-making nodes, which is why operational research methods are increasingly included in the process analyses.ObjectiveThis systematic literature review aimed to provide a detailed and comprehensive description of the relevant aspects of used operational research techniques in Business Process Model and Notation (BPMN) model.MethodsThe Web Of Science of Clarivate Analytics was searched for 128 studies of that used operation research techniques and business process model and notation, published in English between 1 January 2004 and 18 May 2020. The inclusion criteria were as follows: Use of Operational Research methods in conjunction with the BPMN, and is available in full-text format. Articles were not excluded based on methodological quality. The background information of the included studies, as well as specific information on the used approaches, were extracted.ResultsIn this research, thirty-six studies were included and considered. A total of 11 specific methods falling into the field of Operations Research have been identified, and their use in connection with the process model was described.ConclusionOperational research methods are a useful complement to BPMN process analysis. It serves not only to analyze the probability of the process, its economic and personnel demands but also for process reengineering.

The paper describes applications of Operations Research (OR) theory and techniques used to solve various types of social problems occurring in our social system. Social systems analysis has for quite some time been the main analytical and scientific approach used to investigate systems and to solve various problems related to modern social systems, including industry, business, the military, public administration, politics, and society in general. We will present here three major roles that operations research (OR) and social systems analysis (SSA) technique have played both practically and theoretically in the solution of social systems problems since it was developed almost 60 years ago. Firstly, we explain briefly OR, SSA, and public policy (PP) regarding what they are, how OR can be contributing to SSA and PP, and how traditional academic disciplines are related each other with the SSA. Secondly, we introduce several examples of the quantitative data analysis, which we have investigated in our school (National Graduate Institute for Policy Studies) to solve various types of social problems including population, traffic and accident, higher education policy, energy policy, and agriculture policy data analyses. Thirdly, we give mathematical modeling analysis with its application to the optimal location model analysis for integrating promotion branch offices in the local government. Fourthly, as an important role of OR as a theory building analysis technique, we explain two problems of apportionment problem and shortest path counting problem. Finally, in the summary section future perspectives of OR are given.

The operations research is probably one of the most successful field of applied mathematics used in economics, physics, chemistry, almost everywhere where one has to analyze huge amounts of data. Lately, these techniques of operations research were introduced in biology, especially in the protein analysis area to support biologists. The fast growth of protein data makes operations research an important issue in bioinformatics, a science which lays on the border between computer science and biology. This paper gives a short overview of the operations research techniques currently used to support structural and functional analysis of proteins.

Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML’s capability to extract patterns from large datasets and making predictive decisions and OR’s precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. and make predictive decisions under uncertainty complements the decision-making process of OR. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.

This review paper provides an in-depth exploration of optimization techniques in Operations Research (OR), highlighting their significance, recent advancements, challenges, and future directions. Operations Research utilizes mathematical and analytical methods to optimize decision-making processes across various industries. The paper begins with an overview of OR, discussing its definition, scope, and historical development. It then delves into the fundamentals of optimization, covering different types of optimization problems and algorithms. Classical optimization techniques such as Linear Programming, Integer Programming, and Nonlinear Programming are discussed, followed by an exploration of heuristic and metaheuristic optimization techniques including Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and Ant Colony Optimization. Recent advances in optimization, including hybrid methods, multi-objective optimization, optimization for big data, and integration with machine learning and artificial intelligence, are analyzed. Furthermore, the paper examines the challenges faced by optimization research and outlines emerging trends and future prospects. By synthesizing current knowledge and identifying areas for future research, this paper aims to contribute to the ongoing development and application of optimization techniques in OR and related fields.

The purpose of this chapter is to provide an overview of the operations research techniques employed across the Department of Defense, focusing on how the operations research officers and government civilians contribute to senior leader decisions. There are four general areas that the Department of Defense conducts operations research: assessing strategic goals through a gap determination process; assessing the effectiveness of operations and exercises, wargaming, modeling, and simulation of combat for strategic analysis and acquisition; and personnel analysis across the Joint Force. The authors will describe the management processes and components of military only so far as needed to provide the context and background required to adequately describe the operations research techniques. This chapter will contribute to the overall mission of this book by generalizing the techniques used by the military to demonstrate how analysts can better evaluate proposed and existing programs throughout the government and the public sector.

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Medical physics graduate programs should adjust enrollment to achieve equilibrium between graduates and residents

Currently, the model is based on the Clinical Laboratory. It is a great centre that performs almost all of the techniques. Thanks to the automation of them. These laboratories receive samples from centres that often are some kilometers away. Time spent between collection and processing must be as short as possible and this is a requirement to care about in order to process the samples in optimal conditions. Transport logistic factor is essential to achieve the right balance between optimal quality of the specimens and efficiency in managing resources. Our laboratory attends 96 centres in an area of high population density and intricate road network. We considered planning the transport paths and timetables based on a branch of mathematics called Operations Research. The aim was to plan routes from and to the laboratory that covered the specimen transport with the shortest possible time. Material and Methods: In January 2012 we planned restructuring transport routes according to two methods applied to solve the TSP problems: the nearest neighbour method and the edge classified. Although these are not top methods, they are useful to control the time spent between the sample collection centre and the laboratory applying operational research methods. Results: We identified that 12% of the paths used excessive time to reach the laboratory (more than 90 minutes). These routes were modified applying the nearest neighbour method.

This article provides an overview of the applications of operations research (OR) techniques to marine container terminals. The article begins by providing a summary of marine container terminal operations. Following this, a series of decision problems are identified, to which OR techniques have been applied. These include quayside, yard, and landside decision problems. In each section, the problems, technical approaches, and outcomes are described. With respect to quayside decision problems, the specific problems addressed include determination of quayside equipment types, berth allocation, quay crane allocation and scheduling, and operational planning and real‐time decisions related to transportation equipment. Yard decision problems include yard design, storage space allocation, and task assignment for yard handling equipment. Landside decision problems include design of the terminal's landside connection, transport planning, and real‐time control of landside operations. The article provides an overview of the state of the practice. The reader will gain an understanding of the nature of decision problems in marine container terminals, how OR techniques have been used to address these, and the operational improvements made possible through the application of OR techniques to this problem area.

Operations research in the space industry

The field of Soft Operational Research (Soft OR) has emerged from the attempt to address contextually rich, multi-actor ‘wicked’ problems that are not amenable to traditional ‘hard’ operational research techniques, which often rely on mathematical modelling. This study assesses the use of Soft OR techniques in climate change policymaking. Since climate change problems are classical wicked problems, many assume that Soft OR would be in high demand in developing climate change policy. And the review of the use of these techniques conducted here does find that in the cases where Soft OR methods have been used by academics and other non-governmental actors, they have consistently provided useful results for policymaking. It is puzzling therefore that there is little evidence of governments using Soft OR application in this area. We study two cases of explicit (in Bristol UK) and implicit use (Rhode Island, US) of such techniques by governments to explain why this is so. We argue that notwithstanding the challenges the two cases reveal in their application, Soft OR nevertheless has much to offer policymakers in the arena of climate change policymaking and deserve more attention and use.

BackgroundAgreement between planners and treating radiation oncologists (ROs) on plan quality criteria is essential for consistent planning. Differences between ROs and planning medical physicists (MPs) in perceived quality of head and neck cancer plans were assessed.Materials and MethodsFive ROs and four MPs scored 65 plans for in total 15 patients. For each patient, the clinical (CLIN) plan and two or four alternative plans, generated with automated multi-criteria optimization (MCO), were included. There was always one MCO plan aiming at maximally adhering to clinical plan requirements, while the other MCO plans had a lower aimed quality. Scores were given as follows: 1–7 and 1–2, not acceptable; 3–5, acceptable if further planning would not resolve perceived weaknesses; and 6–7, straightway acceptable. One MP and one RO repeated plan scoring for intra-observer variation assessment.ResultsFor the 36 unique observer pairs, the median percentage of plans for which the two observers agreed on a plan score (100% = 65 plans) was 27.7% [6.2, 40.0]. In the repeat scoring, agreements between first and second scoring were 52.3% and 40.0%, respectively. With a binary division between unacceptable (scores 1 and 2) and acceptable (3–7) plans, the median inter-observer agreement percentage was 78.5% [63.1, 86.2], while intra-observer agreements were 96.9% and 86.2%. There were no differences in observed agreements between RO–RO, MP–MP, and RO–MP pairs. Agreements for the highest-quality, automatically generated MCO plans were higher than for the CLIN plans.ConclusionsInter-observer differences in plan quality scores were substantial and could result in inconsistencies in generated treatment plans. Agreements among ROs were not better than between ROs and MPs, despite large differences in training and clinical role. High-quality automatically generated plans showed the best score agreements.

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This paper examines the results of a survey concerning some aspects of quantitative courses offered by North American business schools. In particular, we look at the proliferation of statistics, operations research, and production and operations management courses as well as specific operations research topics and their coverage in undergraduate curricula. This information is analysed and compared to the use of operations research models and techniques in practice as reported in the literature.