Operations Research Research Articles

Research and Practice of the Blended Teaching Model of BOPPPS Teaching Method Under the Background of Digital Education: Taking Operations Research Course as an Example

The rapid development of digital education provides new opportunities and challenges for teaching model innovation. This study aims to explore the application of the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory learning, Post-assessment, Summary) teaching method in the development of a blended teaching model for the Operations Research course under the background of digital education. In response to the characteristics of the course and the needs of the student group, the teaching design is reconstructed with a student-centered approach, increasing practical teaching links, improving the assessment and evaluation system, and effectively implementing it in conjunction with digital educational technology. This teaching model has shown significant effectiveness in the context of digital education, providing valuable experience and insights for the innovation of the Operations Research course.

Operations Research at Ryanair

Operations Research at Ryanair

Operations Research at M.A.I.O.R. srl

Operations Research at M.A.I.O.R. srl

Comparison of HTN Planning and OR-based Approaches for Solving Problems in Logistics Domains

Logistics is a day-to-day real-life situation leading to a very large domain. Among the various kinds of logistics problems we encounter are those pertaining to transportation, product inventories and location. Traditionally, Operations Research (OR) techniques are used most often and are popular candidates for solving logistics problems. With the development of Hierarchical Task Network (HTN) planning algorithms, there has been a recent trend to use HTN planners to address planning problems in general. It is also envisaged that HTN planners could solve logistics problems; yet, no proof or evidence could be found as to whether this approach is more efficient or suitable to handle logistics problems. In this paper, a comparison is done to ascertain whether HTN planning could be more efficient than OR-based approaches in solving logistics problems. Results revealed that HTN planning outperforms the traditional OR-based approach, especially in solving moderate-sized and more difficult harder problems. Furthermore, HTN planning is capable of solving very large scale problems, whereas OR based approach was unable to handle them within the same computational resource limitations.Keywords: Logistics, Operations research, Hierarchical task network, Transportation simplex method, HyperTensioN

Operations Research at Norfolk Southern Railway Company

Operations Research at Norfolk Southern Railway Company

ADAPTATION OF AN AGRICULTURAL PROJECT TO THE REGULATION OF GREENHOUSE GAS EMISSIONS

Using an original methodological approach, a model analysis of the adaptation of an agricultural project to the regulation of greenhouse gas emissions from agricultural land was carried out for the farms in the south of Russia. Emissions from both technological and ecosystem processes were taken into account. Methods of operations research and simulation modeling are applied to the study of two options for regulating greenhouse gas emissions, i.e. through market mechanisms focusing on the cost of carbon units in the EU and through the administrative control, which sets the emission limits. For each of the options, the optimal intensity of use was estimated for each land plot involved in the project.The proposed methodology supplements the scientific and methodological support for the analysis of the largescale agricultural projects under the state regulation of the impact of economic activities on the Earth’s climate. It is applicable to projects with a significant spatial dispersion of plots. As a result of the study, the scientific visions of the climate policy impact on the technological configuration of agricultural production were specified. A significant margin of sustainability of agriculture in the south of Russia to carbon market methods has been revealed; It is shown that as the restrictive administrative approaches are tightened, farmers prefer to stop the intensive use of areas with the highest productivity potential while maintaining the maximum production intensity within the rest. The totality of the results obtained is recommended for practical application for developing and analyzing investment agricultural projects, including the analysis of project risks, as well as for improving the state environmental and climate policy as a tool for analyzing its consequences.

The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem

The permutation flowshop scheduling problem (PFSP) attracted much interest from the operations research (OR) community, resulting in various heuristic and metaheuristic methods over the past half-century. However, given the hard nature of the PFSP, efficient algorithms rely on the configuration of initial solutions and sophisticated heuristics. Combining OR and artificial intelligence (AI), this paper investigates the marriage of OR and reinforcement learning for the PFSP. A novel method integrating NEH into the Q-learning algorithm is proposed for solving the PFSP with makespan criterion. With the refinement of action selection by strengthening good actions and weakening bad actions, the proposed Q-NEH algorithm shows a better learning and convergence rate, and outperforms the Q-learning algorithm for all 120 instances in Taillard’s benchmark dataset, with a significantly decreased average relative learning error (RLE) from 13.05 % to 1.73 %. Furthermore, by comparing the performances of the Q-NEH algorithm with related state-of-art benchmark algorithms in a wide range set of instances, it further confirms the superiority and stability of the Q-NEH algorithm in statistics. With a total of 2570 independent tests in two phases of experimental evaluations, the superiority of the Q-NEH algorithm for the PFSP suggests that the integration of OR and AI is a promising research direction for solving complex scheduling problems.

Systems dynamics research in management and organization studies: Overview and research agenda

This paper presents a bibliometric analysis of the systems dynamics (SD) research landscape, drawing on 2,091 documents from Scopus and Web of Science. This research employs bibliometric techniques to explore the evolution of the scientific community over the past 50 years and assess research productivity and impact. Through network analysis, the study further reveals the field's social and conceptual structures. This approach revealed four pivotal thematic clusters, which were discussed based on content analysis: (1) operations research and strategy formulation, (2) behavioral studies and collaborative approaches, (3) dynamic performance management, and (4) systems thinking for sustainable development. The findings reveal a diverse and interdisciplinary trajectory of SD research, reflecting its integration into a broad array of fields and its potential to inform both theoretical and practical applications. The paper concludes by providing targeted recommendations for future SD research, with a particular emphasis on enhancing management and organizational studies through the incorporation of SD methodologies. This includes the potential for SD to influence the design of adaptive strategies, the use of SD in participatory policymaking, and the application of SD tools in promoting organizational learning and sustainability.

Optimization of bike-sharing repositioning operations: A reactive real-time approach

One of the critical issues in the operation of vehicle-sharing systems is the optimization of the fleet repositioning movements. Repositioning implies the artificial movement of vehicles from places where they accumulate to others in which they are scarce. This yields a higher vehicle availability, without over dimensioning the vehicle fleet and while increasing the vehicle utilization rates. In the particular case of bike-sharing systems, repositioning implies to deploy a fleet of small trucks or vans able to move groups of bicycles from one location to another, with the purpose of maximizing the users’ level of service while minimizing the operating agency costs. This repositioning optimization problem has been previously addressed in the operations research field through Mixed Integer Programing (MIP) and its variants, generally facing two limitations. First, its high computational cost, which prevents achieving direct solutions in realistically large systems. So, it has been necessary to develop heuristics and approximations. And second, its reliance and sensitivity to demand forecasts, with its inherent level of uncertainty. Aiming to overcome these weaknesses, this paper presents a strategy based on a real-time pairwise assignment between repositioning trucks and tasks, in order to optimize the bike-sharing repositioning operations. The proposed method is conceptually simple, less dependent on demand predictions, easily implementable in any coding language and applicable to large systems at a low computational cost. These properties make the method appealing to address the repositioning task assignment in any vehicle-sharing system. On a simulated case study, based on Bicing, the bicycle-sharing system in Barcelona, the proposed strategy has been implemented and compared to the MIP-based routing approach. Results show that the proposed real-time pairwise assignment method is able to significantly improve the performance of the repositioning operations, especially in scenarios where the demand forecast is not accurate. Being based on real-time information, the proposed strategy is flexible enough to solve unpredictable situations. So, the proposed strategy can be implemented as an alternative to MIP-based solutions, or as a complementary strategy for dynamic real-time adaptation of static long-term solutions.

Best Practices for Hepatitis C Linkage to Care in Pregnant and Postpartum Women: Perspectives from the TiP-HepC Community of Practice

There is an increasing burden of hepatitis C virus (HCV) among persons of reproductive age, including pregnant and breastfeeding women, in many regions worldwide. Routine health services during pregnancy present a critical window of opportunity to diagnose and link women with HCV infection for care and treatment to decrease HCV-related morbidity and early mortality. Effective treatment of HCV infection in women diagnosed during pregnancy also prevents HCV-related adverse events in pregnancy and HCV vertical transmission in future pregnancies. However, linkage to care and treatment for women diagnosed in pregnancy remains insufficient. Currently, there are no best practice recommendations from professional societies to ensure appropriate peripartum linkage to HCV care and treatment. We convened a virtual Community of Practice (CoP) to understand key challenges to the HCV care cascade for women diagnosed with HCV in pregnancy, highlight published models of integrated HCV services for pregnant and postpartum women, and preview upcoming research and programmatic initiatives to improve linkage to HCV care for this population. Four-hundred seventy-three participants from 43 countries participated in the CoP, including a diverse range of practitioners from public health, primary care, and clinical specialties. The CoP included panel sessions with representatives from major professional societies in obstetrics/gynecology, maternal fetal medicine, addiction medicine, hepatology, and infectious diseases. From this CoP, we provide a series of best practices to improve linkage to HCV treatment for pregnant and postpartum women, including specific interventions to enhance co-location of services, treatment by non-specialist providers, active engagement and patient navigation, and decreasing time to HCV treatment initiation. The CoP aims to further support antenatal providers in improving linkage to care by producing and disseminating detailed operational guidance and recommendations and supporting operational research on models for linkage and treatment. Additionally, the CoP may be leveraged to build training materials and toolkits for antenatal providers, convene experts to formalize operational recommendations, and conduct surveys to understand needs of antenatal providers. Such actions are required to ensure equitable access to HCV treatment for women diagnosed with HCV in pregnancy and urgently needed to achieve the ambitious targets for HCV elimination by 2030.

Analysis of municipal solid waste as a source of energy production using fuzzy decision system

One of the major causes of global environmental deterioration is the enormous amount of municipal solid waste generated as a result of increasing industrialization and expanding populations. Managing the limited land resources of metropolitan areas and the growing volumes of waste generated as a result of development has become increasingly challenging for decision makers. Anaerobic digestion, gasification, pyrolysis, and incineration are among methods that help to derive energy from the various types of municipal wastes. Using these processes, waste is converted into energy, which is then used to produce power. It reduces the burden on landfills and helps in the fight against pollution in the environment. This article proposes the optimal municipal waste that can be used to derive energy using fuzzy multiple criteria decision making. This area of operational research focuses on achieving the best outcomes in intricate situations with a variety of indicators, competing goals, and requirements. This tool is gaining popularity in the sector of energy extraction because it allows decision makers to make judgments while considering all criteria and objectives at the same time. Finally, sensitive and comparative study is provided to determine the sturdiness of the proposed system.

Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

Coupling life cycle audition and operation research methods to achieve sustainable rapeseed production system

The role of drones in disaster response: A literature review of operations research applications

AbstractThe increasing use of drones has led to growing interest in their potential applications in disaster response. In this study, we examine the role of drones in disaster management under three categories of operation types, which are information collection, delivery, and communication network recovery. Focusing on an operations research (OR) perspective, we analyze and summarize the important characteristics of different problem structures and highlight the solution approaches. We also identify gaps and limitations in the current literature and propose future directions for further investigation. Overall, the study provides a comprehensive summary and valuable insights for researchers and practitioners interested in exploring the use of drones for humanitarian purposes and developing OR‐based approaches to support decision‐making.

THE METHOD OF RANKING EFFECTIVE PROJECT SOLUTIONS IN CONDITIONS OF INCOMPLETE CERTAINTY

The subject of research in the article is the process of ranking options in support systems for project decision-making under conditions of incomplete certainty. The goal of the work is to increase the efficiency of technologies for automated design of complex systems due to the development of a combined method of ranking effective options for building objects in conditions of incomplete certainty of input data. The following tasks are solved in the article: analysis of the current state of the problem of ranking options in support systems for project decision-making; decomposition of problems of system optimization of complex design objects and support of project decision-making; development of a variant ranking method that combines the procedures of lexicographic optimization and cardinal ordering in conditions of incomplete certainty of input data. The following methods are used: systems theory, utility theory, optimization, operations research, interval and fuzzy mathematics. Results. According to the results of the analysis of the problem of supporting project decision-making, the existence of the problem of correctly reducing subsets of effective options for ranking, taking into account factors that are difficult to formalize and the experience of the decision-maker (DM), was established. Decomposition of the problems of system optimization of complex design objects and support for project decision-making was carried out. For the case of ordinalistic presentation of preferences between local criteria, an estimate of the size of the rational reduction of subsets of optimal and suboptimal options for each of the indicators is proposed. Its use allows for one approach to obtain a subset of effective variants of a given capacity for analysis and final selection of the DM. A method of transforming the ordinalistic presentation of preferences between local criteria to their quantitative presentation in the form of weighting coefficients is proposed. Conclusions. The developed methods expand the methodological foundations of the automation of processes supporting the adoption of multi-criteria project decisions. They make it possible to correctly reduce the set of effective alternatives in conditions of incomplete certainty of the input data for the final choice, taking into account factors that are difficult to formalize, knowledge and experience of ODA. The practical use of the obtained results will allow to reduce the time and capacity complexity of the procedures for supporting project decision-making, and due to the use of the technology of selection of subsets of effective options with intervally specified characteristics - to guarantee the quality of project decisions and to provide a more complete assessment of them.

Safety and effectiveness of three novel all-oral shortened regimens for rifampicin- or multidrug-resistant tuberculosis in Kazakhstan.

In 2019, WHO called for operational research on all-oral shortened regimens for multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB). We report safety and effectiveness of three nine-month all-oral regimens containing bedaquiline (Bdq), linezolid (Lzd), and levofloxacin (Lfx) and reinforced with cycloserine (Cs) and clofazimine (Cfz), delamanid (Dlm) and pyrazinamide (Z), or Dlm and Cfz. We conducted a prospective cohort study of patients initiating treatment for pulmonary MDR/RR-TB under operational research conditions at public health facilities in Kazakhstan. Participants were screened monthly for adverse events. Participants with baseline resistance were excluded from the study and treated with a longer regimen. We analyzed clinically relevant adverse events of special interest in all participants and sputum culture conversion and end-of-treatment outcomes among individuals who were not excluded. Of 510 participants, 41% were women, median age was 37 years (interquartile range: 28-49), 18% had a body mass index <18·5 kg/m2, and 51% had cavitary disease. Three hundred and ninety-nine (78%) initiated Bdq-Lzd-Lfx-Cs-Cfz, 83 (16%) started Bdq-Lzd-Lfx-Dlm-Z, and 28 (5%) initiated Bdq-Lzd-Lfx-Dlm-Cfz. Fifty-eight individuals (11%) were excluded from the study, most commonly due to identification of baseline drug resistance (n = 52; 90%). Among the remaining 452 participants, treatment success frequencies were 92% (95% confidence interval [CI]: 89 to 95), 89% (95%CI: 80 to 94), and 100% (95%CI: 86 to 100) for regimens with Cs/Cfz, Dlm/Z, and Dlm/Cfz respectively. Clinically-relevant adverse events of special interest were uncommon. All regimens demonstrated excellent safety and effectiveness, expanding the potential treatment options for patients, providers, and programs.

Travel Salesman Problem Using Dynamic Programming

In the fields of operations research and computer science, the traveling salesman problem (also known as TSP) is a classic algorithmic issue. One method for solving TSP is dynamic programming, which identifies the optimal path at the lowest possible cost. It starts by determining the shortest path between two points, and then it expands to identify routes to other sites. In order to solve the Travel salesman Problem, Dynamic Programming was used in this study as a a solution technique. In addition to a real-world application from AM-EXPRESS NIGERIA LIMITED, illustrative examples of travel salesman difficulties were taken into consideration and handled utilizing the dynamic programming technique. The finding's outcome showed that the least amount of money needed to provide logistical services is ₦6500, and the shortest possible paths or routes were found.

Cost and cost-effectiveness of BPaL regimen used in drug-resistant TB treatment in the Philippines.

In 2022, the WHO announced that the 6-month BPaL/M regimen should be used for drug-resistant TB (DR-TB). We estimate the patient and provider costs of BPaL compared to current standard-of-care treatment in the Philippines. Patients on BPaL under operational research, or 9-11-month standard short oral regimen (SSOR) and 18-21-month standard long oral regimen (SLOR) under programmatic conditions were interviewed using the WHO cross-sectional TB patient cost tool. Provider costs were assessed through a bottom-up and top-down costing analysis. Total patient costs per treatment episode were lowest with BPaL (USD518.0) and increased with use of SSOR (USD825.8) and SLOR (USD1,023.0). Total provider costs per successful treatment were lowest with BPaL (USD1,994.5) and increased with SSOR (USD3,121.5) and SLOR (USD10,032.4). Compared to SSOR, BPaL treatment was cost-effective at even the lowest willingness to pay threshold. As expected, SLOR was the costliest and least effective regimen. Costs incurred by patients on BPaL were 37% (95% CI 22-56) less than SSOR and 50% (95% CI 32-68) less than SLOR, while providers could save 36% (95% CI 21-56) to 80% (95% CI 64-93) per successful treatment, respectively. The study shows that treatment of DR-TB with BPaL was cost-saving for patients and cost-effective for the health system.

Lifetime Performance of the Operational Hurricane Weather Research and Forecasting Model (HWRF) for North Atlantic Tropical Cyclones

Abstract The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA’s National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program.

Efficient Parallel Algorithm for Minimum Cost Submodular Cover Problem with Lower Adaptive Complexity

In this paper, we study the Minimum Cost Submodular Cover (MCSC) problem over the ground set of size [Formula: see text], which aims at finding a subset with the minimal cost required so that the utility submodular function exceeds a given threshold. The problem has recently attracted a lot of attention due to its applications in various domains of operations research and artificial intelligence. However, the existing algorithms for this problem may not be effectively parallelized because of their costly adaptive complexity. This paper proposes an efficient parallel algorithm that returns a [Formula: see text]-bicriteria approximation solution within [Formula: see text] adaptive complexity, where [Formula: see text] are fixed parameters. Our algorithm requires [Formula: see text] query complexity, however, it can reduce to [Formula: see text] instead while retaining a low adaptive complexity of [Formula: see text]. Therefore, our algorithm not only achieves the same approximation guarantees as the state of the art but also significantly improves the best-known low adaptive complexity algorithm for the above problem.

Productivity optimization techniques using industrial engineering tools: A review

This review explores productivity optimization techniques through the lens of industrial engineering tools. Industrial engineering serves as a critical discipline in enhancing efficiency and effectiveness across various industries. The abstract delves into methodologies, such as time and motion studies, Six Sigma, Lean principles, and operations research, which are instrumental in streamlining processes and improving productivity. These tools aid in identifying inefficiencies, eliminating waste, and enhancing overall performance. Through a comprehensive analysis of existing literature and case studies, this review highlights the diverse applications and benefits of industrial engineering techniques in optimizing productivity. Additionally, it discusses the integration of advanced technologies, such as automation, data analytics, and artificial intelligence, in modern productivity enhancement strategies. The review concludes with insights into future directions and potential challenges in leveraging industrial engineering tools for sustained productivity improvements in dynamic organizational environments.