Operational Decision Support Research Articles

A simulation-based optimization approach for the recharging scheduling problem of electric buses

This study proposes a simulation-based optimization approach to address the recharging scheduling problem of electric buses to minimize charging waiting time. Poor scheduling could lead to longer waiting times and potentially affect operation schedules regarding time and service quality. This study addresses a simulation-based optimization framework to evaluate various performance metrics during electric bus service, including waiting times, charging costs, and the utilization of charging piles. In this study, we propose a hybrid approach, simplified swarm optimization (SSO), which is an evolutionary algorithm with a backtracking (BT) mechanism and dynamic charging in a simulation framework. Based on the dynamic charging, SSO is used to determine the additional charging in terms of battery capacities, and a BT mechanism is employed to enhance algorithm efficiency and achieve breakthroughs in solution quality. A case study from Taiwan with 43 generated datasets was conducted in deterministic and stochastic situations to compare the effectiveness and efficiency among three charging rules (i.e., full charging rule, flexible charging rule, dynamic charging rule) and two algorithms (i.e., particle swarm optimization and SSO) The results indicate the superior performance in all scenarios by using a statistical test, which offers effective decision support for bus operators’ electric bus recharging scheduling.

Health status assessment of pump station units based on spatio-temporal fusion and uncertainty information

An effective health status assessment (HSA) for pump station units (PSUs) is crucial for accurately determining their real status and providing technical support for safe operational decisions. Due to the limitations of existing data-driven HSA methods, which primarily focus on the temporal dependencies of monitoring signals and fail to explore the complex interconnections among signals comprehensively. Moreover, when constructing performance degradation indices based on linear differences, these methods do not effectively integrate heterogeneous signals, resulting in an incomplete and inaccurate assessment of the overall system degradation. This paper proposes a real-time comprehensive HSA method for PSUs based on multi-source heterogeneous uncertainty information. Initially, a health benchmark model (HBM) is built using CrossGNN, which possesses cross-scale and cross-variable interaction capabilities, to precisely capture the temporal dependencies and dynamic relationships among variables in monitoring signals. Subsequently, key measurement points that reflect the operational status of the PSUs are identified through correlation analysis to establish multi-source evaluation indices. Then, considering the uncertainty in signal changes, a novel health degradation index (HDI) is developed using Mahalanobis distance (MD) and the Gaussian Cloud Model (GCM) to analyze changes in unit status. Furthermore, a weighting calculation method based on the non-dominated sorting genetic algorithm (NSGA-II) is proposed to establish a real-time comprehensive health index (RCHDI) for a thorough assessment of PSUs status. Finally, the effectiveness of the proposed method is validated through a case study using data from a pump station in the South-to-North Water Diversion Project in China. The results show that, compared to other studies, the proposed method significantly improves the stability and smoothness of the state assessment curve, with increases of 21.5% and 47.1% respectively, providing a new perspective for comprehensively assessing the health status of PSUs.V

Artificial Intelligence in the Organization of Nursing Care: A Scoping Review.

The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest-Dissertations and Theses to search gray literature. Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Использование нечеткого метода распознавания образов для мониторинга запыленных помещений

ABSTRACT This article discusses a decision support system (DSS) based on fuzzy methods used to identify and process gas dynamic images obtained during monitoring and control of underground coal mine atmosphere processes. DSS supports ventilation operator solutions in complex and unforeseen gas dynamic situations. The work will analyze the quantitative and qualitative information used by the ventilation operator to make decisions. Based on these data, an analysis of the effectiveness of using fuzzy models and methods for interpreting and processing gas dynamic images will be carried out. Fuzzy models and methods of interpretation and processing of gas dynamic images will be proposed. In the future, based on these models and methods, the operator's decision support software will be developed.

Cyclic mining of alarm and operator action events for enhanced process operation

This paper addresses the challenge of fault propagation in industrial facilities, where a fault in one process variable can lead to cascading faults in other variables. As a result, the propagation of alarms corresponding to these faulty variables occurs, leading operators to potentially receive an excessive number of alarm notifications that could significantly impact their decision-making capabilities. To address this issue, a systematic method is proposed to investigate potential fault propagation paths to provide decision support in response to alarm notifications, in order to minimize industrial process failures. The contributions of this paper are twofold. Firstly, it involves enhanced dependency analysis that captures dependent alarms and identifies both weak and strong dependencies among alarm variables using historical alarm and event (A&E) logs that are generated by industrial control systems. Secondly, it offers comprehensive visualization of fault propagation in response to single and multiple alarms, including extracting crucial timing information, identifying the shortest, longest, and critical paths, and determining effective operator actions. The proposed method is designed to enhance process operation and provide essential decision support for industrial operators. The effectiveness of the proposed approach is validated through a case study using real industrial data.

Optimization for route selection under the integration of dispatching and control at the railway station: A 0‐1 programming model and a two‐stage solution algorithm

AbstractAt present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm.

TANTANGAN DAN SOLUSI PENERAPAN SISTEM INFORMASI AKUNTANSI DI INDONESIA: SEBUAH ANALISIS TEMATIK

This research aims to investigate the benefits, obstacles and recommendations for implementing Accounting Information Systems (AIS) in Indonesia. The research method used is qualitative, with data sources from scientific documents such as journals and thematic analysis using NVivo 12 Plus software. This study outlines some of the critical benefits of AIS, including improved operational efficiency, data accuracy, transparency, decision support, and regulatory compliance. However, various barriers were also identified, such as limited technological infrastructure, changes in organizational culture and processes, data security, lack of management understanding and support, and implementation and training costs. Recommendations to overcome these barriers include increasing understanding and awareness of AIS, evaluating technology infrastructure, employee training, and collaboration with professional service providers. These findings highlight the need for better awareness of the benefits of AIS, improved technological infrastructure, employee training, and management support as critical factors in the successful implementation of AIS in Indonesia. Further research is recommended to conduct field studies through interviews and observations to gain deeper insight into the challenges and needs in implementing AIS in Indonesia.

Linking digital leadership and employee digital performance in SMEs in China: The chain-mediating role of high-involvement human resource management practice and employee dynamic capability

BackgroundIn the digital era, the role of digital leadership is becoming increasingly prominent. However, the exploration of the alignment between digital leadership and high-involvement human resource management practices (HI-HRMPs) in influencing employee-based resources remains an overlooked research area among current scholars. ObjectiveThis study aims to analyze the influence of these elements on the digital performance of employees. Based on the Resource-Based View (RBV) and Dynamic Capability Theory (DCT), this research focuses on Chinese Small and Medium Enterprises (SMEs) and employs purposive sampling to select four representative regions in China (Shanghai, Guangzhou, Guizhou, and Anhui). The survey targeted employees within SMEs and was conducted using questionnaire surveys. Structural equation modeling (AMOS) is utilized for analysis. ResultsThe findings indicate that: (1) digital leadership cannot directly influence employee digital performance and employee dynamic capability but can directly influence HI-HRMP; (2) HI-HRMP mediate the influence of digital leadership on employee digital performance, but employee dynamic capability cannot mediate the influence of digital leadership on employee digital performance; (3) HI-HRMP and employee dynamic capability play a chain mediating role between digital leadership and employee digital performance. ConclusionIn the digital market, digital leadership improves employee digital performance by boosting HI-HRMP and enhancing employee dynamic capabilities. The findings support studies on practical applications and link between digital leadership and employee digital performance. The uniqueness of this study lies in applying HI-HRMP and employee dynamic capabilities as chain mediating variables in the digital market. This approach extends the application of HRMPs and dynamic capability theory, providing theoretical guidance and decision support for the digital operations of SMEs.

Application of the NCAR FastEddy® Microscale Model to a Lake Breeze Front

This study investigates how urban environments influence boundary layer processes during the passage of a Great Salt Lake breeze using a multi-scale modeling system, NCAR’s WRF-Coupled GPU-accelerated FastEddy® (FE) model. Motivated by the need for sub-10 m scale decision support tools for uncrewed aerial systems (UAS), the FE model was used to simulate turbulent flows around urban structures at 5 m horizontal resolution with a 9 km × 9 km domain centered on the Salt Lake City International Airport. FE was one-way nested within a 1 km resolution Weather Research and Forecasting (WRF) domain spanning 400 × 400 km. Focused on the late morning lake breeze on 3 June 2022, an FE simulation was compared with WRF outputs and validated using surface and radar observations. The FE simulation revealed low sensible heat flux and cool near-surface temperatures, attributed to a relatively low specification of thermal roughness suitable for previously tested FE applications. Lake breeze characteristics were minimally affected, as FE effectively resolved interactions between the lake breeze and urban-induced turbulent eddies, providing insights into fine-scale boundary layer processes. FE’s GPU acceleration ensured efficient simulations, underscoring its potential for aiding decision support in UAS operations in complex urban environments.

NLP-Based Decision Support System for PUV Management: Enhancing Services Through Passenger Feedback Analysis

This paper presents the development and evaluation of an NLP-Based Decision Support System for Public Utility Vehicle (PUV) Management, focusing on enhancing services through passenger feedback analysis. The study addresses the need for improved public transportation quality in the Philippines, where PUVs play a vital role in daily commuting. The proposed system aims to digitize the passenger feedback process and utilize Natural Language Processing (NLP) techniques to provide decision support for PUV operators. Methodologically, the study follows a phased approach based on the System Development Life Cycle (SDLC). Developed primarily using PHP as the programming language, with Vanilla JS, Bootstrap, and jQuery for backend development and frontend technologies including HTML5, CSS3, and JavaScript (ES6) for creating an interactive user interface, the system caters to the needs of its different users. The evaluation process incorporates the ISO 25010 standard for evaluating systems, and with the participation of 43 respondents from the province of Iloilo, the evaluation results revealed an overall mean score of 4.34, indicating an "Excellent" level of system functionality, reliability, portability, usability, performance efficiency, security, compatibility, and maintainability. This implies that PUV operators and management can use the developed system.

Study on Pricing and Replenishment of Vegetable Commodities based on TOPSIS and ARIMA Models

This study focuses on sales forecasting and pricing optimization of vegetable categories in fresh produce supermarkets. By analyzing the sales data, correlation analysis and clustering methods were used to reveal the correlation patterns among different vegetable categories. The ARIMA model was used to predict the daily replenishment quantity, and TOPSIS and entropy weighting methods were combined to screen out 27 high-quality individual products with high sales volume and stable price to achieve the maximum profit target. The study provides effective sales strategies and operational decision support for fresh food supermarkets, which has practical significance and reference value.

From Data to Action: A Graph-Based Approach for Decision Support in Civil Protection Operations Planning

In the face of increasing frequency and severity of crises such as natural disasters, pandemics, and geopolitical conflicts, civil protection organizations are crucial for recovery and support for affected populations. However, the efficiency and effectiveness of these organizations in crisis response are often hindered by the manual generation of operation plans, characterized by cognitive overload and limited analytic overview. Existing systems focus on detecting crises or post-crisis analysis, overlooking proactive planning. We present GRETA, a graph-based operational planning approach utilizing semantic historical data for better decision support. GRETA uses Operational Scenario Patterns to model operations, mapping them onto a knowledge graph in JSON-LD format, thus creating a structured representation of past data to improve future crisis response planning. We tested GRETA with Germany's Federal Agency for Technical Relief, analyzing over 157,450 historic operations from 2012-2022. Results show GRETA enhances plan efficiency, accuracy, and comprehensiveness, aiding especially inexperienced planners.

Enhancing the resilience of combat system-of-systems under continuous attacks: Novel index and reinforcement learning-based protection optimization

Combat system-of-systems (CSoS) comprises diverse combat entities with distinct functions designed to meet future task requirements, and it serves as a central component of military reform. Enhancing the resilience of CSoS is of significant military value for optimizing combat architecture, ensuring military operational capabilities and improving military survivability. This paper proposes an integrated framework called CSoS capability resilience (CSoSCR) framework to enhance the resilience of the CSoS under continuous attacks. Specifically, a continuous attacks resilience index (CARI) is proposed to evaluate the resilience of the CSoS under continuous attacks considering the task baseline, adaptability, recovery probability, and continuous attacks characteristics. Based on this setup, focusing on the degenerate phase, a resilience-oriented protection optimization method based on reinforcement learning (PORL) is presented to identify nodes requiring protecting and then enhance the resilience of CSoS. Finally, the reliability, effectiveness, and superiority of the CSoSCR framework are tested through a comprehensive case study. The results offer valuable insights for operational guidance and decision support in designing more resilient CSoS.

Prediction of cutterhead torque change trend of shield machine based on partial state visible HMM and LSTM

Cutterhead torque is a key parameter to determine the normal operation of shield machine. Accurately predicting the change trend of cutterhead torque can provide decision support for shield operators to control operating parameters. Therefore, a prediction method of cutterhead torque change trend based on partial state visible Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) network is proposed in this paper. First, the Spearman correlation coefficient is introduced to analyze the correlation of the tunneling parameters, and the historical tunneling data of the cutter torque, pitch angle and related parameters are obtained to establish a database. Secondly, the pitch angle prediction model is established based on LSTM network, and the predicted pitch angle is used as the observation sequence, and the implicit relationship between cutterhead torque and pitch angle is mined through HMM to obtain the probability matrix. Finally, the cutterhead torque prediction model is established, and the pitch angle sequence is used as input to output the corresponding cutterhead torque change trend. The results show that the method can accurately predict the dynamic change trend of the cutterhead torque in the case of less known parameter states, and the effectiveness of the method are verified in this paper.

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

A Semi-Supervised Failure Knowledge Graph Construction Method for Decision Support in Operations and Maintenance

Uncertainty-aware deep learning for reliable health monitoring in safety-critical energy systems

In recent years, significant advancements in deep learning technology have facilitated the development of intelligent health monitoring approaches for energy systems. However, when dealing with safety-critical energy systems, such as nuclear energy systems, conventional deep learning models with point estimation fail to account for the inherent uncertainty in the predictions. This limitation poses challenges for providing reliable and trustworthy decision support for critical operations. To overcome this challenge, this study proposes a novel intelligent monitoring approach that integrates uncertainty-aware deep neural networks. Firstly, a spatio-temporal state matrix-based signal preprocessing method is proposed to enhance feature extraction capabilities, enabling the effective integration of diverse multi-source data. Secondly, a probabilistic distribution is developed to generate predictive uncertainty for all network parameters, enabling the assessment of the confidence of the model’s outputs not only for known operation scenarios but also for unknown scenarios. Finally, the experiments are conducted using an established advanced nuclear energy research platform and a public nuclear accident simulation platform, ensuring the effectiveness and applicability of the proposed approach in practical settings. Overall, the proposed approach significantly enhances the reliability and trustworthiness of the monitoring outputs while mitigating the risks associated with the decision-making process in safety-critical energy systems.

Human operator decision support for highly transient industrial processes: a reinforcement learning approach

Human operator decision support for highly transient industrial processes: a reinforcement learning approach

CO2 flow modeling in a coupled wellbore and aquifer system: Details of pressure, temperature, and dry-out

In order to understand the details of thermal and hydrologic processes attending CO2 injection into a deep aquifer in the context of Carbon Capture and Storage (CCS), we have carried out coupled well-reservoir simulations of CO2 injection using the simulator T2WELL-ECO2M. We focus on the injection of cold, dry CO2 into a warm aquifer and analyze in detail the thermal and hydraulic processes of the coupled well-reservoir system. The results demonstrate the effectiveness of T2WELL in accurately modeling non-isothermal, multiphase flow, phase changes, and identifying dry-out regions in porous media.We simulated heat exchange with the ambient environment, friction effects, convection, exothermic dissolution in brine, and cooling due to both Joule-Thomson effect and water vaporization. The temperature profile within the wellbore deviated from the geothermal profile, impacting CO2 properties at the bottomhole. The simulation revealed the presence of three fronts in the formation: the CO2 saturation, thermal, and evaporation fronts. The thermal and evaporation fronts were located farther behind the saturation front, indicating limited dry-out and thermal effects near the wellbore.This simulation capability and insights gained in this study form a foundation for ongoing work such as sensitivity analyses, injection optimization, performance assessment, and operational decision support.

Research on Sentiment Analysis of E-commerce Live Comments based on Text Mining

With the rapid rise of live e-commerce in the field of e-commerce, the rich emotional information generated by users in live comments has become an important research object. This study takes e-commerce live comments as samples, uses text mining technology combined with machine learning methods, builds sentiment dictionary, trains sentiment analysis model and uses SnowNLP library for sentiment analysis, and deeply digs users' emotional tendency towards goods, services and shopping experience. For negative comments, word cloud is generated through word segmentation and word frequency calculation to visually show users' dissatisfaction and provide substantial support for updating the emotional dictionary. Through this comprehensive analysis, it provides a deeper user insight for e-commerce enterprises, and provides strong support for operational decisions and service optimization. Future research can focus on the application of deep learning techniques in sentiment analysis to further improve model accuracy and adaptability.

Impact of Artificial Intelligence–Based Technology on Nurse Management: A Systematic Review

Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes.Background: AI represents a significant transformation in healthcare management by enhancing decision‐making, communication, and resource optimization. However, the integration and strategic application of AI in nursing management are underexplored, particularly regarding its impact on leadership roles and healthcare delivery.Methods: Methodological guidelines described by PRISMA were followed, and quality was assessed using the Joanna Briggs Institute (JBI) methodology. The databases searched included the Web of Science, Scopus, CINAHLi, and PubMed. The review included quantitative, qualitative, and mixed‐method studies published between January 2015 and April 2024.Results: Fourteen studies were selected for the review. The key findings indicate that AI technologies facilitate better resource management, risk assessment, and decision‐making. AI also supports nurse managers in leading changes, enhancing communication, and optimizing administrative tasks.Conclusion: AI has been progressively integrated into nursing management, demonstrating significant benefits in operational efficiency, decision support, and leadership enhancement. However, challenges, such as resistance to technological change and ethical complexities, need to be addressed.Implications for Nursing Management: Specific training programs for nurse managers are essential to optimize the integration of AI. Such programs should focus on the management of AI applications and data analyses. In addition, creating interdisciplinary groups involving nurse managers, AI developers, and nursing staff is crucial for tailoring AI solutions to meet the unique needs of healthcare settings.