Operation Optimization Research Articles

Strategi Bisnis Unit dalam Pendekatan SWOT untuk Meningkatkan Daya Saing di Pasar Global

This research aims to analyze the business unit's strategy in facing dynamic market competition. The focus of the research is to identify internal and external factors that affect the performance of the business unit and evaluate the effectiveness of the strategies implemented. The research used a qualitative approach with a case study method on one of the business units in a particular sector, such as manufacturing or technology. Data was collected through in-depth interviews with business unit managers, analysis of financial statements, and literature review. The results showed that the strategies of product differentiation, operational optimization, and adaptation to technology were key elements to improve the competitiveness of the business unit. SWOT analysis revealed key strengths, such as product innovation capabilities, but also indicated threats from the emergence of new competitors. The study emphasizes the importance of synergy between strategic vision, innovation, and efficiency in supporting business sustainability. The study provides strategic recommendations for other business units looking to improve their competitiveness in the global market. The findings suggest that the right strategy can help businesses be more adaptive to market dynamics and maintain relevance in an increasingly competitive era.

Fault Diagnosis and Optimized Operation and Maintenance of Drop-out Fuses in Alpine Regions Using MKL-SVM and GRU

Extreme climate in alpine regions leads to frequent failures of drop-out fuses, but existing methods have low detection accuracy and response speed. This article combines MKL-SVM (Multiple Kernel Learning-Support Vector Machine) and GRU (Gated Recurrent Unit) model to achieve precise fault diagnosis of drop-out fuses, leveraging their efficient processing of high-dimensional time series data. Sensors are used to collect data such as voltage, current, temperature, and wind speed. Based on GRU, global trends, temporal hidden states, and dynamic nonlinear features of the data are extracted as inputs for subsequent MKL-SVM. In MKL-SVM, a linear kernel function is used to simplify the linear relationship between static features; targeting temporal features, RBF (Radial Basis Function) kernel effectively captures dynamic trends, fluctuations, and periodic changes in temporal features. To further enhance the classification ability of the model, the kernel function parameters, including linear kernel weights and RBF kernel widths, are adjusted through cross-validation. Finally, based on the model diagnosis results and predicted data, an optimized operation and maintenance plan is formulated to reduce maintenance costs and improve equipment operating efficiency. The results show that the fusion model can significantly improve the reliability of fault detection and adapt to the unique environmental challenges of alpine regions. The accuracy of fault diagnosis for drop-out fuses is 95%, with an MSE (mean square error) value of about 0.15 and a maintenance cost reduction of about 21.89%. This method can achieve accurate fault diagnosis and efficient optimization of operation and maintenance under extreme weather conditions.

Overseas Expansion of Chinese Winter Sports Science and Technology Enterprise Taking Love Ski as an Example

With the vigorous development of Chinese winter sports, the international expansion of science and technology enterprises has become an important way to enhance the competitiveness of the industry. From the perspective of the current market situation, Europe accounts for 45% of the global ski market but faces challenges such as climate warming, rising costs, and sharp decline in tourists, while North America accounts for 25% and remains stable. The Asia-Pacific region accounts for 20%, and China has the fastest growth. This market landscape provides opportunities for Chinese enterprises, especially in the output of intelligent solutions and operational optimization services. However, enterprises still need to deal with challenges such as market uncertainty, cultural differences, and legal compliance. In response to these challenges, enterprises need to increase technology research and development, establish a cross-cultural service system, build overseas branches, expand marketing channels, and improve business models. Taking Love Ski Sports Development Company Limited (Love Ski) as an example, this paper discusses the pathway for enterprises to achieve internationalization through strategies such as technological innovation, cross-cultural services, and localized operations, and provides a practical reference for the industry.

Critical success factors for rail infrastructure joint ventures

Due to their myriad socioeconomic and environmental benefits, Joint Ventures (JVs) between construction contractors are considered a highly effective commercial strategy for delivering construction projects. This is especially true in the rail infrastructure industry. While rail infrastructure JVs (RIJVs) between several contractors may not always run smoothly, there have also been cases of successful implementation; nevertheless, the factors contributing to their success have not been comprehensively explored. It is crucial to thoroughly understand the critical success factors (CSFs) for RIJVs, as their success depends on the combination of various key factors during their implementation. This study aims to evaluate the CSFs for RIJVs. Through a comprehensive literature review, 20 CSFs for RIJVs were identified, and an expert survey was conducted with 40 UK-based RIJV professionals. The data were analysed using the relative importance index and Spearman’s rank correlation analysis. The top five perceived CSFs were communication, compatibility of partner objectives, suitable JV partner selection, trust, and effective planning. This suggests the level of importance attached to these CSFs by the professionals working in and managing RIJVs. Spearman’s rank correlation analysis revealed significant interdependencies among CSFs, such as the combination of trust with communication and the intent to learn with the level of participation and involvement, suggesting that these CSFs often go hand in hand. Further analysis of challenges encountered on RIJVs revealed four key themes: information management and IT systems integration, alignment, congruency and synergy, inter-organisational dynamics, and strategic management. Solutions proposed for these challenges also fell into four primary themes: strategic management and leadership, collaboration and communication, risk management and conflict resolution, and operational efficiency and resource optimisation. The findings showed a comprehensive evaluation of CSFs for RIJVs, highlighting pathways to effective JV execution. This study makes a valuable contribution to the existing body of knowledge on RIJV by analysing the CSFs. It also offers practical insights that can enhance the successful implementation of RIJV projects and provide guidance for top team managers in allocating strategic resources to key areas.

KSP-SCOS: new Karp-Steele patching based streamlined customer order scheduling approach

Customer order scheduling (COS) encapsulates one of the most intricate problems in operational optimization, primarily due to the multifaceted interplay of constraints such as temporal dependencies, resource limitations, and cost minimization. This paper introduces the Karp-Steele Patching for Streamlined COS (KSP-SCOS), a cutting-edge framework designed to address these complexities with unparalleled precision and efficiency. By incorporating the dynamic problem decomposition capabilities of Karp-Steele Patching, KSP-SCOS enhances the scheduling landscape through systematic exploration of feasible solutions while eliminating suboptimal paths through strategic pruning. The proposed approach was subjected to rigorous testing across diverse datasets representing a broad spectrum of real-world operational scenarios. These scenarios range from minimally constrained environments to those characterized by extensive resource contention and tight scheduling windows. Experimental findings reveal that KSP-SCOS consistently delivers near-optimal solutions, demonstrating robust scalability and significant reductions in computational overhead, regardless of the problem complexity. This research makes several noteworthy contributions to COS optimization : (1) The integration of the Karp-Steele Patching method within COS, ensuring a systematic yet adaptable solution process. (2) An empirical analysis across datasets of varying complexity, illustrating the algorithm’s effectiveness in balancing computational efficiency and scheduling quality. (3) An exploration of cost-time trade-offs, shedding light on the practical implications of for resource allocation in dynamic operational settings. The adaptability of KSP-SCOS establishes it as a groundbreaking for industries demanding dynamic, accurate, and resource-efficient scheduling strategies. Its capability to seamlessly adjust to evolving operational constraints underscores its critical relevance in scenarios requiring immediate and responsive decision-making. This study marks a pivotal advancement in resolving the pressing challenges of modern COS, paving the way for elevated operational efficacy and adaptability in increasingly complex environments.

A novel multi-task algorithm for operational optimisation of coal mine integrated energy system under multiple uncertainties

A novel multi-task algorithm for operational optimisation of coal mine integrated energy system under multiple uncertainties

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

A photovoltaic-storage system configuration and operation optimization model based on Model Predictive Control

Advances in plastic to fuel conversion: reactor design, operational optimization, and machine learning integration

Plastic waste management is a pressing global problem that requires sustainable solutions to mitigate environmental harm.

Enhancing hydraulic fracturing efficiency through machine learning

Hydraulic fracturing (HF) is a technique employed in the oil and gas industry to extract hydrocarbon resources from shale formations and low-permeability rocks. This process involves the creation of new fractures and the extension of existing ones within the rock by injecting a high-pressure fracturing fluid, which enhances the flow of hydrocarbons. To assess the efficiency and safety of HF, various factors such as fracture orientation, geometry, length, distribution, and stimulated reservoir volume are analyzed. In the complex field of HF, the accurate assessment of fracture parameters is crucial for optimizing operational efficiency and ensuring environmental safety. This study investigates the role of machine learning (ML) methodologies in enhancing the precision of these assessments, with a particular focus on fracture width a critical factor in the effectiveness of HF. By utilizing a comprehensive dataset from hydrocarbon fields, in this study, the application of several advanced ML models was investigated, including Artificial Neural Networks (ANNs), Random Forest (RF), and K-Nearest Neighbors (KNNs). These models were specifically employed to predict and assess fracture width based on a variety of geological and operational parameters: geometric factors (γ), Shear Modulus (G), Poisson’s ratio (ν), viscosity of the fracturing fluid (µ in centipoise), crack height (h), fluid efficiency (η), injection time (t), and crack length (x). This study evaluated the performance of ANN, RF, and KNN models, achieving accuracies of 0.978, 0.979, and 0.893, respectively, which underscores their strong predictive modeling capabilities. The results were meticulously documented for each methodology. The RMSE values for ANN, KNN, and RF were 7.552, 15.711, and 6.194, respectively. Notably, the RF approach demonstrated superior performance with an RMSE of 6.194, establishing it as the most accurate method. This study highlights the transformative potential of ML in the pre-stimulation planning phase of HF, enabling enhanced real-time decision-making and operational optimization, which ultimately results in more effective and efficient fracturing operations.

Economic optimization for the dynamic operation of a grid connected and battery-supported electrolyzer

Economic optimization for the dynamic operation of a grid connected and battery-supported electrolyzer

Design and optimization of power grid intelligent operation and maintenance system based on blockchain and industrial IoT: a case study of State Grid Information & Telecommunication Co., Ltd.

Design and optimization of power grid intelligent operation and maintenance system based on blockchain and industrial IoT: a case study of State Grid Information & Telecommunication Co., Ltd.

Optimisation of high-rate filamentous algal pond operating parameters for nutrient bioremediation of primary municipal wastewater

Effective management of operational parameters is crucial for optimising wastewater treatment in high-rate filamentous algal pond (HRFAP) systems. This study examined three key operational parameters - hydraulic retention time (HRT), stocking density, and harvest frequency - on the growth and nutrient bioremediation efficiency of Klebsormidium flaccidum cultivated in primary municipal wastewater in outdoor HRFAPs in summer and winter. Seasonal conditions significantly influenced biomass productivity, with productivity being 48.3 % higher in summer compared to winter across all experiments. The optimal HRT of 4 days for both seasons achieved the highest average reductions in total ammoniacal‑nitrogen (TAN) concentration (64.6 % day−1 ± 1.82 SE in summer, 32.3 % day−1 ± 1.82 SE in winter) and acceptable reductions in nitrate-N (66.6 % day−1 ± 3.54 SE in summer, 42.6 % day−1 ± 8.98 SE in winter) and dissolved reactive phosphorous (DRP, 19.8 % day−1 ± 1.01 SE in summer, 15 % day−1 ± 1.98 SE in winter). A stocking density of 0.25 g fresh weight (FW) L−1 was optimal in summer as it resulted in the highest reductions in TAN (75.9 % day−1 ± 2.49 SE), nitrate-N (43.8 % day−1 ± 3.31 SE), and DRP (21.6 % day−1 ± 1.37 SE). In winter, a stocking density of 0.5 g FW L−1 was optimal to mitigate the risk of primary wastewater toxicity during slower growth periods. Harvest frequency did not significantly affect nutrient removal rates across treatments and seasons; however, biomass productivity was significantly higher in summer with a 4-day harvest frequency (7.83 g dry weight (DW) m−2 day−1). Longer HRTs improved water quality variables, with the highest Escherichia coli (E. coli) reduction (99.9 %) observed with a stocking density of 0.25 g FW L−1 in winter. This study highlights the importance of seasonal optimisation of HRFAP operation to maximise biomass production and nutrient bioremediation for effective treatment of primary municipal wastewater.

On the economic feasibility of tidal range power plants

The potential energy associated with tides presents a sustainable energy resource that remains largely untapped. Uncertainties on the economic case of tidal range power plants are a known obstacle. Research on tidal range structures suggests energy yield may be maximized through operation strategy optimization, and that impacts can be mitigated through design optimization. While instructive, these perspectives alone are insufficient to support the feasibility of individual projects. We integrate operation optimization and hydrodynamic impact analyses within a cost evaluation framework for tidal range structures focusing on capital costs (CAPEX) and levelized cost of energy (LCOE). Once benchmarked against 11 historic proposal cost projections, we perform a redesign of 18 tidal power plants to deliver a comprehensive comparative basis across a diverse range of sites in the UK. Tidal power plant operation is simulated in regional shallow-water equation models, acknowledging tide variability. The cost evaluation framework demonstrates the impact of geospatial variations on key cost components. The redesign process indicates transformative implications in that equivalent and lower LCOE values can be achieved for designs at a substantially lower CAPEX. Given how the latter hinder development, we show how tidal range schemes could be far more economically feasible than commonly perceived.

Capacity configuration and operational optimization of hybrid concentrating solar power and photovoltaic system by evaluation of solar energy

Capacity configuration and operational optimization of hybrid concentrating solar power and photovoltaic system by evaluation of solar energy

PARMENIDES – ideal voltage sensory placement for battery storage operation optimization

PARMENIDES – ideal voltage sensory placement for battery storage operation optimization

What-if: A causal machine learning approach to control-oriented modelling for building thermal dynamics

Operational optimization of buildings can improve efficiency, and reduce costs and emissions. This optimization typically relies on a model of the building thermal dynamics, which is used by a predictive controller to obtain optimal schedules for heating, ventilation and air conditioning. This model has historically been constructed by domain experts using physical properties of the building. However, this approach scales poorly as more and more residential and commercial buildings need to be modelled and controlled. As a consequence, researchers and practitioners have turned to data-driven models, trained only on observational data. However, this alternative is no panacea: such models often fail to generalize to truly unseen conditions due to their inability to learn cause–effect relationships - i.e. they are not causal, rather they only learn correlational associations between input and target variables. In this paper, we demonstrate this problem using classical machine learning models trained on data from two different use cases (a simulated RC building and nine real-world Dutch buildings). Our results show that, unlike commonly used data-driven methods, causal machine learning (CML) algorithms trained on debiased data can produce accurate models necessary for control-oriented applications which outperform baseline models by over 40%, besides learning the correct causal associations which we verify using a custom testing environment as well as SHAP feature analysis. These results emphasize the need to move beyond simplistic data-driven methods if control-oriented applications are to be realized in a feasible manner.

Robust operation optimization model for building integrated energy system with demand response mechanism and weighting of consumption responsibility

Robust operation optimization model for building integrated energy system with demand response mechanism and weighting of consumption responsibility

A detailed MILP model and an ad hoc decomposition algorithm for the operational optimization of gas transport networks

A detailed MILP model and an ad hoc decomposition algorithm for the operational optimization of gas transport networks

Applicaton Of Mathematics In Big Data Analysis To Support Strategic Decision

This study aims to investigate the application of mathematical models in big data Analysis and their impact on strategic decision making in various industrial sectors. Using a quantitative approach to the survey, data was collected from 190 respondents from the technology, finance, manufacturing and healthcare sectors. The results showed that the application of mathematical models, such as predictive algorithms and machine learning, contributed significantly to improving the quality of strategic decisions. The study also identified that variables such as human resource competence and technological infrastructure moderate the relationship between big data Analysis and effective decision-making. The technology and finance sectors have proven to benefit the most from the application of math-based big data Analytics, with benefits seen in improved market prediction, risk management, and operational optimization. The findings underscore the importance of integrating mathematical models in data analysis to support data-driven decision-making in the digital age.

Automated Object Detection and Tracking for Construction Site Safety

The construction industry is increasingly adopting digital solutions to enhance safety, efficiency, and productivity. This project leverages the YOLOv8 object detection model and ByteTrack algorithm to track and count objects on construction sites. The system enables automated monitoring of personnel, machinery, and safety equipment through video analysis, addressing critical challenges like occlusions and dynamic object interactions. A custom dataset tailored for construction environments ensures high accuracy in detecting safety-critical items, such as personal protective equipment (PPE). This approach demonstrates significant potential for real-time safety assessments and operational optimization, serving as a technological leap in construction practices.