Operational Decision-making Research Articles

Handlungsempfehlungen für SARS-CoV-2-Testkonzepte für asymptomatische Beschäftigte im Gesundheitswesen

ZusammenfassungHintergrund und ZielsetzungBei der Umsetzung von SARS-CoV‑2-Testkonzepten in Einrichtungen des Gesundheitswesens kommen ranggleiche Gesetze und Verordnungen zur Anwendung. Vor dem Hintergrund erlebter Hindernisse bei einer adäquaten Umsetzung gesetzlicher Vorgaben in regelkonforme und rechtssichere Prozesse auf betrieblicher Ebene war das Ziel der vorliegenden Arbeit, dafür konkrete Handlungsempfehlungen zu entwickeln.MethodikIn einer Fokusgruppe mit Vertreter*innen aus Behörden, Fachschaften und Interessenvertretungen wurden auf Basis von Leitfragen aus zuvor identifizierten Handlungsfeldern in einem holistischen Ansatz kritische Aspekte der Umsetzung diskutiert. Die transkribierten Inhalte wurden anhand deduktiv-induktiver Kategorienbildung qualitativ analysiert.ErgebnisseAlle Diskussionsinhalte konnten den identifizierten Kategorien Gesetzliche Hintergründe von, Anforderungen an und Zielrichtungen für Testkonzepte in Gesundheitseinrichtungen, Zuständigkeiten für Umsetzung in betrieblichen Entscheidungsketten sowie Implementierung von SARS-CoV-2-Testkonzepten/Testprozessen zugeordnet werden.ImplikationenDie Umsetzung gesetzlicher Vorgaben in regelkonforme und rechtssichere SARS-CoV-2-Testkonzepte in Einrichtungen des Gesundheitswesens setzt bereits sowohl die Einbeziehung von Ministerien, Fachschaftsvertretungen, Berufsverbänden, Arbeitgeber(AG)- und Arbeitnehmer*innenvertretungen, Datenschutzexpert*innen, Vertretungen möglicher Kostenträger als auch eine integrative und vollzugstaugliche Formulierung von Gesetzen und Verordnungen voraus. Die Definition von Zielrichtungen für Testkonzepte ist maßgeblich für nachfolgend im Betrieb etablierte Prozessabläufe, bei denen Aspekte des Beschäftigtendatenschutzes genauso berücksichtigt werden müssen wie die Bereitstellung zusätzlicher personeller Ressourcen für die Bewältigung der Aufgaben. Zudem muss es in Zukunft ein zentrales Anliegen der Betriebe sein, IT-Schnittstellenlösungen für eine datenschutzkonforme Informationsweitergabe zu Beschäftigten zu finden.

Research on Electric Boiler Auxiliary Thermal Power Unit Operation in Isolated Grid

An electric boiler is widely used in the research and application of thermal power flexibility transformation because of its fast regulation and strong energy storage capacity. However, as an electrical load with high flexibility, the value of isolated operation of auxiliary units in case of grid line failures has not been fully recognized. This paper studies the unit load balance system, unit isolated network operation decision-making, unit load switching, steam turbine control, and boiler control of live boiler operation, so as to realize the control function of an electric boiler assisting the isolated network operation of the thermal power unit, ensure that the unit can stably turn into the isolated network operation state in case of grid failure, and effectively ensure the heating of residents and the rapid restoration of grid power supply.

Study on fusion clustering and improved YOLOv5 algorithm based on multiple occlusion of Camellia oleifera fruit

The detection method of Camellia oleifera fruits is not only an essential part of the Camellia oleifera fruit detection and locating process, but also a key foundation of the vision robot harvesting. Due to the occlusions of branches and leaves, as well as the overlap of fruits, it is adaunting taskto improve the precision of Camellia oleifera fruit detection in a natural environment. In this paper, we propose a YOLO-COF lightweight multiclass occlusion target detection method for Camellia oleifera fruit, introducing K-means++ clustering algorithm under the framework of YOLOv5s to automatically filter and select the target dataset; incorporating Coordinate Attention to enhance the feature extraction ability of occluded target and improve the generalizability and robustness of the model. The study assesses four categories of datasets: complete fruit occlusion NO, fruit occluded by leaf OL, fruit occluded by branches OB, and fruit occluded by other fruits OF, combining the new detection model and comparing with five Faster R-CNN, YOLOv4-MobileNetV3, YOLOv4-CSPDarknet, YOLOv5s, and YOLOv5x models. The experimental results indicate that the AP values of YOLO-COF for the detection of NO, OL, OB, and OF are 0.97, 0.94, 0.93, and 0.92, respectively. In comparison to other neural network models, YOLO-COF is well-balanced in terms of precision, speed, and size. Its mAP value is 94.10%, its frame rate is 74.8FPS, and its model size is 27.1 MB. The proposed model has the potential to facilitate the operational decision-making of vision robot during the process of Camellia oleifera fruit harvesting.

Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis

In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making.

A Risk-Based Framework to Improve a Distribution System’s Resilience against Earthquakes

Earthquakes and other natural disasters have caused significant damages to power systems, indicating the necessity to enhance power system resilience. This paper proposes a risk-based resilience enhancement framework against earthquakes that considers their stochastic nature. The proposed framework supports the decision-making of distribution system operators (DSOs) for retrofitting substation components and underground cables to enhance distribution system resilience. The framework consists of four phases, namely earthquake modeling, vulnerability assessment, risk assessment, and resilience enhancement. An attenuation relationship is used to model the earthquake characteristics. Vulnerability assessment includes the failure probability calculation of substation components and a fault-tree method application to examine the seismic vulnerability of the substation. Conditional value at risk (CVaR) is used to assess the seismic risk, and includes the estimation of repair cost and customer interruption cost due to the damage caused by the earthquake, as well as the power generation cost of the distributed energy resources (DERs) used to meet the demand locally. A modified risk reduction worth (RRW) metric is adopted to determine the optimal retrofitting strategy to enhance resilience. The proposed framework was applied to a real distribution substation to examine the effectiveness of substation component retrofitting for resilience enhancement.

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. The results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage

Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.

Bisecting for Selecting: Using a Laplacian Eigenmaps Clustering Approach to Create the New European Football Super League

Ranking sports teams generally relies on supervised techniques, requiring either prior knowledge or arbitrary metrics. In this paper, we offer a purely unsupervised technique. We apply this to operational decision-making, specifically, the controversial European Super League for association football, demonstrating how this approach can select dominant teams to form the new league. We first use random forest regression to select important variables predicting goal difference, which we use to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisect the Fiedler vector to identify the natural clusters in five major European football leagues. Our results show how an unsupervised approach could identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify teams that dominate their respective leagues and are the best candidates to create the most competitive elite super league.

Rapid seismic fragility curves assessment of eccentrically braced frames through an output-only nonmodel-based procedure and machine learning techniques

A nonmodel and output-only-based framework incorporating an Auto-Regressive model and roof absolute acceleration response are hired in this study to compute a robust wavelet-based damage-sensitive feature (rDSF) and estimate engineering demand parameters (EDPs) of buildings with eccentrically braced frames (EBFs). A large database is recruited to estimate the peak and residual story drift ratios and peak floor absolute accelerations as EDPs. The database comprises cmorfb-fc wavelet-based rDSF, number of stories, building heights, and EDPs of the 4- and 8-story EBF archetypes, resulting from 44 far-field ground motions through incremental dynamic analysis. The capability of both the closed-form equations and machine learning (ML) techniques is investigated to estimate the EDPs. The predictive equations are constructed using ordinary least squares linear regression, Symbolic and Bayesian regressions. To have a more accurate estimation of EDPs, 12 renowned ML-based regression algorithms are also implemented for training, validation, and test datasets. Sensitivity analysis using box-whisker plots on the validation dataset shows that Extreme Gradient Boosted Trees (ExGBT) ensemble has a remarkable efficiency to predict the EDPs and leads to the highest 10-fold correlation coefficient and the lowest root-mean-square error. Moreover, ExGBT has the lowest median absolute relative deviation in different damage states and consequently is selected as the best surrogate algorithm. The predicted EDPs can be further exploited to map rDSF-based fragility curves, a conditional probability at each defined damage state given the value of rDSF. The comparative results for the test data approve that the predicted fragility curves approximately match with those obtained from observed data and can be used for early decision-making in emergency operations.

Online-to-Offline Platforms: Examining the Effects of Demand-Side Usage on Supply-Side Decisions

This study explores the impact of demand-side usage of digital platforms on operational decision-making by supply-side firms. The positive impact of digital platforms on product sales is established in the literature. However, this study focuses on the perspective of supply-side firms, examining the effect of increasing reliance on digital platforms on supply-side firms’ decisions. The study shows that greater demand-side usage of online-to-offline (O2O) digital platforms is associated with more efficient operational decisions of supply-side firms. Benefits of leveraging O2O platforms include information transparency and resource pooling. The study further examines the extent to which benefits from participating in O2O digital platforms are affected by inter-firm relationships in which a supply-side firm is embedded. The study uses a panel data model to test hypotheses with data on China's movie theaters that join O2O platforms. It found that movie theaters with greater demand-side usage of O2O digital platforms see better resource utilization and product concentration. Furthermore, it shows that more vertically integrated firms can better reap the benefits from demand-side usage of digital platforms. In contrast, more horizontally integrated firms can less strategically leverage the benefits from demand-side usage of O2O platforms.

A protocol for analysing the role of shared situational awareness and decision-making in cooperative disaster simulations

Acute environmental emergencies and disasters call for multi-organisational collaboration where each individual response agency has their own responsibilities and specific roles in an emergency operation. However, different operational systems, cultures and norms between the response agencies can hamper such collaboration and the formation of shared situational awareness (SA). SA has an impact on how an individual response agency understands the situation and therefore forms the basis for decision-making in emergency and disaster management. The premise of this paper is that formation of shared SA is a key to improving preparedness to deal with disasters. The effective formation can be facilitated by creating shared understanding of how disaster management should be implemented. We developed a protocol for analysing the creation of shared SA and its significance for successful operational decision-making during multi-agent disaster simulations. As a starting point, based on a literature review, we built a systemic framework to represent the process of acute operational decision-making, describing particularly the role of the shared SA creation as part of the process. Based on the framework, we then developed a protocol for monitoring the formation and analysing the magnitude of shared SA, how it is linked to the decision making process and the execution of the actions during the exercises. We suggest the protocol can be applied in various emergency and disaster preparedness contexts to support the development and harmonisation of multi-agent and multi-organisational training and future crisis response practises.

Big Data Analytics Applications in Information Management Driving Operational Efficiencies and Decision-Making: Mapping the Field of Knowledge with Bibliometric Analysis Using R

Organizations may examine both past and present data with the aid of information management, giving them access to all the knowledge they need to make sound strategic choices. For the majority of contemporary enterprises, using data to make relevant, valid, and timely choices has become a must for success. The volume and format of data have changed significantly over the past few years as a result of the development of new technologies and applications, but there are also impressive possibilities for their analysis and processing. This study offers a bibliometric analysis of 650 publications written by 1977 academics on the use of information management and big data analytics. The Bibliometrix function in the R package and VOSviewer program were used to obtain the bibliographic data from the Scopus database and to analyze it. Based on citation analysis criteria, the top research journals, authors, and organizations were identified. The cooperation network at the author level reveals the connections between academics throughout the world, and Multiple Correspondence Analysis (MCA) identifies the research holes in the area. The recommendations for further study are influenced by the findings.

The effect of supply chain finance on sustainability performance: empirical analysis and fsQCA

Purpose In the context of global economic downturn and intense competition, firms are increasingly resorting to supply chains to acquire capital support and achieve sustainability. This study aims to investigate the effect of supply chain finance (SCF) on corporate sustainability performance (CSP) and identifies SCF-related recipes for CSP. Design/methodology/approach Based on a sample of 1,038 firms that disclose CSP – namely, corporate financial performance (CFP) and environmental, social and governance performance (ESGP) – the authors use a quasi-replication method consisting of empirical analysis with fuzzy-set qualitative comparative analysis (fsQCA) to investigate SCF’s effects on CSP. Findings The authors find that SCF has a “doing well by doing good” effect on CSP. CFP can promote the positive effect of SCF and ESGP while ESGP’s positive effect on SCF and CFP is nonsignificant. In addition, heterogeneity tests show that SCF’s promoting effect on CSP is affected by high-low CFP and ESGP. The fsQCA results verify the empirical findings and reveal five SCF-related recipes for achieving high CSP. Research limitations/implications This study has the following two limitations. First, we do not consider how SCF affects CSP in different industries. There is a need to investigate whether industry heterogeneity changes SCF’s effects on CSP, especially in prominent industries, such as the energy industry, with its high susceptibility to ESGP, and the manufacturing industry, with its extensive application of SCF. It will be important to investigate these industries to better understand SCF’s role in sustainability. Second, we study the secondary supply chain – namely, core firm–suppliers and core firm–customers. The authors do not consider financial institutions (e.g. banks and guarantee institutions). SCF modes that include the participation of financial institutions, such as factoring financing and reverse factoring financing, cater more to the capital needs of diversified firms. In the future, studying specific industries that have made significant contributions to the application of SCF along with others that are more sensitive to environmental governance could better highlight the effect of SCF on sustainability and help supply chain managers understand the application value of SCF. Future research could also extend SCF participants into multiple roles to explore separate effects. Tracking financing demanders, fund providers and credit guarantors could capture SCF characteristics more comprehensively. Methodologically, it will be challenging to accurately measure SCF networks in terms of quantification. In future work, this could be performed with the help of artificial intelligence. Practical implications First, our findings indicate that SCF has a “doing well by doing good” effect on core firms. SCF can not only overcome the capital shortage of SMEs but also provide significant benefits to core firms. Second, our findings provide SCF-related recipes to help firms fulfil ESGP obligations without sacrificing CFP under the pressure to “do good.” The authors provide valuable insights and diverse recommendations to help supply chain managers, marketing executives and researchers adjust supply chain management strategies. Third, this work can guide executives in various fields to adopt SCF to achieve sustainability as a risk-mitigation strategy by means of marketing. Originality/value This study identifies better, more straightforward SCF-related recipes for CSP (consisting of CFP and ESGP) using a quasi-replication analysis that improves upon conventional methods such as regression analysis, which have limited power. The authors provide valuable insights and diverse recommendations to help managers pursue sustainable development. The findings point to practical guidelines and feasible solutions that can support well-founded operational strategic and management decision-making, which can enhance a firm’s competitiveness under uncertainty and a sluggish economy.

The Construction and Application of Dual-Objective Optimal Speed Model of Liners in a Changing Climate: Taking Yang Ming Route as an Example

In a changing climate, ship speed optimization plays an important role in energy conservation and emission reduction. In order to establish a dual-objective optimization model of minimizing ship operating costs and reducing carbon emissions, fuel costs, berthing costs, emission costs and fixed cost during sailing cycles, the emission reduction strategies of ships using MGO in emission control areas and the AMP in ports are taken into account. The PSO algorithm is adopted to find the Pareto solution set, and the TOPSIS algorithm is used to screen the optimal compromise solution, while Yang Ming, a trans-Pacific route, is selected to verify the applicability of the model. The result shows that the optimization model can effectively reduce the operating cost during sailing cycles and control carbon emissions, which can provide references for ship operation decision-making to achieve carbon peaking and carbon neutrality.

Nonlinear mathematical model of an economic soliton

In modern conditions of economic transformation, the functioning and development of production systems is characterized by instability, nonlinearity and dynamism of the main indicators and parameters. The financial and production system is affected by a large number of market, inflationary, social and other disturbances, which leads to the losses in financial stability, a decrease in production volumes, a decrease in demand for manufactured products, etc. Therefore, it is necessary to have an effective management system and operational decision-making. The relevant direction for solving this problem is the development of a flexible intelligent control system that makes it possible to diagnose quickly the unfavorable state of the financial and production system and activate promptly the mechanisms that return the system to equilibrium. In this case, information becomes the main resource of management, which performs an integrating role and is a necessary component when using all other resources. All this predetermines the use of information technology management. With a high dynamism and instability of the external environment, it becomes necessary to apply the methods and approaches of the new science of management, concentrating its attention on the theory of complex systems and nonlinear dynamics, with the help of which complex control systems can cope effectively with uncertainty and rapid changes. The development and use of modern information technologies for enterprise management are based on the introduction of integrated information processing technology and the creation of mathematical methods and computer modeling tools. In the work of information technologies, management is implemented by creating an integrated intelligent computerized system based on the use of economic and mathematical methods, computer technology and communication means, i.e. implements a fundamentally new management platform, which is based on the integration of management information through a mechanism for generalizing the information database and knowledge.

Machine-Learning Methods to Identify Key Predictors of Site-Specific Vineyard Yield and Vine Size

<h3>Background and goals</h3> Lake Erie Concord growers have access to high-resolution spatial soil and production data but lack protocols and information on the optimum time to collect these data. This study intends to provide clearer information regarding the type and timing of sensor information to support in-season management. <h3>Methods and key findings</h3> A three-year study in a 2.6 ha vineyard collected yield, pruning mass, canopy vigor and soil data, including yield and pruning mass from the previous year, at 321 sites. Stepwise linear regression and random forest regression approaches were used to model site-specific yield and pruning mass using spatial historical production data, multi-temporal in-season canopy vigor and soil data. The more complex yield elaboration process was best modelled with non-linear random forest regression while the simpler development of pruning mass was best modelled by linear regression. <h3>Conclusions and significance</h3> Canopy vigor in the weeks preceding bloom was the most important predictor of the current season’s yield and should be used to generate stratified sampling designs for crop estimation at 30 days after bloom. In contrast, pruning mass was not well predicted by canopy vigor, even late-season canopy vigor, which is widely advocated for pruning mass estimation in viticulture. The previous year’s pruning mass was the dominant predictor of pruning mass in the current season. To model pruning mass going forward, the best approach is to start measuring it. Further work is still needed to develop robust, local site-specific yield and pruning mass models for operational decision-making in concord vineyards.

THE APPLICATION OF TIME-SERIES FORECASTING MODELS IN GROCERY RETAIL INDUSTRY

The grocery retail landscape in Ukraine has witnessed profound transformations, driven by disruptions like the COVID-19 pandemic and full-scale invasion, leading to unstable consumer behavior and market dynamics. In response, forecasting models must evolve to consider stochastic exogenous factors, such as blackout periods and air alarms. This study explores advanced time series forecasting models and proposes a comprehensive framework for optimal model selection. The study introduces the Neural Prophet, a model that combines interpretability and predictive power by incorporating components like non-periodic trends, periodic seasonality, holiday effects, and regressors. The research methodology involves a comparative analysis of classical time series forecasting methods, machine learning regression approaches, and neural networks. Noteworthy models include LightGBM, RNN, TCN, and Neural Hierarchical Interpolation for Time Series (N-HiTS). Optuna hyperparameter optimization and k-fold cross-validation enhance model accuracy. The study applies the proposed framework to forecast order quantities in the e-commerce segment of the Ukrainian grocery retail company. The system accommodates diverse factors like weather, holidays, and promotions, providing robust decision support. Anomalies are detected using the IQR method, and missing values are filled using Exponentially Weighted Moving Average. Results show the Neural Prophet consistently outperforming other models in 65% of cases, emphasizing its superiority. However, a complete transition to neural models results in reduced accuracy, highlighting the need for a nuanced approach based on data characteristics. The study presents a sophisticated framework for forecasting accuracy, supporting effective operational decision-making. Future research should explore ensemble methods while maintaining computational efficiency, aligning with the ongoing pursuit of optimized forecasting accuracy for informed decision-making in grocery retail.

Peculiarities of Evaluating the Effectiveness of Operational Improvements

The article is devoted to a review of approaches to the formation of a system for evaluating the effectiveness of operational improvements of the enterprise. The authors suggested evaluating the effectiveness of operational improvements as a result of operational decision-making. The article examines the essence of the concept of «operational decisions», considers the main types of operational decisions and methods of their evaluation at enterprises. A generalized system of indicators for evaluating the effectiveness of operational decisions. It was determined that the main goal of such a system is to ensure management efficiency and help make effective management decisions to achieve the planned goals of the enterprise. It is noted that in order to ensure the effectiveness of the adopted operational improvements, the enterprise should establish cooperation and communication, improve the data collection and analysis system as a guarantee of adopting a high-quality operational decision. It has been proven that for this it is expedient to use modern information technologies, digitization tools for analysis, modeling and data processing, data visualization tools and their forecasting. The scientific novelty of this article lies in the fact that the recommendations and conclusions provided by the authors can be useful for enterprises that wish to improve their own decision-making systems, review the current decision-making evaluation criteria for the implementation of operational improvements.

The model of financial support for the sustainable development of agriculture

The article is devoted to the importance of implementing methodological approaches and methods of evaluating an economic system effectiveness in the field of finance to optimize operational decision-making based on modeling. Expert tools are considered, including: databases and knowledge, systems for modeling economic situations, intelligent decision-making systems. A simulation model has been built that covers the full range of relationships between blocks and indicators-factors that determine the essential characteristics of the economic development of the territory, which made it possible to find out the proportional dependence of the subsystems of financial support for agriculture development.

A dual-channel secondary closed-loop supply chain considering retail groups and fairness concerns.

The rise of retail groups has strengthened their voice in the supply chain, drawing more attention from supply chain members to the issue of profit fairness. To explore the influence of fairness concerns on operational decisions in closed-loop supply chains after the formation of retail groups. In this paper, we first construct a secondary dual-channel closed-loop supply chain led by a retail group and followed by a manufacturer. Next, the corresponding game models are constructed under three scenarios, namely, fairness neutrality (FN), fairness concerns of the retail groups (FR), and fairness concerns of the manufacturer (FM), respectively. Finally, the game models are solved and analyzed. It turns out that it is easier for the manufacturer to satisfy its demands for fairness by adjusting the wholesale price. Furthermore, we find that fairness concerns do not enhance the recycling rate of used products and the greenness of remanufacturing. For retail groups, fairness concerns can hurt their profits, but appropriate fairness concerns can contribute to profitable growth in their retail business. Interestingly, the manufacturer's fairness concerns do not affect the total profitability of the supply chain system, but the retail group's fairness concerns do. This paper identifies dual changes in the scope of operations and power structure of retail groups in closed-loop supply chains, as well as analyzes the fairness concerns raised by these changes, which will lead to new recommendations for operational decision-making in firms.