Model Of Operation Research Articles

Decision-making model for production and operation of underground gold mines considering low-carbon condition.

Within the framework of a low-carbon transition and integrated mineral resource exploitation, this study presents an innovative system dynamics (SD) model designed to optimize decision-making and enhance profitability in underground gold mining operations. The novel approach seamlessly integrates critical subsystems, including reserves, mining, ore dressing, smelting, financial, and carbon reduction, offering a comprehensive framework for the analysis of efficiency and sustainability. Utilizing causal loop and system flow diagrams, the model elucidates the synergistic impacts of index variations on mine operational efficiency. The model is applied to a case study involving three mining areas within a specific gold mine in China, where sensitivity analysis identifies key indicators affecting profitability. Furthermore, it examines dynamic trends under varying carbon tax scenarios. The findings reveal that mining strategic adjustments can significantly enhance profitability, extend the operational lifespan of mines, and reduce emissions.

A dynamic analysis of a green closed-loop supply chain with different on-line platform smart recycling and selling models

Global warming and waste product recycling have become increasingly serious. Green innovation of a closed-loop supply chain (GCLSC) and selection of on-line platform recycling and sales models need to be solved as a matter of urgency. To achieve this purpose, dynamic influences of green innovation, platform smart marketing, and recycling investments on goodwill are considered. Dynamic game models are used to solve the equilibrium solutions and optimal trajectories under different models, and influences of commission rates on the profits and key variables are compared via numerical simulations. The results show that the evolution paths of goodwill under the four decision models all descend at first, then stabilize; the optimal strategy can be selected based on the intervals of sales and recycling commission rates. Under the dual-agency model (agency selling and platform agency recycling (Model AP)), the higher the sales commission rate (SCR) is, the more likely the manufacturer chooses the reselling and platform agency recycling (Model RP), which, however, may lead to insufficient green innovation investment. The higher the RCR, the greater the platform profit when selecting agency selling. The system profit first increases, then decreases with growing sales commission, and Model AP represents the optimal strategy. The research offers reference for the selection of operation models and investment decision, is conducive to improving the enterprise goodwill, and promotes the sustainable development of the GCLSC.

Challenges hindering industrialized wooden multi-story construction from a contractor’s perspective: a case study from Finland

PurposeIndustrialized construction (IC) is growing globally as an alternative and supplemental construction concept. High expectations have been set for IC to solve existing problems in the industry and enhance productivity. This study aims to identify the most intriguing challenges hindering industrialized wooden multi-story construction (WMC) in the Finnish context from a contractor’s perspective.Design/methodology/approachMethodologically, this study was a qualitative, single-case study with an inductive approach. Data were collected through five semi-structured interviews and analyzed using NVivo software. Observations were coded and classified into 10 categories and finally synthesized into 3 themes.FindingsThe results revealed that industrialized WMC companies are generating many innovations, primarily occurring at the company level. However, shortages in product systems and operation models, such as concept inflexibility and insufficient design fidelity and quality control practices, hinder the progress of industrialization. The findings also indicate that within the pre-fabrication context, production capacity management and cash flow management are key control factors in industrialized WMC. Strong variation in regulatory interpretation by authorities creates a significant challenge for IC concept development and project management.Originality/valueThis study endorses the existing research findings, highlights a new perspective on challenges in IC and highlights the importance of increasing cooperation between companies engaged in industrialization measures, accelerating the development of individual companies and fostering a more appropriate industry structure.

Understanding to collaborate: the unique characteristics of NGOs and intersectoral policy

This paper aims to explore ways to foster more effective collaboration between the state and non-governmental non-proft organizations (NGOs) when their interests align. While NGOs often perceive state policies as well intentioned but poorly executed, this paper argues that a more effective approach would leverage the unique strengths and weaknesses of NGOs, informed by scientific insights. By comparing Russian realities with established theories, we identify the prerequisites for a thriving NGO sector. A key factor is the intrinsic motivation of NGOs, coupled with adherence to legal frameworks that define their role within the broader societal landscape. Our analysis reveals shortcomings in current state policies. These include a lack of clear distinctions between sectors, hindering the optimal utilization of each sector's strengths, as well as an underestimation of the NGO sector's innovative potential and its unique governance structures. To improve this situation, we propose a gradual transition towards a more efficient model of NGO sector operation. This model would encourage the state to support NGOs without disrupting existing practices, while fostering a shift toward more progressive approaches.

Optimal operation and offering strategy for profit‐seeking of a coal‐mine energy prosumer in a liberalised market

AbstractCoal mines consume electricity from grids while making use of various derived energy resources to generate heat and power. This allows coal mines to act as energy prosumers. This paper presents an integrated optimisation model of optimal operation and offers strategies for profit‐seeking of a coal‐mine energy prosumer in a liberalised market. The discussed problem is formulated as a tri‐stage bi‐level programming model. In the upper level, the coal mine‐integrated energy system (CMIES) operator decides the retail contract management, equipment operation plan, and retail electricity price offered to end‐users through three stages to maximise its expected profits at a predefined risk level. In the lower level, the end‐users react to the price bids offered by the CMIES operator under study and other market retailers to minimise their costs for energy procurement. Unlike previous approaches, the endogenous uncertainties associated with market subjects' strategic behaviours are explicitly considered. To solve the proposed problem efficiently, the Karush–Kuhn–Tucker conditions and multicut benders decomposition method are employed. The simulation results show that the collaborative optimisation of the energy cycle and production scheduling can reduce daily operating costs by nearly $3576 and carbon emissions by 6.72 tons, which verifies the effectiveness of the proposed method.

Higher hospital volume reduces early failure rates in single-stage revision TKR for infection: An analysis of the United Kingdom National Joint Registry and National Administrative Databases.

Revision knee replacement (RevKR) for infection is rare but increasing. It is hypothesised that higher hospital volume reduces adverse outcomes. The aim was to estimate the association of surgical unit volume with outcomes following first, single-stage RevKR for infection. This population-based cohort study merged data from the United Kingdom National Joint Registry, Hospital Episode Statistics, National Patient Reported Outcome Measures and the Civil Registrations of Death. Patients undergoing procedures between 1 January 2009 and 30 June 2019 were included. Early outcomes were chosen to reflect the quality of the surgical provision and included re-revision at 2 years, mortality, serious medical complications, length of stay and patient-reported outcome measures (PROMs). Adjusted fixed effect multivariable regression models were used to examine the association between surgical unit mean annual caseload and the risk of adverse outcomes. A total of 1477 patients underwent first-time single-stage RevKRs for infection across 267 surgical units and 716 surgeons. Following adjustment for age, gender, American Society of Anaesthesiologists grade, surgeon volume, year of surgery and operation funder and modelling surgical unit volume with restricted cubic spline, a greater mean annual volume was associated with a lower risk of re-revision at 2 years. The odds of re-revision in hospitals performing fewer than or equal to 12 cases per year was 2.53 (95% confidence interval = 1.50-4.31) times more likely than hospitals performing three to four cases per month. Annual variation in surgical unit volume was not associated with mortality and serious medical complications within 90 days. Only 99 out of 1477 (7%) of patients had linked PROMs which precluded subsequent analysis. Overall, higher volume surgical units had lower rates of early re-revision following the first RevKR for infection. We were unable to provide recommended specific volume thresholds for units; however, the probability of re-revision appears to be lowest in the highest volume units. Level III, retrospective cohort study of prospectively collected data.

Results of development and experimental studies of the storage complex with the energy storage unit SPENE-1

The RELEVANCE of the study is to develop a controlled storage complex (SC) of electrical energy for modern distributed energy based on advanced innovative technologies. OBJECTIVE. To conduct experimental studies, confirm the main technical solutions incorporated into the SC design and develop design and technological documentation (DTD). METHODS. When solving the tasks, experimental methods were used to confirm theoretical studies and calculations of SC units and elements, implemented by MatLab®. RESULTS. The article describes the relevance of the topic, the ways and stages of developing the SPENE-1 SC energy storage device. The work was carried out in three stages. At the first stage, the functional structure of the storage complex, a mathematical model of the storage complex operation as part of an electrical network, methods for calculating parameters and controlling energy flows in electrical networks of various topologies, as well as the ECD for an experimental sample of the storage complex and the control system were developed. At the second stage, an experimental sample of the storage complex control system was manufactured and the experimental sample of the storage complex control system was adjusted and tested. A program and methodology for testing an experimental sample of the storage complex, including the control system, have been developed; methodological materials for conducting comprehensive tests of experimental samples and working documentation for upgrading the test facility have been prepared. At the third stage of the work, the following was performed: a) assembly, adjustment and testing of the NC for conducting joint comprehensive tests of experimental samples; b) determination of the optimal parameters of the NC, the main characteristics and the range of their changes; c) experimental tests of the critical parameters of the superconducting suspension of the rotor-flywheel of the experimental sample of the storage complex and its control system; d) experimental comprehensive tests of experimental samples; e) adjustment of the KTD based on the results of the manufacturing technology and experimental comprehensive tests. CONCLUSION. The results obtained in the work are of great importance for the development of distributed energy in the Russian Federation. The ECD of the storage complex and the test base, as well as regulatory documents for the operation of the storage complex in various power supply systems have been developed. The NC can be used in traction electric systems of electric transport, distributed electric networks with alternative power sources. The application of the developed NC is especially effective in power supply systems operating in low temperature conditions, for example, in the Arctic and space.

Dynamic coupling of qualitative–quantitative models for operation of water resources based on environmental criteria

Concerning issues include the distribution of scarce water resources, the quality of utilized water, environmental repercussions, and regulations for the sustainable use of water resources. In the management of water resources, optimal qualitative–quantitative exploitation of surface water bodies is regarded as a desirable strategy. The Dez River surface water system from the Dez regulatory dam to Band-e-Ghir is selected in the current paper to create a qualitative–quantitative model that can determine the best exploitation strategies. A dynamic linkage between qualitative and quantitative models is built in order to simulate the current exploitation conditions under the umbrella of the best-case scenario. In this coupled system, hydraulic relationships are established between all of the system’s components. The available data are shared between two models in this structure to simulate the qualitative and quantitative effects of surface water. Then, a new structure is produced to derive the best policies for exploiting the dam and the river by connecting the multi-objective particle swarm optimization algorithm with the qualitative–quantitative coupled model body. The monthly river environmental demand is one of the decision variables in the ideal scenario, and the goals include boosting the percentage of supply demands and minimizing the violation of quality standards. The best-case scenario’s implementation increases the likelihood that all plain demands will be met, regardless of priority. Furthermore, in comparison with the reference scenario, the results of the optimal scenario show that not only are the concentrations of contaminants and qualitative parameters increased, but there are also only minimal violations of the quality and pollution standards of the river water in the majority of river points, particularly in the locations of agricultural withdrawals. The findings demonstrate that using the qualitative–quantitative dynamic relationship between water resources and the development of the coupled model using the NSGA-II algorithm allows us to better plan for the appropriate use of existing water resources by taking into account all stakeholders in such a way that, in addition to meeting needs, maintains the river quality close to standard limits throughout the exploitation period. By using this strategy, users will be informed of the negative effects of their actions, as well as the encroachment on river boundaries and associated consequences.

Reducing the CO2 footprint at an LNG asset with replicate trains using operational data-driven analysis. A case study on end flash vessels

Abstract A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

Risk diagnosis model for high-speed rail safety operation in big-data environment

Risk diagnosis model for high-speed rail safety operation in big-data environment

Hydraulic Modeling of Flow-through Piston Confuser Operation in a Circulation Sub Due to a Change in its Geometry

Hydraulic Modeling of Flow-through Piston Confuser Operation in a Circulation Sub Due to a Change in its Geometry

Multi-state load model for multiple hydrogen electrolysers operation in the power grid

Multi-state load model for multiple hydrogen electrolysers operation in the power grid

Research on the Practical Teaching Model of Hotel Simulation Investment and Operation with the Integration of Industry and Education in Tourism Majors of Higher Vocational Colleges

This study focuses on the training of talents in tourism majors at vocational colleges. Addressing the existing issues in the current education system, such as the disconnect between talent training mechanisms and the demands of frontline production and services, and the overemphasis on theoretical teaching, this paper delves deeply into the necessity and implementation pathways of industry-education integration. Innovatively, a hotel simulation investment operation practice teaching model is proposed. Through five key transformations, namely, integrating concentrated internships into full-process practice teaching, transitioning from a single service position to multi-faceted practices in simulated hotel operations, shifting from repetitive low-level skills to comprehensive training in simulated investment and operations, moving from the separation of industry and education to their integration and promoting industry through education, and transitioning from formalized internship assessments to assessments based on job operation standards, a comprehensive upgrade of the teaching model has been achieved. This model offers valuable insights for the integration of industry-education reforms in tourism management majors at higher vocational colleges.

A bi-objective Genetic Algorithm for flexible flow shop scheduling: A real-world application in the electrical industry

The electrical sector forces manufacturing companies of electrical solutions to continually innovate and implement new processes for greater efficiency. The growing demand for electrical energy, as well as the need to adapt to hybrid operations that combine multi-project operation models with continuous production models, requires efficient workflow management. Accordingly, this article proposes a Genetic Algorithm (GA) approach for solving the scheduling problem in a Flexible Hybrid Flow Shop (FHFS) environment considering a transfer batch approach to minimize makespan and total tardiness. The approach is inspired by a real-world application in the electrical industry and also accounts for unrelated parallel machines, precedence, release times, and due dates for jobs at each production center as key constraints. Three real-data scenarios were generated and evaluated. In the first scenario, a 7 % improvement in makespan was observed compared to real execution times. In Scenario 2, the makespan improved significantly by 33 %, and only 17.4 % of jobs were delayed, compared to 96 % in the real data. Likewise, GA showed a lightly better performance over Tabu Search (TS) in 3.01 % for makespan while the delayed jobs found by GA were 25 % below those obtained by TS. These results highlight the potential of the proposed method to improve overall production efficiency, not only in the electrical sector but also in similar industries.

Model for Joint Operation of Multi-Energy Systems in Energy and Frequency Regulation Ancillary Service Markets Considering Uncertainty

Model for Joint Operation of Multi-Energy Systems in Energy and Frequency Regulation Ancillary Service Markets Considering Uncertainty

Thermoeconomic analysis in solar collector fields: a focus on constant flowrate and variable flowrate models

In this study, the operation models with constant flowrate and variable flowrate of a flat-plate solar collector field are analysed. The model that achieves greater energy utilization and lower operating costs was determined under two conditions: when scaling fouling occurs in the collector tubes and when a clean operation (without fouling) is considered. The case study involves a pasteurization plant operating at temperatures above 85°C. The operational scenarios are as follows: 1) Variable flowrate: the flowrate is adjusted based on environmental conditions to maintain a constant outlet temperature, and 2) Constant flowrate: the flowrate remains constant, resulting in varying collector outlet temperatures depending on weather conditions. The results show that under clean conditions, operating with variable flowrate yields a lower cost per kWh of captured energy ($0.048/kWh). In the presence of fouling, the cost of operating with variable flowrate significantly increases to $0.15/kWh, while constant flowrate results in the lowest cost ($0.077/kWh).

Mathematical modelling of the process line of a multifunctional fuel gas preparation unit (MGPU) to improve its accident-free operation

The purpose of the research is a comprehensive study of the process line of a multifunctional fuel gas preparation unit (MGPU), creation of a mathematical model capable of predicting and controlling gas dryness in the process of MGPU operation, as well as determination of optimal parameters of the unit operation to improve accident-free operation and high productivity of the equipment using treated gas.Methods. Mathematical modeling with the use of multiple regression model for prediction of dew point temperature and its influence on accident-free operation of the plant was carried out. The adequacy of the model was confirmed by the coefficient of determination and Fisher's criterion. An analysis of the limiting factors for the accident-free operation of the MGPU, including temperature, pressure and gas composition, is also presented. The accuracy of numerical modeling of the trouble-free operation of the MUPG process line based on the developed model of multiple regression of gas dryness was estimated using paired correlation coefficients and elasticity coefficients. Results. In the course of the work, the accident-free operation of the process line of MGPU was modeled based on the developed multiple regression model of gas dryness. The following interpretation of the model parameters is possible: increasing factor X1 by 1 leads to an average decrease in Y by 0.279; increasing factor X2 by 1 leads to an average increase in Y by 0.46; increasing factor X3 by 1 leads to an average increase in Y by 0.000418; increasing factor X4 by 1 leads to an average increase in Y by 13.288; increasing factor X5 by 1 leads to an average decrease in Y by 13.337; increasing factor X6 by 1 leads to an average decrease in Y by 0. According to the maximum coefficient β2=0.384 we can conclude that the factor X2 has the greatest influence on the result Y. Statistical significance of the equation was tested using the coefficient of determination and Fisher's criterion. According to the assessment of the accuracy of numerical modeling of the trouble-free operation of the MUPG process line, based on the developed model of multiple regression of gas dryness, a strong linear relationship between X1 and Y, a low linear relationship between X2 and Y, a low linear relationship between X3 and Y, a moderate linear relationship between X4 and Y, a moderate linear relationship between X5 and Y, moderate linear relationship between X6 and Y.Conclusion. It was found that in the investigated situation: "in the possibility of prediction and control of gas dryness: justification of the hypothesis about the possibility of prediction of dew point temperature and its influence on accident-free operation" the parameters of the model are statistically significant.

Risk performance analysis model of escort operation in Arctic waters via an integrated FRAM and Bayesian network

Escort operation is an effective mean to ensure the safety of ship navigation in the Arctic ice area and expand the window period for ship navigation. At the same time, the operation mode between icebreaker and escorted ship may also causes collision accident. In order to scientifically reflect the complex coupling relationship in the escort operation system in Arctic waters and effectively manage the navigation risks. This study proposes to use the functional resonance analysis method (FRAM) to identify the risk factors of ship escort operation in Arctic waters, and uses the Bayesian network (BN) method to establish a risk assessment model for escort operation collision accident. The cloud model is used to process the uncertain data information. The proposed method is applied during the actual escort operation of a commercial ship on the Arctic Northeast Passage. According to the model simulation results, the risk performance of ship escort operation in Arctic waters is quantitatively analyzed, and the key risk causes are further analyzed. This study has positive significance for better understanding the risk evolution mechanism of ship escort operation in Arctic ice area and helping relevant management departments to take risk control measures.

Operation Modeling Within the Framework of Life Test Optimization for Gas Turbine Engine Based on Life Cycle Model

The choice of parameters for the life tests of gas turbine engines is a complex multi-criteria task. In this article, as a tool for solving this task, it is proposed to use simulation modeling of the engine life cycle to select gas turbine engine life test parameters. A three-level system for collecting and processing operational data is considered. It is suggested to combine big data and simulation technologies for engine diagnostics, forecasting, and refining the operation model based on aggregated data on aircraft schedules, flight geography, and weather conditions. Special attention is paid to the choice of a method for modeling the operation of aircraft engines. The authors have developed a number of possible methods for a modeling operation within the framework of the engine life cycle model. The results of a comparative assessment of the proposed methods are presented using the example of choosing the parameters of resource tests of an auxiliary power plant. The analysis of the operation is carried out.

Daily Engine Performance Trending Using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and functional principal component analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm (GA)-assisted orthogonal matching pursuit (OMP) for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.