Prolegomena to OR Modelling of the Global Environment-Development Problem

Innovative Operational Research practitioner, intellectual leader in the development of systems ideas, and founder of the field of management cybernetics. Born: September 25, 1926 in London, England. Died: August 23, 2002 in Toronto, Canada. Education: D.Sc., University of Sunderland, 2000. Key positions: Production Controller and Head of OR Group, Samuel Fox (1949–1956); Head, Operational Research and Cybernetics, United Steel (1956–1961); Managing Director, SIGMA (1961–1966); Development Director, International Publishing Corporation (1966– 1970); Consultant (1970–2002). Awards and Recognition: Silver Medal, Royal Swedish Academy for Engineering Sciences (1958); Lanchester Prize, Operations Research Society of America (1966); McCullough Plaque of the American Society for Cybernetics (1970); Norbert Weiner Gold Medal, World Organization of Systems and Cybernetics (1984); Lifetime Achievement Award, UK Systems Society (1999); Honorary degrees: Doctor of Laws, Concordia University, Montreal, Canada (1988); Doctor of Economic Sciences, University of St. Gallen, Switzerland (2000). Key OR roles: President, Operational Research Society (1970–1971); President, Society for General Systems Research (1971–1972); President, World Organization of Systems and Cybernetics (1970–the time of his death). Stafford Beer was one of the most remarkable figures that Operational Research (OR) in any country has produced. A charismatic, even flamboyant, character, he founded two major and pioneering OR groups; wrote some of the best books about the subject; and was a world leader in the development of systems ideas. He is widely acknowledged as the founder of the field of International Transactions in Operational Resarch.

It is widely recognized that health-care systems both in the United States (U.S.) and in most other nations are in crisis. For example, the U.S. has the highest healthcare spending per capita in the world, its annual health expenditures exceed $2 trillion annually, and yet it lags behind several other developed nations in significant healthcare outcomes such as life expectancy or infant mortality. European health-care systems are also facing their own challenges, especially as they relate to the treatment of patients with very expensive conditions. At the same time, health-care systems in developing nations are still in their infancy and plagued by inefficiencies and inequities. Several factors contribute to the health-care systems crisis. Two of them are limited health-care budgets and innovative medical technologies that prolong life but at a very high cost. In this environment, it is natural to expect that the field of operations research (OR) has something to contribute. When resources are limited, OR models can highlight trade-offs, identify good solutions, and inform decision making. This special issue aims to showcase innovative applications of OR in the domain of health care. We received more than 60 excellent submissions that have undergone the normal review process. The papers appearing in this issue passed a dual threshold: they make a significant contribution to the field of OR by examining a health-care application, and each of them was reviewed by at least one health-care expert reviewer to verify the face validity of the health-care application and model assumptions. Three broad problem categories are reflected in the special issue papers: Health Care Policy, Treatment Optimization, and Health Care Operations Management.

Operational research (OR) is a relatively new, applied branch of mathematics, which helps provide a scientific base for the management and problem-solving. At present, its application in public health issues is gaining importance both nationally and internationally, particularly in program management and policy making. It is an interdisciplinary team approach that requires managers and researchers to work together toward identification of the problems and implementation of optimal solutions. There is rising importance accorded by international and national agencies to invest resources in OR and guide program implementation in public health, and the global fund to fight TB, AIDS, and malaria allows up to 10% of each grant to be allocated for OR. There are multitude of research methods used in the field of health. These fields of OR, implementation research, and health system research are a source of confusion for researchers. OR may sometimes be used interchangeably with implementation research or under the subheading of health system research. OR using various qualitative and mathematical models provides solutions which are currently being applied to overcome the obstacles in health policy making and implementation. Integration of OR with monitoring and evaluation is a new paradigm in program management. This paper aims to summarize the theme, relevance, approach, methods, and applications and challenges of OR in the field of public health and how it had led to policy changes in the field of health.

The field of operations research faces a visibility problem that represents a serious problem in attracting the next generation of operations researchers and in building a base of consumers of operations research studies. A major goal of the initiative described here, the High School Operations Research Outreach (HSOR), is to bring operations research into high school mathematics classrooms. This serves the dual purpose of increasing public awareness of the field of operations research and introducing rich, real-world problem situations to students of high school mathematics. HSOR has attempted to do so by developing a series of classroom materials set in OR contexts and by establishing a cadre of OR professionals who are willing and available to present these materials to groups of high school mathematics teachers.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

The use of high performance fibre reinforced cementitious composites (HPFRCC) as a strengthening material for reinforced concrete (RC) structures is promising due to their compatible mechanical and physical properties, especially their pseudo strain hardening behaviour in tension. At present, most research on HPFRCC has focused on the material behaviour, investigations of structural behaviour of components strengthened using HPFRCC are scarce. In this paper, a 3D finite element model is developed using LS-DYNA implicit for the analysis of RC beams strengthened with HPFRCC. The material model for HPFRCC is calibrated based on the available experimental data. The pseudo strain hardening behaviour is accurately captured, and the appropriate failure criteria for HPFRCC are selected. The developed numerical model and modelling technique are validated by comparing the predicted results with test data from literature.

Using a wide range of operational research (OR) optimization examples, Applied Operational Research with SAS demonstrates how the OR procedures in SAS work. The book is one of the first to extensively cover the application of SAS procedures to OR problems, such as single criterion optimization, project management decisions, printed circuit board assembly, and multiple criteria decision making. The text begins with the algorithms and methods for linear programming, integer linear programming, and goal programming models. It then describes the principles of several OR procedures in SAS. Subsequent chapters explain how to use these procedures to solve various types of OR problems. Each of these chapters describes the concept of an OR problem, presents an example of the problem, and discusses the specific procedure and its macros for the optimal solution of the problem. The macros include data handling, model building, and report writing. While primarily designed for SAS users in OR and marketing analytics, the book can also be used by readers interested in mathematical modeling techniques. By formulating the OR problems as mathematical models, the authors show how SAS can solve a variety of optimization problems.

Digitization and artificial intelligence are radically changing virtually all areas across business and society. These developments are mainly driven by the technology of machine learning (ML), which is enabled by the coming together of large amounts of training data, statistical learning theory, and sufficient computational power. This technology forms the basis for the development of new approaches to solve classical planning problems of Operations Research (OR): prescriptive analytics approaches integrate ML prediction and OR optimization into a single prescription step, so they learn from historical observations of demand and a set of features (co-variates) and provide a model that directly prescribes future decisions. These novel approaches provide enormous potential to improve planning decisions, as first case reports showed, and, consequently, constitute a new field of research in Operations Management (OM). First works in this new field of research have studied approaches to solving comparatively simple planning problems in the area of inventory management. However, common OM planning problems often have a more complex structure, and many of these complex planning problems are within the domain of capacity planning. Therefore, this dissertation focuses on developing new prescriptive analytics approaches for complex capacity management problems. This dissertation consists of three independent articles that develop new prescriptive approaches and use these to solve realistic capacity planning problems. The first article, “Prescriptive Analytics for Flexible Capacity Management”, develops two prescriptive analytics approaches, weighted sample average approximation (wSAA) and kernelized empirical risk minimization (kERM), to solve a complex two-stage capacity planning problem that has been studied extensively in the literature: a logistics service provider sorts daily incoming mail items on three service lines that must be staffed on a weekly basis. This article is the first to develop a kERM approach to solve a complex two-stage stochastic capacity planning problem with matrix-valued observations of demand and vector-valued decisions. The article develops out-of-sample performance guarantees for kERM and various kernels, and shows the universal approximation property when using a universal kernel. The results of the numerical study suggest that prescriptive analytics approaches may lead to significant improvements in performance compared to traditional two-step approaches or SAA and that their performance is more robust to variations in the exogenous cost parameters. The second article, “Prescriptive Analytics for a Multi-Shift Staffing Problem”, uses prescriptive analytics approaches to solve the (queuing-type) multi-shift staffing problem (MSSP) of an aviation maintenance provider that receives customer requests of uncertain number and at uncertain arrival times throughout each day and plans staff capacity for two shifts. This planning problem is particularly complex because the order inflow and processing are modelled as a queuing system, and the demand in each day is non-stationary. The article addresses this complexity by deriving an approximation of the MSSP that enables the planning problem to be solved using wSAA, kERM, and a novel Optimization Prediction approach. A numerical evaluation shows that wSAA leads to the best performance in this particular case. The solution method developed in this article builds a foundation for solving queuing-type planning problems using prescriptive analytics approaches, so it bridges the “worlds” of queuing theory and prescriptive analytics. The third article, “Explainable Subgradient Tree Boosting for Prescriptive Analytics in Operations Management” proposes a novel prescriptive analytics approach to solve the two capacity planning problems studied in the first and second articles that allows decision-makers to derive explanations for prescribed decisions: Subgradient Tree Boosting (STB). STB combines the machine learning method Gradient Boosting with SAA and relies on subgradients because the cost function of OR planning problems often cannot be differentiated. A comprehensive numerical analysis suggests that STB can lead to a prescription performance that is comparable to that of wSAA and kERM. The explainability of STB prescriptions is demonstrated by breaking exemplary decisions down into the impacts of individual features. The novel STB approach is an attractive choice not only because of its prescription performance, but also because of the explainability that helps decision-makers understand the causality behind the prescriptions. The results presented in these three articles demonstrate that using prescriptive analytics approaches, such as wSAA, kERM, and STB, to solve complex planning problems can lead to significantly better decisions compared to traditional approaches that neglect feature data or rely on a parametric distribution estimation.

Ethics beyond the model: How social dynamics can interfere with ethical practice in operational research/management science

Improving Patient Flow in Emergency Departments with OR Techniques: A Literature Overview

CPMS, the Practice Section of INFORMS, strives to support OR/MS practitioners by publicizing OR/MS success stories and by connecting professionals with similar interests. We are therefore pleased to present the results from the 2009 competition for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, established in memory of the late Dr. Daniel H. Wagner. Dan Wagner earned his PhD in mathematics from Brown University in 1951. His dissertation, “On free products of groups,” was published in Transactions of the American Mathematical Society in 1957. Dr. Wagner began his career in the US Navy’s Operations Evaluation Group (OEG) at the Pentagon, where he worked on operations research for naval warfare. He worked there until 1956, with a one-year leave of absence for postdoctoral research on free algebras at MIT. Dan then joined Burroughs Research Center, where he directed a group of mathematicians performing analysis for the development of digital computers. In 1957, Dan’s entrepreneurial spirit took over, and together with Dr. John D. Kettelle, he formed the partnership of Kettelle and Wagner, which was dissolved in 1963. That same year, he formed a new company, Daniel H. Wagner Associates, Inc. This company did leading-edge work in the mathematics of naval tactics, especially antisubmarine warfare, detection theory, and search planning. During his years as president and principal owner of Wagner Associates, Dan brought many highquality mathematicians into the operations research community. This led to significant advances in the firm’s fields of endeavor and delivery of significant applications to the Navy, Coast Guard, and other clients; many of these applications are still in service today. After retirement from his eponymous company, Dan continued his commitment to the field of operations research, serving in various teaching and research positions with the US Naval Postgraduate School and the US Naval Academy. He was an active member of ORSA, and then INFORMS, for more than 40 years. The idea for this prize began at Dan’s memorial service in April 1997, where many of his former colleagues gathered. Following the agreements made on that day and the subsequent pledges, the firms Metron, Inc., Daniel H. Wagner Associates, Inc., and Applied Mathematics, Inc., generously donated a total of $51,000 as an endowment to ensure the availability of a cash award in perpetuity. Each of these companies is an outgrowth in large part of Dan’s early efforts. Metron, Inc. (President and COO Tom Corwin, http://www.metsci.com) is a scientific consulting company dedicated to solving challenging problems in national defense through the development and application of advanced mathematical methods. Problem solutions are realized using computer software with graphical interfaces that allow a user to understand and act on the results without having to comprehend the intricacies of the models themselves.

Measuring Performance Evolution of Academic Journals in Management Science and Operations Research: A DEA-Malmquist Approach

Data science is a relatively new requirement for business students. Historically, many business students shied away from business statistics and quantitative or operational research (OR) modules believing them to be boring and irrelevant. The high-profile use of analytics and modelling during the COVID pandemic has drawn awareness to the relevance of analytics. Greater availability of data and modelling tools afford business students an opportunity to re-engage with operational research and analytics and to enjoy the satisfaction of modelling and solving real-world problems, but the challenge of the mathematical modelling skills gap of business students remains. In this paper, we describe a learning pathway of modules in business analytics that can enhance business students’ confidence and capabilities in performing statistical and analytical business tasks. We recommend modelling tools and incremental innovative mathematical modelling teaching approaches that are pedagogically sound and suitable for business students with varying quantitative backgrounds.

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.

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