Operations Research Research Articles

Behavioral Nudges to Encourage Appropriate Antimicrobial Use Among Health Professionals in Uganda

Background/Objectives: Antimicrobial resistance (AMR) is a global public health concern exacerbated by inappropriate antimicrobial prescribing practices, particularly in low-resource settings such as Uganda. The research aimed to develop a culturally sensitive behavioral intervention, leveraging a “nudge” strategy, to improve healthcare provider adherence to the 2016 Uganda Clinical Guidelines (UCG 2016) in five Ugandan hospitals. This intervention formed part of broader antimicrobial stewardship initiatives led by the United States Agency for International Development Medicines, Technologies, and Pharmaceutical Services Program. Methods: This study employed a mixed-methods approach, combining formative research and behavioral intervention. Guided by the Deloitte Behavioral Insights Framework, the research team conducted key informant interviews to identify prescribing barriers and motivators and developed three suitable behavioral interventions: perceived monitoring, ward leaderboards, and educational workshops. The study evaluated the interventions’ impact through point prevalence surveys (PPS), using the World Health Organization PPS methodology at three stages: pre-intervention, immediate post-intervention, and one-month post-intervention. Results: Key behavioral themes across individual, social, environmental, and organizational elements informed the intervention design and implementation. The behavioral intervention package increased antimicrobial prescription compliance with the UCG 2016 from 27% at baseline to 50% immediately post-intervention, though these effects diminished at one-month post-intervention. Conclusions: Our study addresses an existing gap in behavioral nudges-based operational research on antimicrobial prescribing in low- and middle-income countries. These results showed an immediate improvement in adherence to the UCG 2016 among healthcare providers in Ugandan hospitals, though the effect was attenuated at one-month follow-up. Despite the attenuation, behavior change presents a feasible, cost-effective, and sustainable approach to improving antimicrobial prescribing practices and addressing AMR.

Barriers and Facilitators in Implementation of Nutritional Benefit Transfer to Tuberculosis Patients

Abstract Background Malnutrition & Tuberculosis (TB) form a vicious cycle. Nikshay Poshan Yojana (NPY) is a key strategy for a TB free India, that seeks to provide financial assistance for nutritional support to TB patients. The study is a qualitative study to identify operational challenges and facilitators in implementation of NPY Methods In Depth Interview (IDI) of 18 Senior Treatment Supervisor (STS) at Tuberculosis Unit (TU) and 19 Tuberculosis Health Visitor (TB HV) at Designated Microscopy Centre’s (DMC) was done. For further insights, the STS, at District Tuberculosis Centre, Lucknow was also interviewed. Informal discussion was done with about 50 Tuberculosis patients. We explored providers perspective on impact of NPY on other duties/services, case load, hours dedicated to NPY, practice protocols, record keeping and data management, compliances, capacity building needs, quality protocols, key enablers, motivating factors, challenges, best practices and beneficiary’s perspectives on quality of life, facilitators and challenges in availing services. Results Motivating leadership, supportive staff, good working relationship and seamless communication, teamwork, use of technology, supportive supervision & collaboration and support of NGO for private sector were the key facilitators. Barriers comprised system level barriers (Budget constraints, issues with NIKSHAY software and bank account), provider level barriers (shortage of human resource, nonfunctional tablets) and patient level barriers (stigma and bank account related challenges). The encouraging finding was majority of recipients, responding that they used the incentive to purchase ration, vitamin supplements etc. Conclusions NPY is having its intended benefit but there are barriers that need to be resolved for successful roll out. The study yield suggestions for removing barriers and found a set of innovations that can be replicated across other settings with high Tuberculosis burden Key messages • Malnutrition and Tuberculosis form a vicious cycle. Nutritional support to TB patients improves treatment outcome and can save lives. • Operational research on implementation of NPY offers insights for successful roll out of this key initiative across other resource limited settings in developing countries.

The EU Health Task Force, supporting preparedness and response in the European Union and globally

Abstract The COVID-19 pandemic revealed shortcomings in the European Union (EU) mechanisms for managing public health threats. In 2022, the European Centre for Disease Prevention and Control (ECDC) was mandated to establish the EU Health Task Force (EUHTF) to strengthen preparedness and response at country, EU and global levels. The EUHTF is coordinated by an ECDC Permanent Capacity and relies on pools of experts including ECDC experts, ECDC fellows and European public health experts that can be mobilized for specific assignments or broad public health emergencies. Members from EU/ Economic European Area (EEA) countries, EU agencies, the European Commission, WHO and non-profit organizations advise ECDC on the EUHTF establishment. From May 2023 to April 2024, the EUHTF addressed 13 requests for support from seven EU countries and three non-EU countries. The requests were related to preparedness (e.g. after-action reviews, simulation exercises, mass gathering preparation, capacity building) and emergency response activities (e.g. risk assessment, operational research during crisis, emergency and outbreak response). The EUHTF is operational and able to mobilize expertise from ECDC, the ECDC fellowship and EU/EEA countries. The EUHTF Pool of External Experts, comprised of public health professionals across various levels and institutions of the EU/EEA, is poised to offer comprehensive and robust expertise. Through these resources, the EUHTF is collaborating with and reinforcing the work of EU institutions and other international partners in strengthening preparedness and response. The EUHTF is a new and structured EU-level approach to public health crises, ensuring tailored in-country support to EU and non-EU countries in preparedness strengthening and crisis response.

Building Resilience: Strengthening European public health workforce through ECDC Fellowship Programs

Abstract The European Centre for Disease Prevention and Control (ECDC) Fellowship programmes offer a two-year training in field epidemiology (EPIET) and in public health microbiology (EUPHEM) aiming to create and maintain a network of highly trained field epidemiologists and public health microbiologists and strengthen the public health epidemiology workforce in the European Union (EU) and beyond. The Mediterranean and Black Sea Programme for Intervention Epidemiology Training (MediPIET) also aims to strengthen the public health workforce in countries neighboring the EU. During the training, fellows who are based in different public health institutes in the EU and neighboring countries, set-up, implement and evaluate surveillance of communicable diseases, investigate outbreaks and conduct operational research directly linked to public health actions. The programmes facilitate knowledge exchange and collaboration across borders, building a foundation for participants and their institutions and promoting long-term collaboration through the alumni network. Participants from diverse disciplinary and cultural backgrounds enable interdisciplinary and multi-sectoral knowledge transfer, enhancing the effectiveness of public health responses. By facilitating collaboration and networking, exchanging experiences, disseminating best practices and assimilating lessons learned, these programmes contribute to addressing contemporary health challenges, including emerging vector-borne diseases and strengthening public health systems in Europe and neighboring regions. In the face of global challenges such as climate change and globalization, the fellowships exemplify how collaboration can strengthen Europe’s defenses against emerging health threats.

Explainable Optimisation through Online and Offline Hyper-heuristics

Abstract - Research in the explainability of optimisation techniques has largely focused on metaheuristics and their movement of solutions around the search landscape. Hyper-heuristics create a different challenge for explainability as they make use of many more operators, or low-level heuristics and learning algorithms which modify their probability of selection online. This paper describes a set of methods for explaining hyper-heuristics decisions in both online and offline scenarios using selection hyper-heuristics as an example. These methods help to explain various aspects of the function of hyper-heuristics both at a particular juncture in the optimisation process and through time. Visualisations of each method acting on sequences provide an understanding of which operators are being utilised and when, and in which combinations to produce a greater understanding of the algorithm-problem nexus in hyper-heuristic search. These methods are demonstrated on a range of problems including those in operational research and water distribution network optimisation. They demonstrate the insight that can be generated from optimisation using selection hyper-heuristics, including building an understanding of heuristic usage, useful combinations of heuristics and heuristic parameterisations. Furthermore the dynamics of heuristic utility are explored throughout an optimisation run and we show that it is possible to cluster problem instances according to heuristic selection alone, providing insight into the perception of problems from a hyper-heuristic perspective.

Towards resilience: Primal large-scale re-optimization

Perturbations are universal in supply chains, and their appearance has become more frequent in the past few years due to global events. These perturbations affect industries and could significantly impact production, quality, cost/profitability, and consumer satisfaction. In large-scale contexts, companies rely on operations research techniques. In such a case, re-optimization can support companies in achieving resilience by enabling them to simulate several what-if scenarios and adapt to changing circumstances and challenges in real-time. In this paper, we design a generic and scalable resilience re-optimization framework. We model perturbations, recovery decisions, and the resulting re-optimization problem, which maximizes resilience. We leverage the primal information through fixing, warm-start, valid inequalities, and machine learning. We conduct extensive computational experiments on a real-world, large-scale problem. The findings highlight that local optimization is enough to recover after perturbations and demonstrate the power of our proposed framework and solution methodology.

Pre-extension Popularization of Improved Dual Purpose Koekoek Chickens in Tahtay Maichew District Central Zone of Tigray, Ethiopia

Central zone of Tigray has potential and poultry stock. However, the productivity in terms of egg and meat is low since the majority of household depends on local chickens and rearing of their chickens through a traditional production practices. Then, the objectives of the pre-extension popularization of Koekoek breed was i) to popularize and evaluate the production performance the Koekoek breed chickens in the small holder Farmers and ii) To analyze farmers feedback towards this improved Koekoek chickens breed at smallholder farmers' management. T/maichew District, My-sie kebelle was selected based on the project financial support of Irish Aid Operational Research (OR) purposely. A total of 17 household heads willingly participated to adopt the Koekoek breeds. Data record sheet and semi-structured questionnaires were prepared to collect the required data. Then, using simple descriptive and Likert measurement level used to achieve result. The finding of the research shows that an annual average yield was found to be 204 eggs per year/ chicken. Similarly, the respondents agreed on that the chickens have well adapted, good growing ability, relatively resistance to diseases, have good scavenging ability, and productivity as well have medium eggs size and good egg and meat tasty of Koekoek chickens. The improved chickens’ breed (Koekoek) is better to be transferred to other mandate areas of Axum Agricultural Research Center. In addition, collaboration of stakeholders needs to be strengthening so that large scale demonstration community production system is going to be functional. Strengthen the human resource and institutional innovations are important point for dissemination of the improved poultry breeds to all beneficiaries of small holder farmers.

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices

Enhancing operational research in mechatronic systems via modularization: comparative analysis of four clustering algorithms using validation indices

The Field Assessment of Quiescent Egg Populations of Aedes aegypti and Aedes albopictus during the Dry Season in Tapachula, Chiapas, Mexico, and Its Potential Impact on Vector Control Strategies.

Although integrated management and control programs implement intense control measures for adult, pupal, larval, and breeding sites during outbreaks, there is a lack of studies to understand the role of the vector egg stage in disease dynamics. This study aimed to assess the dry season quiescent Aedes aegypti and Aedes albopictus egg populations in houses and backyards in Tapachula, southern Mexico. Two hundred and fifty ovitraps were placed in 125 homes in the Las Americas neighborhood. A total of 7290 eggs were collected from 211 (84.4%) ovitraps. Only 5667 (77.7%) hatched under insectary water immersion and food supply conditions, with 4031 (71.1%) identified as Ae. aegypti, and 1636 (28.8%) as Ae. albopictus, respectively. The remaining 1623 (22.3%) did not hatch due to Delayed Hatching and/or quiescence tropical stage. Eighty-three larval containers were sampled with desiccated eggs during the dry season; most of them were described as trash waste because larvicides are only used for larger containers of 5-10 L. Evolutionary characteristics for the two species including partial egg hatching, ambient-regulated quiescence, the ability of the embryo to survive for a more extended period intra-seasonally, the egg sticking to inner container walls, demands urgent operational research to achieve successful egg-proof larval container methods.

Editorial for the Special Issue: "Novel Solutions and Novel Approaches in Operational Research"

Abstract This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.

An evaluation model for an optimal decarbonisation process in the built environment

PurposeThe goal is to deliver a decision-support framework to both public and private entities engaged in energy retrofit investments in the property market.Design/methodology/approachThe evaluation algorithm that is being offered takes an innovative approach to financial and economic analysis. Its foundation is a market-driven/cost-driven method, drawing logic from operational research and goal programming.FindingsThe algorithm is tested to a real estate portfolio yielding an optimal asset retrofitting schedule. The ranking list is determined by taking into consideration a variety of parameters, including investment costs and total CO2 emissions from energy retrofit initiatives. The Carbon Risk Real Estate Monitor’s (CRREM) emission targets for 2030 are employed as a reference point in the process of creating a ranking list of the assets that compose the real estate portfolio under examination.Practical implicationsThe evaluation algorithm will allow to determine, in a real estate portfolio, a priority list of assets to be enhanced. This is accomplished by taking into account the client’s financial resources, the overall cost of the intervention programmes for each asset, and the effects that each asset would have on the environment and the energy once the suggested retrofit programme is put into place.Originality/valueThe study proposes a methodological approach that seeks to balance the optimisation of energy performance, the reduction of environmental effect, the promotion of social well-being and economic sustainability in the context of managing the current property sector.

Operational Research to Support Rapid Evidence-Based Responses to Outbreaks: Learnings from COVID-19.

During the COVID-19 pandemic, the need for making testing readily available was recognized as an important factor for individuals to help make informed decisions, including to isolate or seek care, and for policymakers to control transmission. Toward this end, FIND and the Access to COVID-19 Tools Accelerator funded 16 rapid operational research studies and one implementation project in Africa, the Caribbean, and Asia evaluating the utility, acceptability, and feasibility of different community-based SARS-CoV-2 testing approaches. Here, we discuss common factors and challenges encountered during study implementation. We note six key factors essential for success: 1) collaboration and partnerships; 2) buy-in of local stakeholders, including communities; 3) access to affordable supplies; 4) flexible financing; 5) effective approval systems; and 6) a skilled and motivated workforce. We also note various challenges that must be addressed to fully capitalize on these success factors. In particular, ethics committees are often not well equipped to assess operational research during outbreaks. Outbreaks, especially of novel pathogens, are unpredictable, and transmission dynamics are even more likely to change if the pathogen is prone to frequent mutations, such as SARS-CoV-2. Research that aims to evaluate strategies for curbing transmission must hence be easily and swiftly adaptable. This requires flexibility from researchers, funders, staff conducting the studies, and ethics and other approval committees. International guidelines for evaluating operational research protocols in outbreaks are needed to provide timely evidence to enable informed decisions by individuals, communities, and policymakers, thereby reducing both the human and the economic impact of outbreaks.

Improving production sequencing in the age of Industry 4.0: A mathematical launching model approach

Purpose: To enhance enterprise efficiency, this study examines the pivotal role of operations management in optimizing the use of materials, technology, equipment, and personnel, especially within the transformative framework of Industry 4.0.Design/methodology/approach: This article investigates operational preparation in the context of Industry 4.0. Through questionnaires and interviews with stakeholders, problems related to operational preparation were identified. To address these problems, a launching model was developed for production systems. The model considers several parameters important for the production process, including business goals, customer needs, and environmental conditions that impact enterprise performance and profit.Findings: The model determines the importance of jobs to be performed in a certain order to optimize production sequence. The proposed parameters are included in a mathematical-algorithmic launching model based on categorical levels related to the production process, such as profit, delivery times, processing times, total number of technological operations, product types, materials, required quality, product complexity, and resource use. The model was developed based on observations and investigations conducted in Kosovo's enterprises on operations research, and it has the potential to significantly improve production efficiency and profitability in the Industry 4.0 era.Practical implications: The findings of this study have practical implications for operations management within the Industry 4.0 framework, proposing a launching model designed to optimize production processes and enhance efficiency.Originality/value: The originality and value of this research lie in the development of a mathematical-algorithmic launching model that addresses operational preparation in the Industry 4.0 context, taking into account various crucial parameters.

The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify

In the paper, “The Analytics of Robust Satisficing: Predict, Optimize, Satisfice, Then Fortify,” published in Operations Research, authors Sim, Tang, Zhou, and Zhu introduce a novel approach to decision making under uncertainty. Their method, termed “estimation-fortified robust satisficing,” leverages advanced predictive and prescriptive analytics to optimize decisions where traditional models falter due to risk ambiguity and estimation uncertainties. This approach not only enhances the resilience of decisions against unforeseen variations but also consistently outperforms conventional predictive methods in scenarios characterized by sparse data. This significant advancement promises to fortify decision-making processes in critical sectors such as finance and operations management, offering a new paradigm in handling the inherent uncertainties of real-world systems.

Improving Performance in Surgical department through Communicating Information to Decision Makers: Intervention Study in a tertiary Hospital

Hospital performance evaluation is crucial for early detection of pitfalls, and problem-solving. Data shouldn’t be passively collected. They must be measured and understood through an underlying conceptual framework. Purpose: The General Surgical Department in collaboration with the Public Health Department had the objective of developing a module for periodic monitoring and evaluation of the eight general surgical units based on calculated key performance indicators (KPIs), Composite Indices (CIs) and communication of growing out information through policy briefs directed to the heads of units for better resources allocation and evidence based decision making. Methodology: An operations research using pre-post intervention design. The exploratory component was completed in six months in the 8 units of the department. Pretest service statistical data analyzed KPIs derived from hospital bed and surgical room utilization. Data were utilized for the calculation of 5 CIs for each unit. Study intervention included the development of policy briefs (PB) that were submitted to the head of the department. Informed decisions were made guided by the info-graph data in the form of traffic color coded ranking matrix to promote various services and to improve performance in the department units particularly lower ranked units. The post-test included reanalyzing of the service statistical data. Findings: Comparing pre-posttest figures revealed improvement in the calculated KPIs and CIs. Originality and practical implications: Bringing up a novel, feasible monitoring and evaluation model using refitted tools would support surgical unit heads in evidence-based decision-making and eventually improve performance efficiency.

Artificial intelligence in industrial operations management: a bibliometric analysis

Considering the exponential growth of research on Artificial Intelligence (AI) in industrial operations management, this study aims to map the scientific landscape through a bibliometric analysis. The research employed data from the Web of Science, focusing on key terms such as "AI," "industrial operations," and "management." Using VOSviewer, co-occurrence networks and citation analyses were generated to identify research trends and gaps. The results reveal significant contributions from countries like the United States and China, emphasizing AI's role in enhancing efficiency and innovation in industries. The findings provide a foundation for future research and practical implementation strategies in industrial operations.

A survey of some operations research and data sciences applications developed in the last 20 years at Argentinian and Chilean Universities

A survey of some operations research and data sciences applications developed in the last 20 years at Argentinian and Chilean Universities