Operations Research Research Articles

Foreword for the special issue: “Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts”

We present the scientific results of the call for papers in SEPS titled ’Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts.’ As guest editors in Socio-economic Planning Sciences (SEPS), we describe the domain of our academic initiative and present the collected papers. Interestingly, our collection includes papers employing various methodologies such as multi-criteria decision making, game theory, operations research, microeconomics, computational models and insurance mathematics. We received more than fifty submissions from Asia, Europe and America. However, only thirteen papers were accepted for publication in SEPS. This demonstrates the extensive investigation of the problem of common goods and the ongoing academic debate surrounding it.

Learning to Optimize Contextually Constrained Problems for Real-Time Decision Generation

The topic of learning to solve optimization problems has received interest from both the operations research and machine learning communities. In this paper, we combine ideas from both fields to address the problem of learning to generate decisions to instances of optimization problems with potentially nonlinear or nonconvex constraints where the feasible set varies with contextual features. We propose a novel framework for training a generative model to produce provably optimal decisions by combining interior point methods and adversarial learning, which we further embed within an iterative data generation algorithm. To this end, we first train a classifier to learn feasibility and then train the generative model to produce optimal decisions to an optimization problem using the classifier as a regularizer. We prove that decisions generated by our model satisfy in-sample and out-of-sample optimality guarantees. Furthermore, the learning models are embedded in an active learning loop in which synthetic instances are iteratively added to the training data; this allows us to progressively generate provably tighter optimal decisions. We investigate case studies in portfolio optimization and personalized treatment design, demonstrating that our approach yields advantages over predict-then-optimize and supervised deep learning techniques, respectively. In particular, our framework is more robust to parameter estimation error compared with the predict-then-optimize paradigm and can better adapt to domain shift as compared with supervised learning models. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported in part by the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.03565 .

The safety of modified, all-oral shorter tuberculosis regimens in Armenia.

The implementation of modified, all-oral shorter regimens for treatment of rifampicin-resistant tuberculosis has started in Armenia since August 2020 under the conditions of operational research. This study aims to evaluate the safety and effectiveness of shorter regimens. We evaluated cumulative incidence rates of serious adverse events, adverse events of grade 3 and greater and events resulting in treatment modifications or suspension for 52 study participants. A new, different pattern of adverse events emerged compared with the previous evaluations of drug safety of treatment for rifampicin-resistant tuberculosis. Arthralgia (23.1%) and peripheral neuropathy (21.2%) took leading positions among the adverse events resulting in modifications of treatment. Some adverse events of interest (prolonged QT interval, elevated liver enzymes and anemia) remained relevant for the patients receiving new combinations of anti-TB drugs. The other adverse events (impaired hearing, acute kidney injury and hypokalemia) lost their significance for safety surveillance of rifampicin-resistant tuberculosis treatment. One unexpected serious adverse event (lymphoproliferative skin lesion) brought to a "failed treatment" outcome. The other serious adverse event was anemia. The shorter regimens proved to be safe and effective for treatment of rifampicin-resistant tuberculosis, but proper follow-up of adverse events is necessary.

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set.

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital's level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Incorporating sustainability into product lifecycle management: a systematic literature review

AbstractAmidst environmental, regulatory, and societal pressures, integrating sustainability into Product Lifecycle Management (PLM) is key, evolving into Sustainable PLM (sPLM). This paper uses a systematic literature review and text-mining (C-value method) to categorise sPLM research into clusters, assess their integration at organisational levels, and evaluate the level's maturity. Findings highlight a gap in operational sPLM research. Future studies should bridge the gap between theory and industrial application, enhance sPLM operationalisation, and explore emerging technologies’ impact on sPLM.

Enhancing Hajj Pilgrim Satisfaction: A Strategic Analysis of Service Quality Dimensions using the Analytic Hierarchy Process in Alignment with Saudi Vision 2030

Saudi Arabia every year receives more than two million Muslims from all over the world to perform the Hajj (the Muslim pilgrimage to Makkah). "It must take place during a specific period of the year in a specific and limited space." (“Asma BAHURMOZ | Faculty Member | Prof of Operations Research | King ...”) To manage and satisfy such crowd, Considering the variety of cultures, languages, and different levels of socio-economic background, is next to impossible. Saudi vision 2030 has taken it as its duty to ensure that pilgrimages will have joyful experience and good memories to take back home. Authorities develop four categories of packages based on quality dimensions to meet different budgets. To manage and provide quality service for the pilgrimagesis incredibly challenging task. In an effort from the authors to dir ect their research towards this goal. A multi criteria decision model MCDM is developed to evaluate the service quality dimensions (SQD) and identify essential criteria that achieve pilgrim’s satisfaction and keep up with hajj vision 2030, The aim is to provide an easy tool though a scientific one for companies providing these packages to adjust their services to match pilgrims ' expectations.

Dynamic rebalancing optimization for bike-sharing systems: A modeling framework and empirical comparison

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternative. As a remedy to unbalanced stations, operators typically rebalance bikes by trucks. The resulting dynamic planning has received significant attention from the Operations Research community. Due to its modeling flexibility, mixed-integer programming remains a popular choice. However, the complex planning problem requires significant simplifications to obtain a computationally tractable model. As a result, existing models have used a large variety of modeling assumptions and techniques regarding decision variables and constraints. Unfortunately, the impact of such assumptions on the solutions’ performance in practice remains generally unexplored.In this paper, we first systematically survey the literature on rebalancing problems and their modeling assumptions. We then propose a general mixed-integer programming model for multi-period rebalancing problems that can be easily adapted to different assumptions, including trip modeling, time discretization, trip distribution, and event sequences. We develop an instance generator to synthesize realistic station networks and customer trips, as well as a realistic fine-grained simulator to evaluate the operational performance of rebalancing strategies. Finally, extensive numerical experiments are carried out, both on the synthetic and real-world data, to analyze the effectiveness of various modeling assumptions and techniques. Based on our results, we identify the assumptions that empirically provide the most effective rebalancing strategies in practice. Specifically, a set of specific trip distribution constraints and event sequences ignored in the previous literature seem to provide particularly good results.

How performance measurement systems enable or hinder organizational ambidexterity

PurposeThis study aims to examine how different uses of performance measurement systems (PMS) enable or hinder organizational ambidexterity (OA), intended as the simultaneous pursuit of exploitation and exploration.Design/methodology/approachFollowing a qualitative research design, we gathered data through semi-structured interviews, observations and reviews of documents at four departments of an automotive firm.FindingsWe contribute to operations management research and practice by demonstrating how PMS, which are typically associated with exploitation, can also foster exploration and enable organizations to become ambidextrous. Specifically, we show how PMS can be structured and used in more agile ways and, in relation to innovation, we identify which PM practices should be introduced and with what effects and those that should be avoided. We also contribute to organization theory by highlighting how a single management tool can promote the achievement of both exploration and exploitation.Practical implicationsIn investigating PMS uses and their effects, we identify several positive practices. For example, we show how managers can use PMS more effectively and how targets could be deployed to stimulate creativity and innovation. We also emphasize the need for managers to opt more often for team incentives rather than individual ones to encourage the collaboration needed for OA.Originality/valueWe provide in-depth insight into how PM tools affect an organization’s ability to pursue exploitation and exploration, thus contributing to research in operations, innovation and organization theory.

Supportive supervision of village, health, sanitation and nutrition day (VHSND) during post-pandemic period in Assam

Background: VHSND, a community platform bringing health, early childhood development, nutrition, and sanitation services by community engagement was affected by COVID-19 pandemic. Aims & Objectives: To assess the process of VHSND implementation during post-pandemic period in Assam. Methodology: This cross-sectional study was conducted as operational research in five districts of Assam, selected based on zonal representation; between Sept’2021- Aug’2022. Standard format (GOI) was used for VHSND monitoring. Descriptive analysis was done along with quarter-wise comparison of observations and knowledge of Auxiliary Nurse and Midwife (ANM). Results: A total of 370 VHSND sessions were observed. VHSND micro plan availability increased from 87.5% to 98.4% over four quarters. Displaying banners varied between 56.3% and 79.5%, and functional toilets increased from 41.3% to 77%. Height measurement improved from 58.8% to 88.5%. Haemoglobin estimation varied between 46.3% and 75.4%. Recognition of high-risk pregnancies due to severe anaemia increased from 60% to 91.8%. Regarding danger signs like bleeding from vagina increased from 50.0% to 95.1%. Conclusion: Notable progress could be documented in logistical readiness and ANM knowledge in different quarters in post-pandemic period. While service delivery and ANM counselling efforts have seen improvement, targeted interventions remain crucial for optimizing maternal and child health outcomes in the region.

A Review of Sports Analytics

Abstract: Due to the lack of review papers published in the field of operations research in sports analytics, the central aim of the authors of this paper is to conduct a bibliometric review of publications on the application of Operations research in sports analytics between 2000 and 2022. Through this bibliometric analysis, this paper aims to identify research gaps for future academicians to enable them to work on the topic. The data (130 papers) used for the paper comprises various central as well as interlinked disciplines and topics like sports scheduling, decision-making, and mathematical models used in sports. To present the data cleanly and allow the reader to visualize it, the authors have used various types of different graphs, tables, and figures. The present bibliometric review would be instrumental in helping academicians and students in identifying areas of research as well as helping tournament organizers and team managers, among others, by enumerating various areas that they can use to optimize decision-making in sports further.

Mathematical Optimization in Innovation Productivity: A Framework and A Case Study on UAV Border Patrolling in Türkiye

Purpose: In this paper, the potential of mathematical optimization (MO) in enhancing innovation productivity is explored. Innovation is a process that converts new ideas and methods into products and services, MO can contribute to innovation management by improving productivity across all stages, from pre-innovation to post-innovation. This paper establishes a connection between MO and innovation productivity while demonstrating an application for a post-innovation phase problem of unmanned aerial vehicles (UAVs). Methodology: A framework for incorporating MO into the design problems of innovation processes is developed. Additionally, a MO model is developed for a case study concerning UAV border patrolling in Türkiye. Findings: Computational experiments demonstrate MO's effectiveness in optimizing UAV routes and strategies, enhancing operational efficiency, and innovation productivity. Optimal recommendations and trade-offs among different mission considerations are obtained in 18 minutes on average (with a median of 5 seconds) over 210 runs. Originality: A link is established between MO and innovation productivity. An operations research problem is introduced for UAV operations in border patrolling in Türkiye. The codebase and data are openly provided for readers to apply the model in their research.

Two-Stage Probe-Based Search Optimization Algorithm for the Traveling Salesman Problems

As a classical combinatorial optimization problem, the traveling salesman problem (TSP) has been extensively investigated in the fields of Artificial Intelligence and Operations Research. Due to being NP-complete, it is still rather challenging to solve both effectively and efficiently. Because of its high theoretical significance and wide practical applications, great effort has been undertaken to solve it from the point of view of intelligent search. In this paper, we propose a two-stage probe-based search optimization algorithm for solving both symmetric and asymmetric TSPs through the stages of route development and a self-escape mechanism. Specifically, in the first stage, a reasonable proportion threshold filter of potential basis probes or partial routes is set up at each step during the complete route development process. In this way, the poor basis probes with longer routes are filtered out automatically. Moreover, four local augmentation operators are further employed to improve these potential basis probes at each step. In the second stage, a self-escape mechanism or operation is further implemented on the obtained complete routes to prevent the probe-based search from being trapped in a locally optimal solution. The experimental results on a collection of benchmark TSP datasets demonstrate that our proposed algorithm is more effective than other state-of-the-art optimization algorithms. In fact, it achieves the best-known TSP benchmark solutions in many datasets, while, in certain cases, it even generates solutions that are better than the best-known TSP benchmark solutions.

Optimizing Concrete Paver Block Production: A Linear Programming Approach for Enhanced Profitability in the Construction Industry

As an operational optimization (maximization or minimization) methodology, linear programming (L.P.) in operation research has proven successful in several areas by offering logical outcomes to challenging issues, including resource constraints. The study aimed to bridge the gap between theoretical applications of L.P. and the practical needs of the construction industry, specifically focusing on determining the optimal production quantities of 60 mm and 40 mm concrete paver blocks for Janta Udhog Pvt. Ltd. as a case study to achieve maximum profitability, offering essential new insights into how L.P. may successfully handle the complex problems encountered by businesses in this industry. An interview with the owner of Janta Udhog Pvt. Ltd. was conducted to obtain the primary data for the study. Secondary data such as sale records, monthly demand of each product, and inventory records were also obtained to formulate a linear programming model. The model was then solved using the LINGO software. This research underscores the optimal production recommendations for Janta Udhog Pvt. Ltd., advising the manufacture of 566.67 units of the 60 mm block and 350 units of the 40 mm block, leading to a maximum daily profit of NPR 2966.67. By applying linear programming techniques, these findings contribute to a more informed and strategic decision-making approach in the construction sector. The study illuminates the benefits of utilizing linear programming in challenging situations, providing valuable insights for academics, business owners, and decision-makers.

A method of network attack-defense game and collaborative defense decision-making based on hierarchical multi-agent reinforcement learning

Faced with the challenges of security strategy design brought about by the complexity of attack behavior and the dynamism of network structure, dynamic hierarchical intelligent defense methods have shown their effectiveness. However, in complex network environments, their application requires a higher level of coordination mechanisms. Therefore, this paper proposes a hierarchical multi-agent reinforcement learning network attack and defense game and cooperative defense decision-making method, which autonomously and efficiently completes the formulation of defense strategies and defense behavior responses. Firstly, we construct a Stackelberg hypergame model of cyberspace conflicts, and under the condition of information loss, characterize the multi-layer dynamic defense coordination response mechanism. Secondly, By utilizing a hierarchical multi-agent reinforcement learning method as the driving force for game evolution, we sequentially solve the Nash equilibrium of the game, and form a dynamic autonomous defense strategy. Finally, we construct a hierarchical multi-agent reinforcement learning framework, which decouples the defense decision problem, reduces the dimension of the defense action space and the exploration difficulty of the strategy space, and learns coordinated defense strategies more efficiently. We used the CybORG (Cyber Operations Research Gym) environment for simulation. We compared and analyzed the autonomously generated cyber defense strategies with other related works, verifying the superior coordination performance of our method in defense strategy generation and control.

Registration shutdown of tax invoices / calculations of adjustments: prerequisites and consequences

This article covers the prerequisites and consequences of introducing a mechanism for the registration shutdown of tax invoices / calculations of adjustments. In fact, any innovations related to the administration of taxes and fees in the future have both negative and positive consequences for the state and business. That is why timely identification of negative consequences and shortcomings helps in the operational research and improvement of tax administration mechanisms, in order to increase the level of revenues to the state budget, reduce the level and risk of corruption and tax evasion, increase the level of business activity of businesses, as well as in order to prevent future scaling and generate concomitant negative consequences for the economy and budget of the country as a whole. We have studied current publications and articles of modern scientists and practitioners related to the administration of value-added tax. An assessment of the effectiveness of the current mechanism for the registration shutdown of tax invoices / calculations of adjustments is given. The main problems and incomplete development of the current procedure for the registration shutdown of tax invoices / calculations of adjustments, administrative and judicial appeals against decisions of regulatory authorities are highlighted. The consequences of introducing a mechanism for the registration shutdown of tax invoices / calculating adjustments on the level of economic activity of businesses, the judicial system of Ukraine and the tax system as a whole are studied. Based on the conducted research, we provided recommendations on ways to improve the existing system of value-added tax administration in the context of the mechanism for the registration shutdown of tax invoices / calculations of adjustments. In addition, options were proposed for combining electronic document management systems, tax audit, as additional monitoring tools for the electronic value-added tax administration service.

Reconciling Rigor Versus Relevance: Lessons from Humanitarian Fleet Management

This position paper reframes the ongoing relevance versus rigor debate in operations research (OR) as a Kuhnian epistemological crisis, in which the dominant paradigm of quantitative modeling shows signs of exhaustion. Humanitarian fleet management is presented as an empirical case of extensive operations theory, which has not been implemented by the stakeholders who paid for its production. We propose a possible way out of the crisis by combining “hard” and “soft” OR, illustrating the potential with a selected problem structuring method. Optimization solutions can become more productive by first surfacing the organizational context of decision-making. The illustration emphasizes that hard and soft OR are not binary opposites but interlocking, mutually empowering components which expand the evidence base. Shifting the current paradigm toward more engaged scholarship could counteract the ongoing theoretical drift, for more strategic impact on the pressing problems of today.

A categorization of observed uses of operational research models for fundamental surprise events: Observations from university operations during COVID-19

Operational research (OR) approaches have been increasingly applied to model resilience to surprise events. To model a surprise event, one must understand its characteristics, which then become parameters, decisions, and/or constraints in the model. This means that these models cannot (directly) handle fundamental surprise events, which are events that could not be defined before they happen. However, OR models may be adapted, improvised, or created during a fundamental surprise event, such as the COVID-19 pandemic, to help respond to it. We provide a categorization of observed uses for how OR models were applied by a university in response to the pandemic, thus helping to understand their role during fundamental surprise events. Our categorization includes the following adaptations: adapting data, adding constraints, model switching, pulling from the modeling toolkit, and creating a new model. Each of these adaptations is formally presented, with supporting evidence gathered through interviews with modelers and users involved in the university response to the pandemic.

Management of a post-disaster emergency scenario through unmanned aerial vehicles: Multi-Depot Multi-Trip Vehicle Routing with Total Completion Time Minimization

One of the most valuable and promising applications for Unmanned aerial vehicles (UAVs) is in natural disaster management, where these aircraft can operate autonomously without any need for human intervention during their flights.In this paper, we foster the interface of Operational Research with computer science in general and sensor networking in particular by focusing on managing a post-disaster emergency scenario where the use of a fleet of UAVs helps rescue teams identify people needing help inside an affected area. We model this situation as an original graph theoretical problem called Multi-Depot Multi-Trip Vehicle Routing Problem with Total Completion Time minimization (MDMT-VRP-TCT). The main novelty of the MDMT-VRP-TCT is the combination of the following three features: multi-depot, multi-trip, and completion time minimization.We propose a mixed-integer linear programming (MILP) formulation, develop a matheuristic framework to address large instances, and present an extended set of experiments to test the performance of the proposed matheuristic: first, we compare the matheuristic with the MILP formulation on a set of small instances (up to 30 nodes); then, we compare our matheuristic with two heuristics from networking literature, showing that it outperforms the existing algorithms.

Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats.

Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity. During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats. APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO). The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model. The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats.