Operation Schedule Research Articles

Gambaran Perilaku Caring Perawat pada Pasien Pre Operasi di Ruang Rawat Inap Bedah Rumah Sakit Palembang

Anxiety is a response given by an individual to a threat. The situation of a person who is waiting for an operation schedule shows an event that is full of uncertainty, it can cause fear and anxiety, it is necessary for nurses to apply caring behavior which is the focus of nursing practice. This study aims to determine the description of nurses' caring behavior for pre-operative patients in the surgical inpatient room at a hospital in Palembang. The research design used in this research is descriptive. The data collection technique was purposive sampling with a total of 79 respondents. From the research results, it was found that more nurses had positive caring behavior, namely 70 respondents stated that nurses' caring behavior was positive with a percentage of 88.6% and 9 respondents stated that nurses' caring behavior was negative with a percentage of 11.4%. The conclusion is that more nurses have positive caring behavior for preoperative patients in the Palembang Hospital Surgical Inpatient Room.

A two-layer heuristic for patient sequencing in the operating room theatre considering multiple resource phases

In this paper, we study the surgery scheduling problem in the operating room theatre. The problem considers the sequencing of patients and calculation of their start times with splitting of surgeries into resource phases to facilitate the efficient use of different types of resources. We propose a dedicated two-layer heuristic to compose an operational patient and resource schedule. The first optimisation layer applies an evolutionary heuristic to devise patient schedules while considering the scheduling of the operating surgeons and rooms. This step employs a machine-learning mechanism predicting the feasibility of chromosomes, which improves the algorithm’s efficiency and effectiveness, and relies on novel local search operators to find high-quality solutions. The second layer devises the schedule of the other resources using a decomposition-based heuristic. Computational experiments are conducted to show the performance of the proposed two-layer heuristic and validate its design choices. We benchmark the proposed algorithm with other optimisation procedures and show the contribution of considering multiple resource phases for real-life decision-making.

A Method of Integrating Air Conditioning Usage Models to Building Simulations for Predicting Residential Cooling Energy Consumption

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

PERAMALAN JUMLAH PENUMPANG BUS RAPID TRANSIT (BRT) BANJARBAKULA DENGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE WITH EXOGENOUS VARIABLE (ARIMAX) DENGAN EFEK VARIASI KALENDER

Banjarbakula Bus Rapid Transit (BRT) is an inner-city bus-based mass transit system that provides a sense of comfort, safety, speed in mobility, and low cost in serving the citizens of Banjarmasin City and Banjarbaru City. Based on data on the number of passengers on the Banjarbakula BRT for the period April 2020 - February 2023, public interest in using the Banjarbakula BRT as a mode of transportation is quite high. However, the limited units and operational schedules make the Banjarbakula BRT unable to fully meet the needs of the public. Forecasting the number of passengers of BRT Banjarbakula for the next 12 periods is one of the measures to prepare the infrastructure, quality and units of BRT Banjarbakula in order to facilitate the public and create a better transportation system. In the Banjarbakula BRT passenger data, there is an increase in the number of passengers at certain times such as during religious holidays and school holidays, so this increase in passenger numbers is thought to be due to the influence of the calendar variation effect. This research intends to forecast the number of passengers of BRT Banjarbakula using the best ARIMAX model with the effect of calendar variation. The results indicate that the ARIMAX (0, 1, 1) model is the best ARIMAX model to forecast the number of passengers of BRT Banjarbakula for the next 12 periods. The forecast results indicate an increase in the month where the Christmas celebration and also the memorial haul guru sekumpul, so that the variable Christmas celebration and memorial haul guru sekumpul significantly affect the number of passengers of BRT Banjarbakula.Keywords: Forecasting, BRT Banjarbakula, ARIMAX with calendar variation effects

Energy management of multiple resource charging station

Managing plug-in electric vehicles (PEVs) recharging with different capacities can achieve the optimum operation schedule. Otherwise, stability issues for the microgrid may occur. Therefore, Energy management becomes essential for charging stations to meet the complexity of bidirectional power flow and multiple resource availability. This paper presents an energy management system of PEVs based on multiple resources managed by a DC microgrid. Three main resources are used: the national grid, Photovoltaic cells (PV), and Battery. The grid and PV cells are centralized distributed, whereas Batteries are decentralized distributed. Balance options are defined to match the vehicle requirements based on power availability, even the surplus power between the connected vehicles. The aims are to optimize grid energy usage, long life of the storage devices, and improve resilient power to the load demand. Metaheuristic methods (PSO) are used to enhance the response to the charging station operator. The results show that electric vehicles are a promising solution to keep the voltage stability of the grid in a high-demand approach. The PV is a flexible and cheap resource to recharge electric vehicles and synergize the national grid. Sometimes, trading power from a high state of the charging PEV to a lower one is beneficial to maintain the stability of the charging station. Keywords: microgrid, charging station, Electric vehicles, multiple resources, voltage stability.

Pulsed Laser Links Simulation for Small Satellite Clock Model Estimation

The expansion of satellite constellation applications brings attention to the need for responsive, reliable satellite communication. For preflight technology assessment of missions, a simulation of two spacecraft in low Earth orbit has been created to estimate clock synchronization and precision orbit determination based on measured instrumentation performance. A novel MATLAB-based numerical simulator was developed to model spacecraft-to-spacecraft laser time-transfer and estimate the offset between the spacecraft clocks over time. This simulation includes timing errors associated with laser pulse detection, as well as non-Gaussian clock drift models. Two on-board clocks are modeled: a cesium-based chip-scale atomic clock and a rubidium-based miniature atomic clock. The positions and velocities of the spacecraft at a reference epoch and the constant coefficients of a polynomial clock model are estimated. Results compare the estimated clock model of a mission operation that only uses GPS measurements and one that uses both GPS and laser pulse time-of-flight measurements between spacecraft referenced to their on-board clocks. Including lasing measurements reduces the root-mean-square clock model error to approximately 80% of the RMS of the cases with only GPS measurements. This simulation tool can be used to optimize the lasing operations schedule based on mission timing performance objectives.

Dynamic analysis of non-point source pollution in the Sanhekou Reservoir watershed: Implications for sustainable water management

Exogenous pollution, especially non-point source pollution runoff during a flood season, is an important factor affecting the water quality of reservoirs. The Sanhekou Reservoir (Shaanxi, China) is a newly dammed water body, so its non-point source pollution characteristics are unknown. In this study, three major tributaries of the Sanhekou Reservoir watershed were sampled during five rainfall-runoff events in the 2022 flood season. This enabled study of the transport processes of nitrogen, phosphorus, and suspended particles in the runoff under different rainfall conditions. Using hysteresis and redundancy analyses, the sources and transport modes of pollutants are explored here, alongside analysis of rainfall and hydrological characteristics that affect pollutant concentrations, and exploration of land-use relationships. The results showed that (i) nitrate nitrogen and particulate nitrogen were the main forms of nitrogen output, and particulate phosphorus was the main form of phosphorus output. (ii) Total nitrogen, suspended particles, total phosphorus, and particulate phosphorus were significantly affected by maximum rainfall intensity, average rainfall intensity, and average flow rate, whereas nitrate nitrogen and ammonia nitrogen were significantly affected by rainfall duration, rainfall amount, and rainfall-runoff duration. (iii) If 70 % of barren land and cultivated land with slope >15° were converted to forests, total nitrogen, total phosphorus, and suspended particulate loads in the Sanhekou Reservoir would be reduced during the flood season by 10.37 %, 33.01 %, and 45.39 %, respectively. The factors influencing migration and transformation of pollutants during rainfall runoff are important for operational scheduling decisions of reservoirs and guaranteeing quality of the water supply.

A Hybrid Approach for the Multi-Criteria-Based Optimization of Sequence-Dependent Setup-Based Flow Shop Scheduling

A key challenge in production management and operational research is the flow shop scheduling problem, characterized by its complexity in manufacturing processes. Traditional models often assume deterministic conditions, overlooking real-world uncertainties like fluctuating demand, variable processing times, and equipment failures, significantly impacting productivity and efficiency. The increasing demand for more adaptive and robust scheduling frameworks that can handle these uncertainties effectively drives the need for research in this area. Existing methods do not adequately capture modern manufacturing environments’ dynamic and unpredictable nature, resulting in inefficiencies and higher operational costs; they do not employ a fuzzy approach to benefit from human intuition. This study successfully demonstrates the application of Hexagonal Type-2 Fuzzy Sets (HT2FS) for the accurate modeling of the importance of jobs, thereby advancing fuzzy logic applications in scheduling problems. Additionally, it employs a novel Multi-Criteria Decision-Making (MCDM) approach employing Proportional Picture Fuzzy AHP (PPF-AHP) for group decision-making in a flow shop scheduling context. The research outlines the methodology involving three stages: group weight assessment through a PPF-AHP for the objectives, weight determination using HT2FS for the jobs, and optimization via Genetic Algorithm (GA), a method that gave us the optimal solution. This study contributes significantly to operational research and production scheduling by proposing a sophisticated, hybrid model that adeptly navigates the complexities of flow shop scheduling. The integration of HT2FS and MCDM techniques, particularly PPF-AHP, offers a novel approach that enhances decision-making accuracy and paves the way for future advancements in manufacturing optimization.

Neural network assisted branch and bound algorithm for dynamic berth allocation problems

One of the key challenges in maritime operations at container terminals is the need to improve or optimize berth operation schedules, thus allowing terminal operators to maximize the efficiency of quay usage. Given a set of vessels and a set of berths, the goal of the dynamic berth allocation problem is to determine the allocation of each vessel to a berth and the berthing time that minimizes the total service time. This problem can be solved using exact solution methods such as branch and bound (BB) algorithms or heuristic methods, however, exact methods do not scale to large-scale terminal operations. To this end, this paper proposes a BB algorithm in which branching decisions are made with a deep neural network. The proposed exact algorithm utilizes the search order of nodes based on the output of the neural network, with the goal of speeding up the search. Three types of solution representations are compared, along with machine learning models are created for each of them. Computational results confirm the effectiveness of the proposed method, which leads to computation times that are on average around half of those without the neural network.

A practical semi-empirical model for predicting the SoH of lithium-ion battery: A novel perspective on short-term rest

In this paper, the semi-empirical battery degradation prediction model proposed considers electrochemical degradation characteristics and represents degradation effects under various conditions, including different states of charge (SoC) areas. This model is specifically designed to address degradation during cycling and short-term rest periods in lithium-ion batteries using liquid electrolytes. Cycle aging incorporates the impact of solid electrolyte interphase (SEI) growth, a known dominant factor, and the model for short-term resting periods captures potential aging impacts on subsequent cycles due to internal material concentration gradients, moving away from the traditionally used calendar life approach. The derivation of the model presented in this paper is based on 14 data sets under different SoC conditions and 8 data sets under various Crates, explaining the degradation effects at 10 % SoC intervals and three different Crate points. Moreover, the model's performance was validated through capacity prediction for two data sets experimented with dynamic operational schedules of actual energy storage systems (ESS), including various conditions. The results showed a root mean square error (RMSE) of 0.564 and a mean absolute percentage error (MAPE) of 0.346. The superiority of this model is demonstrated by comparing its performance with four other types of degradation models derived through the same process in the validation data.

Prediction of Patient Visit Levels Based on Type of Service at Patar Asih General Hospital Using the Monte Carlo Method

This study aims to predict the level of patient visits at RSU Patar Asih based on the type of service provided using the Monte Carlo method. The Monte Carlo method was chosen because of its ability to handle data variability and uncertainty that often arises in the context of patient hospital visits. The data used in this study includes the number of patient visits based on the type of service at RSU Patar Asih, such as health services for pregnant women, health services for postpartum mothers, health services for newborns, health services for toddlers, health services at primary education age, health services at productive age. , health services for the elderly, health services for people with hypertension, health services for people with diabetes mellitus, health services for people with serious mental disorders (ODGJ), health services for people suspected of having tuberculosis, health services for people at risk of being infected with the human immune system (HIV)during the period 2021, 2022, 2023.The prediction results from this simulation obtained an accuracy of 81.75%, providing a fairly accurate estimate of the level of patient visits in the future. These predictions can be used by hospital management for resource planning, such as medical staff allocation, drug and medical equipment inventory management, and operational scheduling. Thus, the results of this research have the potential to improve operational efficiency and overall quality of hospital services. It is hoped that this research can contribute to more effective and efficient hospital management through data-based predictions, thereby increasing patient satisfaction and optimizing hospital resources.

Multi-strategy reference vector guided evolutionary algorithm and its application in multi-objective optimal scheduling of microgrid systems containing electric vehicles

To solve the optimization scheduling problem of microgrids involving Electric Vehicles (EVs), in this paper, the integration of EVs into the microgrid is considered in the microgrid system composed of wind turbine, photovoltaic turbine, micro gas turbine, fuel cell, and storage battery, and the economic dispatch model of the microgrid system containing EVs is established, with the minimization of the operation cost and pollutant treatment cost as the optimization objective. Meanwhile, Chebyshev mapping is introduced into the reference vector guided evolutionary algorithm (RVEA) to initialize the distribution of the population, and then an improved angle-penalized distance (APD) method is used to balance the convergence and diversity of the algorithm. The improved RVEA algorithm is applied to the scheduling model proposed in this paper, Different operational scheduling scenarios are investigated, scheduling results with different optimization objectives are discussed. The results show that the multi-strategy RVEA algorithm proposed in this paper can improve the economical and environmentally friendly protection of the microgrid system, and promote the optimal operation of the microgrid.

Decision Support System for Ranking Active Waste Bank in Makassar City Using TOPSIS and VIKOR Methods

In the city of Makassar, there were initially around 1000 waste banks, but this number has decreased significantly, and by 2023 only 381 waste banks remain active. The decline in the number of waste banks is primarily due to the society's lack of knowledge regarding the utilization of waste banks. This research aims to rank active waste banks in Makassar using the MCDM (Multi-Criteria Decision Making) technique. Two MCDM methods will be utilized in this study: the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method and the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method. Both methods share a common goal of finding the closest value to the ideal solution, but they differ in their normalization and aggregation functions. TOPSIS calculates the criteria weight values first, followed by the criteria values, whereas VIKOR starts with the highest criteria values and then calculates the criteria weights. The results of this research indicate that some alternatives received the same ranking using TOPSIS and VIKOR methods. The criteria used to calculate data for Waste Banks are Operational Hours, Operational Schedule, Total Customers, Total Employees, and Amount of Collected Waste. These criteria are determined based on Regulation Minister of Environment and Forestry Republic of Indonesia Number 14 of 2021 concerning Waste Management at Waste Banks.

Cost-Optimality Assessment of a Solar Trigeneration System for Tertiary Sector Buildings in Greece

To pave the way towards buildings’ decarbonization in the context of the European Union’s (EU) policy, the methodology of cost-optimality assessment based on regulation 244/2012/EU is a useful tool to explore and foster the application of energy technologies in buildings. Meanwhile, the fostering of concentrated solar power is included in the EU solar energy strategy. In this study, the cost-optimal methodology is employed for the techno-economic assessment of the integration of a novel solar, multi-purpose energy technology, namely a parabolic trough collector-based trigeneration system, in two building types with different characteristics, namely an office and a hospital, in Greece, thus allowing the evaluation of the cost-optimal system design and the impact of the building type on the system’s techno-economic performance. Reference buildings are defined and their energy demand is calculated through dynamic energy simulations. The trigeneration system’s performance for different design scenarios is then parametrically investigated using a simulation model. For each scenario, energy, environmental and economic indicators are calculated and the cost-optimal designs are extracted. In the cost-optimal implementation, the system covered 18.19–36.39% and 3.58–15.71% of the heating and cooling demand, respectively, while the reduction of the primary energy consumption and emissions was estimated at 10–14% and 10–16%, respectively. However, differences between the buildings related to the operation schedule and the loads led to the implementation of the system being economically more attractive in the hospital, while for the office, financial support is necessary for a viable investment.

Impact of the Delay Period between Electrochemical Hydrogen Charging and Tensile Testing on the Mechanical Properties of Mild Steel

With escalating global regulatory pressure for countries to adhere to emission laws, repurposing existing natural gas pipelines for hydrogen-based commodities stands to be an economical solution. However, the effects of hydrogen embrittlement must be thoroughly considered for this application to avoid the unexpected catastrophic failure of these pipelines. The literature proposes several physicochemical embrittlement models. This paper reports one aspect of hydrogen embrittlement that remains to be quantified: the recovery of ductility (embrittlement) of mild steel specimens subjected to artificially accelerated hydrogen absorption via electrochemical charging as a function of time. The effects of charging duration and particularly the delay period between charging and mechanical tensile testing were investigated. Unsurprisingly, longer charging time shows a greater loss of elongation; however, a more extensive recovery of ductility correlated with longer charging time in the first few days after charging. The data also show that while the uncharged mild steel met all minimum required values for strength and elongation for the specified grade, there was a substantial variability in the elongation to failure. The same trends in variability of elongation translated to the hydrogen-charged specimens. Due to this extensive variability, failure to meet the elongation specification of the grade is reported based on the worst-case scenario obtained for a given set of samples for each exposure condition. These results have practical implications for the monitoring and testing of infrastructure exposed to hydrogen, particularly as this relates to industry planned operational shutdown schedules.

Analisa Faktor-Faktor Perceived Service Quality dalam Bidang Aviasi terhadap Passenger Loyalty melalui Passenger Satisfaction dan Passenger Complaint pada Penumpang Batik Air Tujuan dari dan ke Surabaya

This research is intended to find factors that influence Passenger Loyalty through Passenger Satisfaction and Passenger Complaint of Batik Air customers in Surabaya. This research provides benefits for the aviation industry in Indonesia, especially to create customer loyalty to an airline in order to be able to survive in the market in the long term.This study is causal with quantitative methods and uses the Amos 22.0 application to assist the processing of primary data from 200 Batik Air customers in Surabaya which was collected using the Snowball Sampling Technique. In this study it was found that customers will be loyal to a brand influenced by Customer Satisfaction which is most influenced by Safety and Security, Reliability, Connectivity, Information Service, Accessibility, Operation Schedule, Comfort, Ticket service, and Personalized Services. Consumers' perception that airplanes with low prices do not prioritize safety, but from this research it can be broken because Batik Air is significant in the Safety and Security variable. eywords: Accessibility, Comfort, Connectivity, Information Service

Operation scheduling method in an off-grid considering frequency violation probability caused by short-term variation

The use of renewable energy (RE) resources in off-grid applications is a promising solution from both the economic and environmental perspectives. However, the employment of RE generation should consider the uncertainty in their generation output; in particular, short-term variations in generation output result in serious frequency violations. The development of the off-grid planning, operation, and scheduling considering frequency variation is significant. This study proposes a probabilistic model for the frequency variation caused by load variation, which includes the dynamics of rotating generators and frequency-supporting smart inverters. The proposed method calculates the maximum frequency deviation by transforming the block, considering the output limit of the governor and smart inverters. This calculation is fast because it does not require time-sequential simulation. The effectiveness of the proposed method for off-grid operational scheduling problems was demonstrated through numerical studies. The results show that the proposed method can evaluate the same results as time series simulations 1500 times faster. Additionally, frequency deviations caused by short-term variations can be avoided, and battery capacity for zero frequency deviation is estimated with a 5% error. These results have the potential to contribute significantly to the operation of the grid with a large portion of RE resources.

Normalised difference moisture index in water stress assessment of maize crops

Remote sensing is a promising technique for better management of water resources in agriculture through improvement of dynamic control and operational scheduling on irrigated croplands. The main goal of this study was to identify the possibilities of application of the normalised difference moisture index (NDMI) to water stress monitoring in maize crops, and to determine the relationship between the index and soil moisture content. The study was carried out in 2019–2021 in the experimental fields of disturbed maize, cultivated on dark-chestnut soils in the Southern Ukraine at the NAAS Institute of Climate-Smart Agriculture. The crop cultivation technology was common for the conditions of the steppe zone of Ukraine. Actual soil moisture content was determined by gravimetric method in the pre-sowing and post-harvest period. The NDMI values were calculated using cloudless aerospace images from the satellites Landsat 8, Sentinel-2, and MODIS with 250 m resolution. It was revealed that the seasonal NDMI dynamics perfectly reflected the water-supply conditions of the disturbed maize, and could be used for operational monitoring and scheduling of irrigation. The parameters of the water-supply conditions were determined in 2021, which was the wettest year of the study: the cumulative seasonal NDMI reached 1.71, while the highest water stress was recorded in the driest year, 2020, – the cumulative NDMI was 0.15. Additionally, there was a moderately strong negative correlation between NDMI and soil moisture content, and the coefficient of determination was 0.62. The linear regression models, developed to predict soil moisture content in the 0–100 cm layer depending on the NDMI values, had good fitting quality and reasonable accuracy, but they required further calibration and extension of the initial dataset to provide more robust and reliable results for practical implementation. Based on the results of the study, spatial NDMI could be considered a good and reliable tool for improving irrigation water management. Further studies should focus on the practical implementation of the NDMI-based model of moisture-content estimation, as well as on the possibilities of the index usage for mapping irrigated lands.

Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field Algorithm

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field Algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions; while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result.

Design of information management model based on multiobjective optimization algorithm in intelligent electric financial system

The electric power infrastructure is the cornerstone of contemporary society’s sustenance and advancement. Within the intelligent electric power financial system, substantial inefficiency and waste in information management persist, leading to an escalating depletion of resources. Addressing diverse objectives encompassing economic, environmental, and societal concerns within the power system helps the study to undertake a comprehensive, integrated optimal design and operational scheduling based on a multiobjective optimization algorithm. This article centers on optimizing the power financial system by considering fuel cost, active network loss, and voltage quality as primary objectives. A mathematical model encapsulates these objectives, integrating equations and inequality constraints and subsequently introducing enhancements to the differential evolutionary algorithm. Adaptive variation and dynamic crossover factors within crossover, variation, and selection operations are integrated to optimize algorithm parameters, specifically catering to the multiobjective optimization of the electric power system. An adaptive grid method and cyclic crowding degree ensure population diversity and control the Pareto front distribution. They experimentally validated the approach and the comparisons conducted against AG-MOPSO, INSGA-II, and NSDE algorithms across standard test functions: ZDT1, ZDT2, ZDT3, and DTLZ4. The convergence evaluation indices for this study’s scheme on ZDT1 and ZDT2 are 0.000938 and 0.0034, respectively. Additionally, distribution evaluation indices on ZDT1, ZDT2, ZDT3, and ZDT4 stand at 0.0018, 0.0026, 0.0027, and 0.0009, respectively. These indices indicate a robust convergence and distribution, facilitating the optimization of electric power financial information management and the intelligent handling of the electric power financial system’s information, thereby enhancing the allocation of material and financial resources.