Efficient Management Research Articles

Estimation of Groundwater Recharge in a Volcanic Aquifer System Using Soil Moisture Balance and Baseflow Separation Methods: The Case of Gilgel Gibe Catchment, Ethiopia

Understanding the recharge–discharge system of a catchment is key to the efficient use and effective management of groundwater resources. The present study focused on the estimation of groundwater recharge using Soil Moisture Balance (SMB) and Baseflow Separation (BFS) methods in the Gilgel Gibe catchment where water demand for irrigation, domestic, and industrial purposes is dramatically increasing. The demand for groundwater and the existing ambitious plans to respond to this demand will put a strain on the groundwater resource in the catchment unless prompt intervention is undertaken to ensure its sustainability. Ground-based hydrometeorological 36-years data (1985 to 2020) from 17 stations and satellite products from CHIRPS and NASA/POWER were used for the SMB method. Six BFS methods were applied through the Web-based Hydrograph Analysis Tool (WHAT), SepHydro, BFLOW, and Automated Computer Programming (PART) to sub-catchments and the main catchment to estimate the groundwater recharge. The streamflow data (discharge) obtained from the Ministry of Water and Energy were the main input data for the BFS methods. The average annual recharge of groundwater was estimated to be 313 mm using SMB for the years 1985 to 2020 and 314 mm using BFS for the years 1986 to 2003. The results from the SMB method revealed geographical heterogeneity in annual groundwater recharge, varying from 209 to 442 mm. Significant spatial variation is also observed in the estimated annual groundwater recharge using the BFS methods, which varies from 181 to 411 mm for sub-catchments. Hydrogeological conditions of the catchment were observed, and the yielding capacity of existing wells was assessed to evaluate the validity of the results. The recharge values estimated using SMB and BFS methods are comparable and hydrologically reasonable. The findings remarkably provide insightful information for decision-makers to develop effective groundwater management strategies and to prioritize the sub-catchments for immediate intervention to ensure the sustainability of groundwater.

Impact of knowledge management on knowledge worker productivity: individual knowledge management engagement as a mediator

ABSTRACT The current research intends to develop and test a multidimensional model of knowledge management using second-order psychometric constructs and determine whether individual knowledge management engagement mediates the knowledge worker productivity. Data was collected using structured questionnaires, and 411 final samples were analysed using structural equation modelling. The results show that knowledge worker productivity is positively impacted by the knowledge management factor. The research also discovered evidence of an interaction between knowledge management and knowledge worker productivity that is mediated by individual knowledge management engagement. The theoretical conclusion of the research is that this multidimensional knowledge management model is suitable for gauging knowledge worker productivity in developing nations as well as industrialised ones, and it may also be applied to other service sectors. This study demonstrates a clear relationship between knowledge worker productivity and individual knowledge management engagement which will the practitioners to create efficient knowledge management strategies.

DRL-Tomo: a deep reinforcement learning-based approach to augmented data generation for network tomography

Abstract Accurate and current comprehension of network status is crucial for efficient network management. Nevertheless, direct network measurement strategies entail substantial traffic overhead and demand intricate coordination among network entities, making them impractical. Network tomography, an indirect measurement approach, utilizes insights garnered from measured parts to deduce characteristics of the entire network. Past studies frequently depend on acquiring challenging-to-access information, such as the complete network topology or support from specialized protocols. Unfortunately, these constraints pose challenges in non-cooperative scenarios where obtaining such information is difficult. Recent endeavors pursue emancipating tomography from dependence on copious information, striving to predict unmeasured path performance using limited data. Nevertheless, the disparity between the measured data and actual performance has hindered the accuracy. In response, we introduce an innovative tomography framework named DRL-Tomo, designed to alleviate potential biases. DRL-Tomo initiates by generating augmented data through deep reinforcement learning, gradually approximating the genuine performance of unmeasured paths. Subsequently, a neural network model is trained using this augmented data, enabling precise inferences. Our experiments, encompassing both real-world and synthetic datasets, vividly demonstrate DRL-Tomo’s remarkable enhancement. Specifically, it achieves a substantial 10%–67% improvement in path delay prediction and an impressive 30%–98% enhancement in path loss rate prediction.

Control of Grid-Connected and Standalone Microhydraulic Turbine Using a Six-Phase Induction Generator

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system.

Urbanization and Its Environmental Impact on Rangareddy's Peri-Urban Landscape: Insights from Secondary Data

This study investigates the dynamics of urbanization and its environmental implications in peri-urban areas of Rangareddy, focusing on key regions including Shamshabad, Manikonda, Gachibowli, and Shadnagar over the past two decades. Through a comprehensive analysis of land use changes, environmental assessments, socio-economic dynamics, and sustainability challenges, the research highlights significant transformations driven by rapid urban expansion. The findings reveal a notable shift in land use patterns characterized by a substantial increase in residential and commercial developments, leading to a decline in agricultural lands and natural ecosystems. This urban sprawl is fueled by population growth, economic activities, and infrastructural developments, reflecting broader urbanization trends observed across Indian cities. Environmental assessments underscore critical challenges associated with urban growth, including deteriorating air and water quality attributed to increased industrial activities and vehicular emissions. The reduction in green spaces exacerbates environmental concerns such as heat island effects and biodiversity loss, necessitating urgent interventions to integrate sustainable land management practices into urban planning strategies. Demographic shifts in peri-urban Rangareddy reveal accelerated population growth rates, surpassing national averages. While urbanization has brought enhanced access to amenities and economic opportunities, it has also exacerbated socio-economic disparities among residents. Addressing these disparities requires inclusive development strategies that ensure equitable access to resources and opportunities, fostering social cohesion and community resilience. The study identifies critical sustainability challenges, including inadequate infrastructure, water scarcity, and inefficient waste management systems. These challenges underscore the imperative for effective policy interventions and governance frameworks aimed at promoting environmental conservation and enhancing urban resilience. Policy recommendations emphasize the need for integrated urban planning approaches that prioritize green infrastructure development, pollution control measures, and community engagement initiatives. Looking forward, future research should focus on longitudinal studies to monitor urbanization trends and evaluate the effectiveness of policy interventions in mitigating environmental impacts and enhancing socio-economic conditions in peri-urban areas. Long-term strategies should emphasize adaptive governance mechanisms, stakeholder engagement, and capacity building to support sustainable urban development goals.

Rancang Bangun Sistem Inventory (Studi Kasus: UD. Asia Pratama Pekanbaru)

UD Asia Pratama Pekanbaru, a trading company in the building industry, is facing problems in managing inventory which is still manual, causing inefficiencies and inaccurate data. This research aims to design and implement an efficient and effective web-based inventory management system. The system development method uses the Waterfall model, including requirements analysis, design, implementation and testing. Data was collected through interviews with employees and analysis of documents related to inventory. The research results show that the system developed can increase inventory management efficiency, with real-time stock management features, recording of incoming and outgoing goods, and accurate reporting. User testing and evaluation shows that the system is acceptable and meets UD Asia Pratama's needs in overcoming inventory problems.

Enhancing Inventory Management through Safety-Stock Strategies—A Case Study

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

Relationship between civil construction companies and suppliers: comparative analysis with companies in other sectors

Strategic supplier management establishes criteria for the selection, evaluation, and development of suppliers, in addition to the administration of contracts and supplies. This type of management aims to organize all processes involving suppliers, defining criteria to support decision-making in selecting the best partners, considering those who meet pre-established requirements. Furthermore, efficient management provides closer proximity to suppliers, facilitating relationships, fostering collaborative innovation, improving product costs, and increasing the company's competitiveness. The objective of this work is to analyze the relationship of suppliers in the construction industry and compare it with companies from other sectors. To achieve this objective, an applied, quantitative, and exploratory research was developed through field research. Questionnaires were applied to employees of construction companies and companies from other sectors, such as service providers and large industrial sectors, addressing issues related to organizational characteristics and specific axes that determine the relationship, such as trust, long-term relationship, and information sharing between client and supplier. The collected data were subjected to descriptive and exploratory analyses to observe the divergent and similar characteristics between the two groups. The results indicate that construction companies have gaps in the axes of trust, information sharing, and especially in the sphere of long-term relationships compared to companies in other sectors. This indicates the need for improvements in supplier management practices in the construction industry to achieve a competitive performance more aligned with other industrial sectors. The implementation of more robust strategies and the adoption of better practices in supplier management can help overcome these gaps, resulting in a more efficient and collaborative business environment, with mutual benefits for construction companies and their suppliers.

Smart AODV Routing Protocol Strategies Based on Learning Automata to Improve V2V Communication Quality of Services in VANET

The Adhoc On-Demand Distance Vector (AODV) protocol faces challenges in selecting the best relay nodes, which requires optimization to improve performance in Vehicular ad-hoc networks (VANETs). This study aims to enhance Vehicle-to-Vehicle (V2V) communication in VANETs by implementing the Learning Automata-Driven Ad-hoc On-Demand Distance Vector (LA-AODV) routing protocol. LA-AODV is designed to achieve higher packet delivery ratios and optimize data transfer rates, even under congested network conditions, by dynamically adjusting to changing network scenarios. The performance evaluation includes six key metrics analyzed under varying node densities and time intervals, comparing LA-AODV against the standard AODV protocol. Results indicate that LA-AODV consistently outperforms AODV, demonstrating improved efficiency in flood identifier management, reduced data loss, higher packet delivery ratios, better throughput, and reduced end-to-end delay and jitter. Specifically, under a 20-node scenario, LA-AODV exhibits lower flood ID scores (54 vs. 88), reduced packet loss (11% vs. 12%), higher PDR (88.0% vs. 87.0%), and superior throughput (85.34 Kbps vs. 47.26 Kbps). Additionally, LA-AODV achieves lower end-to-end delay (6.84E+09 ns vs. 3.76E+10 ns) and jitter (2.52E+09 ns vs. 2.15E+10 ns). These findings suggest that LA-AODV significantly enhances Quality of Service (QoS) in vehicular ad-hoc networks, positioning it as a promising solution for optimizing V2V communication performance.

Determinants Factor of Dividend Policy with Company Size as a Moderation Variable

This study looks at how company size (SIZE) moderates the effects of profitability (ROI), liquidity (CR), and leverage (DER) on dividend policy (DPR) in food and beverage firms listed on the Indonesia Stock Exchange between 2017 and 2021. Purposive sampling is the procedure used in the sampling approach. This study's sample comprised sixteen companies listed on the Indonesia Stock Exchange. Multiple regression and moderated regression are the analysis techniques employed, with a 5% significance level. The study's pre-moderation results demonstrate the significant influence of the variables Profitability (ROI) and Leverage (DER) on Dividend Policy. The study's post-moderation results demonstrate the significant influence of Liquidity (CR), Leverage (DER), and Company Size (Size) on Dividend Policy. The executive. The managerial implications in this study are that management needs to set clear, realistic financial performance targets to ensure that the company is able to achieve sufficient to support dividend policy, and large companies can take advantage of economies of scale to improve financial performance, which in turn can support a larger dividend policy stable. Management must ensure efficient cash flow management to support dividend payments.

A novel MTCMOS based 8T2M NVSRAM design for low power applications with high temperature endurance

This research investigates, for the first time, a novel eight-transistor-two-memristor (8T2M) nonvolatile static random access memory (NVSRAM) with 7-nm technology. The key innovation in this design lies in the incorporation of multiple-threshold complementary metal oxide semiconductor (MTCMOS) technology with power gating technique, which enables efficient power management and enhanced performance with low leakage current. The implementation of multiple threshold voltage levels allows for dynamic control of transistor behavior, optimizing power consumption and read/write speeds. As compared to a traditional six-transistor (6T) static random access memory (SRAM) cell, it has been ascertained that there is a 33% enhancement in the read margin and an 18% improvement in the write margin. Moreover, the delay for read, write ‘0’ and write ‘1’ is also minimized by 63.89%, 37.99% and 42.77%. Furthermore, the power attenuation is also reduced for read and write by 63.02% and 81.6%, respectively with respect to a conventional SRAM.

STRATEGIC PLANNING GUIDELINES FOR SUCCESS IN INDUSTRIAL ENTERPRISES

Objective: The objective of this research is to examine the organizational structure and operational characteristics of industrial businesses, investigate components of strategic planning guidelines, and develop a structural equation model, with the aim of achieving sustainable success for industrial enterprises. Theoretical Framework: Content analysis method is a theoretical framework used to examine the presence and concepts of qualitative data. The framework is used to summarize findings and recommendations, from in-depth interviews and focus group discussions, into frequency values. Survey research framework is a structured approach that guides design, implementation, and analysis of surveys to gather data from targeted populations. The framework is used in quantitative research component, to collect and analyze data using descriptive and inferential statistics Method: Qualitative research involved conducting in-depth interviews with 9 experts and facilitating focus group discussions with 11 qualified individuals. Quantitative research is conducted through surveys to 500 industrial business executives, utilizing descriptive statistics, correlation analysis, and multivariate statistics. Results and Discussion: Findings from developed structural equation model indicated good alignment with observational data, with a Chi-square value of 0.094, Chi-square ratio of 1.115, goodness-of-fit index of 0.954, and a root mean square error of approximation of 0.015. Research Implications: Strategic planning is significant in business operations. Well-formulated business strategies will impact managerial efficiency, creating opportunities and enhancing a competitive advantage for industrial enterprises. Originality/Value: Findings revealed four significant factors contributing to success in industrial enterprises, listed in descending order of importance: 1) adaptive skills, 2) digital technology, 3) collaboration, and 4) organizational support.

Simulation of Hospital Waste Supply Chain in the Context of Industry 4.0—A Systematic Literature Review

Supply chain management is a crucial task for all companies, as it is essential to respond to increased demand and competitiveness. An exponential increase in hospital waste can cause very significant risks to both humans and the environment if it is not treated adequately. In this way, the efficient management of this waste can bring benefits and prevent an increase in the likelihood of disease occurring. With the growth of technology, there are various tools available to improve decision-making in the supply chain. Simulation, one of the pillars of Industry 4.0, is one of the tools being used. With the complexity of systems increasing, hybrid simulation provides more precise solutions to problems. The aim of this article is therefore to systematically research and analyze the literature on the use of simulation/hybrid simulation in supply chains in the health sector. In this way, 20 articles that approach simulation as a decision support tool in hospital waste supply chains were analyzed in the context of Industry 4.0. The results indicate that the years 2020 and 2023 had the highest number of publications (with five each) and that countries such as China and Indonesia had the highest number of documents on this topic (with five each). The topics in this research area with more related documents were environmental sciences, computer sciences and medicine. This research highlights the fundamental role of simulation approaches in improving the performance of supply chains in the healthcare sector, promoting resilience and improving healthcare outcomes for patients.

Association of Microbiome Diversity with Disease Symptoms in Brassica oleracea Leaves

Cabbage (Brassica oleracea), a crop of major economic importance worldwide, is affected by numerous diseases, which are caused by a wide range of microorganisms, including fungi, oomycetes, bacteria, and viruses, which lead to important losses in yield and quality. The increasing availability of reference genomes of plant-associated microbes together with recent advances in metagenomic approaches provide new opportunities to identify microbes linked to distinct symptomatology in Brassica leaves. In this study, shotgun metagenomics was used to investigate the microbial community in leaves of B. oleracea plants from agricultural farmlands. Compared with conventional techniques based on culture-based methods, whole-genome shotgun sequencing allows the reliable identification of the microbial population inhabiting a plant tissue at the species level. Asymptomatic and symptomatic leaves showing different disease symptoms were examined. In the asymptomatic leaves, Xanthomonas species were the most abundant taxa. The relative abundance of bacterial and fungal communities varied depending on disease symptoms on the leaf. The microbiome of the leaves showing mild to severe levels of disease was enriched in bacterial populations (Sphingomonas, Methylobacterium, Paracoccus) and to a lesser degree in some fungal taxa, such as Alternaria and Colletotrichum (e.g., in leaves with high levels of necrotic lesions). Sclerotinia species were highly abundant in severely damaged leaves (S. sclerotium, S. trifolium, S. bolearis), followed by Botrytis species. The common and specific bacterial and fungal species associated to disease symptoms were identified. Finally, the analysis of the gene functions in the metagenomic data revealed enrichment in carbohydrate-active enzymes potentially involved in pathogenicity, whose distribution also varied among disease severity groups. Understanding the B. oleracea leaf microbiome in agricultural ecosystems will pave the way for the efficient management of diseases in this crop.

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Integrated learning has attracted much attention from industry and academia. In the new era, colleges and universities need to discuss information management in light of actual conditions, integrate different data in each information system into the same database, so as to form a data warehouse based on the integrated database which can truly reflect the historical changes of data and provides support for managers' decision-making. This paper analyzes the clustering effect of standard differential evolution algorithm, improved differential evolution algorithm and K-means algorithm. The algorithm is tested using Iris and Wine database marts, the results show that the K-means algorithm is a relatively poor algorithm and its accuracy is significantly lower than the other two. Based on big data, multi-factor interactive variance analysis technology is used to analyze different data indicators and influencing factors. Therefore, colleges and universities can use the database to better understand the problems and advantages in management, thus to improve management efficiency and teaching level.

LEVERAGING ARTIFICIAL INTELLIGENCE (AI) IN PUBLIC SECTOR FINANCIAL RISK MANAGEMENT: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS

This paper examines the transformative role of Artificial Intelligence (AI) in enhancing financial risk management practices within the public sector in different context. With the increasing complexity of financial markets and the growing demand for transparency and efficiency in public finance management, traditional methods of risk identification and mitigation are proving to be inadequate. AI and machine learning technologies offer a promising alternative, with their ability to process vast amounts of data, recognize patterns, and predict future trends with a high degree of accuracy. We delve into the application of predictive models for identifying various financial risks, including credit, market, liquidity, and operational risks, and explore how AI algorithms can optimize decision-making processes through scenario analysis, resource allocation, and policy impact analysis. The paper also addresses the challenges associated with integrating AI into public financial management systems, such as data privacy concerns, the need for skilled personnel, and the importance of developing regulatory frameworks that ensure ethical use and transparency. By presenting case studies and real-world applications, this study highlights both the successes and failures of AI implementations in the public sector, providing valuable lessons learned and best practices. As AI technologies continue to evolve, this paper underscores the need for ongoing research and adaptation to fully realize their potential in making public sector financial risk management more proactive, efficient, and resilient.

Causality-inspired crop pest recognition based on Decoupled Feature Learning.

Ensuring the efficient recognition and management of crop pests is crucial for maintaining the balance in global agricultural ecosystems and ecological harmony. Deep learning-based methods have shown promise in crop pest recognition. However, prevailing methods often fail to address a critical issue: biased pest training dataset distribution stemming from the tendency to collect images primarily in certain environmental contexts, such as paddy fields. This oversight hampers recognition accuracy when encountering pest images dissimilar to training samples, highlighting the need for a novel approach to overcome this limitation. We introduce the Decoupled Feature Learning (DFL) framework, leveraging causal inference techniques to handle training dataset bias. DFL manipulates the training data based on classification confidence to construct different training domains and employs center triplet loss for learning class-core features. The proposed DFL framework significantly boosts existing baseline models, attaining unprecedented recognition accuracies of 95.33%, 92.59%, and 74.86% on the Li, DFSPD, and IP102 datasets, respectively. Extensive testing on three pest datasets using standard baseline models demonstrates the superiority of DFL in pest recognition. The visualization results show that DFL encourages the baseline models to capture the class-core features. The proposed DFL marks a pivotal step in mitigating the issue of data distribution bias, enhancing the reliability of deep learning in agriculture. © 2024 Society of Chemical Industry.

An in-depth analysis of facility management approaches in Nigeria's ailing healthcare sector

Purpose This study aims to examine facility management practices in the Nigerian healthcare sector, exploring approaches and identifying challenges facing effective healthcare facilities management. The purpose is to contribute to the development of a framework for enhancing healthcare facility management efficiency in Nigeria. Design/methodology/approach The study employs a sequential in-depth exploratory qualitative research approach. The data collection involved conducting semi-structured interviews with 15 facility managers from diverse healthcare organisations in Nigeria. The qualitative data collected were analysed using thematic analysis. Findings The study reveals scheduled, unscheduled and mixed approaches as the three facility management approaches used in Nigeria. It also substantiates the underdeveloped nature of facility management in Nigeria's healthcare sector, exacerbated by challenges such as socioeconomic, operational, technological and regulatory challenges. Practical implications The study uncovers systemic issues affecting have attainment of Sustainable Development Goal 3 (Good Health and Well-being) and advocates for a comprehensive approach to enhance healthcare infrastructure, contributing to improved health outcomes and sustainable development. Originality/value This research uniquely uncovers the hidden challenges facing effective healthcare facility management in Nigeria, providing a foundation for stakeholders to formulate solutions and rescue the struggling state of healthcare facilities in the country.

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

Lagrangian relaxation-based approaches for cooperative location and assignment of emergency responders

Efficient fire response and the coordination of various fire vehicles are paramount for delivering optimal services and conducting effective rescue operations. This study explores the challenges of cooperative station location and vehicle assignment, focusing on the synergistic dispatch of fire vehicles from multiple stations. It highlights that the response time of the last arriving vehicle, crucial for forming a complete and effective response unit, is a critical determinant in the overall success of fire rescue operations. The primary objective of the model is to minimize the total system cost, encompassing both the capital investment in fire stations and the potential losses from fire incidents. By employing a Lagrangian relaxation technique, the model effectively simplifies the optimization challenge into more manageable sub-problems, thereby enhancing solution accuracy. The robustness and flexibility of the model were tested through simulations in a hypothetical network and a real-world scenario, using data from urban fire services. The results demonstrate the model’s scalability and adaptability to different network configurations and operational constraints, showcasing its potential to substantially enhance emergency response efficiency and decision-making in urban fire management. This research makes a significant contribution to emergency response management by providing a validated framework that improves the coordination and effectiveness of fire rescue operations, thereby reducing fire damage and enhancing public safety.