Optimal Cost Research Articles (Page 1)

This article develops inverse reinforcement learning (IRL) control algorithms for nonlinear networked control systems (NCSs) to mimic trajectories of a target system governed by an unknown optimal cost function, despite the presence of random data dropouts and external disturbances. Data dropouts occur during: 1) reception of target trajectory data by the controller; 2) reception of state feedback data by the controller; and 3) reception of control input data by the actuator. By organically integrating $H_{\infty } $ control to account for disturbances and dropout-induced uncertainty, a model-based IRL algorithm is first developed. Building on this, a neural-network-based data-driven IRL algorithm is developed to infer the cost function and optimal control policy using available data while reducing dependence on system models. The proposed methods enable effective trajectory imitation under partial model knowledge, data dropouts, and disturbances, as demonstrated through simulation studies.

In modern manufacturing, delivering high-quality products at minimal cost is essential for maintaining competitive advantage. This study combines statistical hypothesis testing with cost-strategy optimization to design a low-cost sampling and testing scheme that effectively controls defect rates at multiple production stages. First, under the null hypothesis that the defect rate does not exceed the nominal value, In this article,calculate required sample sizes of 98 at 95 % confidence and 59 at 90 % confidence. In this article,then apply normal distribution theory to determine appropriate sampling frequencies, ensuring test accuracy across confidence levels. Second, by constructing a cost matrix and sampling-quantity vector, In this article,evaluate inspection strategies at both the procurement (spare parts) and production (finished product) stages. In this article,find that foregoing spare-parts inspection in favor of finished-product testing significantly reduces inspection costs and market-exchange losses while maintaining quality standards. Specifically, this approach minimizes the number of original-parts inspections yet preserves finished-product integrity, yielding substantial cost savings and lower risk exposure. Overall, our framework offers manufacturers a theoretically grounded, practically feasible method for defect-rate control and inspection-cost optimization.

This paper analyzes a finite-capacity GI/M/2/N queue with two heterogeneous servers operating under a multiple working-vacation policy, Bernoulli feedback, and customer impatience. Using the supplementary-variable technique in tandem with a tailored recursive scheme, we derive the stationary distributions of the system size as observed at pre-arrival instants and at arbitrary epochs. From these, we obtain explicit expressions for key performance metrics, including blocking probability, average reneging rate, mean queue length, mean sojourn time, throughput, and server utilizations. We then embed these metrics in an economic cost function and determine service-rate settings that minimize the total expected cost via the Bat Algorithm. Numerical experiments implemented in R validate the analysis and quantify the managerial impact of the vacation, feedback, and impatience parameters through sensitivity studies. The framework accommodates general renewal arrivals (GI), thereby extending classical (M/M/2/N) results to more realistic input processes while preserving computational tractability. Beyond methodological interest, the results yield actionable design guidance: (i) they separate Palm and time-stationary viewpoints cleanly under non-Poisson input, (ii) they retain heterogeneity throughout all formulas, and (iii) they provide a cost–optimization pipeline that can be deployed with routine numerical effort. Methodologically, we (i) characterize the generator of the augmented piecewise–deterministic Markov process and prove the existence/uniqueness of the stationary law on the finite state space, (ii) derive an explicit Palm–time conversion formula valid for non-Poisson input, (iii) show that the boundary-value recursion for the Laplace–Stieltjes transforms runs in linear time O(N) and is numerically stable, and (iv) provide influence-function (IPA) sensitivities of performance metrics with respect to (μ1,μ2,ν,α,ϕ,β).

The excited-state properties of metal nanoclusters have attracted considerable attention for potential applications in optoelectronics and biomedicine. However, the theoretical exploration of these properties, particularly emission mechanisms, remains challenging due to the high computational cost of excited-state structure optimization. Herein, we investigate the geometric and electronic structural changes upon photoexcitation in the magic series gold nanocluster Au8n+4(SR)4n+8 (R = H or phenyl, n = 3-6) using time-dependent density functional theory (TDDFT) and its approximate variant, DFT plus tight binding (TD-DFT+TB). Our results demonstrate that approximate methods, which combine full DFT-ground-state descriptions with the tight-binding approximation in linear-response calculations, offer a cost-effective and reliable approach for predicting size and ligand effects on the excited-state properties of gold nanoclusters. Key parameters such as the Stokes shift, charge-transfer character, and energy gap between the first singlet (S1) and triplet (T1) states show strong dependence on cluster size and the nature of the ligand shell, and these trends are well captured by the approximate methods. These results emphasize their potential for the efficient design of gold nanoclusters with tailored optical functionalities.

This paper explores the extent and form of the minimal intervention concept, essential for the adaptive reuse of buildings and spaces, within 21st-century architecture. It focuses on European case studies from the last 15 years. By its nature, minimal intervention has the potential to reduce the environmental impact of construction, support recycling and upcycling processes and address current architectural challenges in Europe and globally. The paper presents the diverse perceptions and applications of the concepts of "minimum" and "almost nothing" in architecture throughout history. This ranges from pragmatic approaches to minimalism, such as cost optimization during crisis periods, through the understanding of minimalism as an architectural movement associated with Mies van der Rohe's "less is more" adage, to Burckhardt's considerations of preserving existing architecture without intervention. Based on an analysis and comparison of three European case studies, the paper describes what constitutes the "minimum" in each, across their entire lifecycle from design to eventual disappearance. For the case study of "Elektrický anjel," a theatrical play situated in an old boiler room in Bratislava, the minimum was achieved by maximizing the utilization of the old boiler room's character, condition, and genius loci. Into this space, a self-supporting, reversible structure defining a provisional stage was inserted. In the exhibition "The Exciting Mysterious Aquarium" by Andrej Dúbravský, the minimum was achieved by introducing art into an unfinished metro depot in Bratislava. The new gallery function primarily emerged from a programmatic redefinition of this raw, abandoned space, into which Dúbravský's paintings were placed with minimal physical intervention (solely by mounting them on walls and providing provisional lighting). The exhibition demonstrated how a subtle intervention can yield significant spatial and conceptual impact. "The Theatre of the useFULL," presented at the 16th Venice Architecture Biennale, featured a pavilion with integrated projection within an old industrial hall. The intervention was constructed from materials and pieces of furniture chosen by the architects in collaboration with local initiatives supporting the homeless. After the Biennale, the temporary theater was dismantled, and all elements were donated to the initiatives that needed them. Almost nothing was left unused from "The Theatre of the useFULL," a core principle conceived from its very inception. These analyses suggest that minimal interventions represent a multifaceted approach, offering relevant and responsible solutions that address contemporary architectural demands such as sustainability, economic efficiency, recycling and upcycling, the elimination of new construction, and the overall minimization of material, costs, and energy. Minimal intervention emerges as a viable alternative to the often complex and resource-intensive structural changes commonly associated with architectural conversions.

In the service sector, accounting information systems face growing risks in data security, unauthorized access, and fraud. Strong internal control and risk management are essential for efficiency and trust. This study proposes an intelligent accounting information system with four modules: (1) data collection for financial and operational data completeness; (2) information encryption using Advanced Encryption Standard with 256-bit key in Galois/Counter Mode (AES-256-GCM), Rivest-Shamir-Adleman (RSA), and Elliptic Curve Digital Signature Algorithm (ECDSA); (3) real-time risk assessment based on probability and impact; and (4) decision support comparing linear regression, support vector machines, and artificial neural networks for cost prediction. Results show the artificial neural network achieves the highest accuracy and is adopted for cost optimization and budgeting. The system enhances security, enables proactive risk management, and supports data-driven decisions.

Water quality management lies at the intersection of environmental protection, resource sustainability, and public health. This comprehensive review synthesizes global research on integrated approaches to water quality assessment, monitoring, and treatment. It examines the fundamental physical, chemical, and biological parameters governing water quality and the use of Water Quality Indices (WQI) as a unified tool for evaluation and policy communication. Major pollution sources—agricultural runoff, industrial discharge, and domestic waste—are explored alongside emerging contaminants such as pharmaceuticals and microplastics. The paper highlights advances in real-time monitoring using IoT-enabled sensors, remote sensing, and machine learning for predictive analysis. Treatment technologies ranging from conventional coagulation and filtration to advanced membrane, adsorption, and biological systems are reviewed, emphasizing innovations in nanotechnology and advanced oxidation processes. Design and management strategies for modern water treatment plants (WTPs) incorporate automation, sustainability, and cost optimization frameworks. Key challenges include technical, economic, and regulatory barriers, as well as the exacerbating impacts of climate change and inequities in developing regions. Future directions focus on renewable energy integration, circular economy principles, and water reuse. The study concludes that sustainable water management demands an adaptive, interdisciplinary, and technology-driven framework supported by robust policy, public participation, and global cooperation.

To ensure the transition to electric transport in order to reduce CO2 emissions, a number of projects have been initiated to develop and introduce new types of locally produced vehicles. The Russian Federation market is quite conservative and has its own specifics and a special consumer model. In addition, the component base of localized components for electric vehicles is relatively small, which is justified by relatively low demand and market volumes. To create the concept of a Class C passenger vehicle with electric traction, marketing research was conducted in a group of people who are potentially ready to abandon traditional vehicles and choose electric vehicles or hybrids. The purpose of the study is to evaluate the opinion of consumers and to form the technical characteristics of a Class C hybrid car based on localized components. Methods: To obtain the results, various components of the power unit were modeled, and a balanced solution was found that meets the requirements of consumers in the region. Mathematical modeling was carried out and analytical studies of the power reserve of various configurations of power units within the WLTC cycle were carried out in the digital environment of Siemens Amesim. Analytical work on the study of the composition of cars for calculating the masses of modern components and aggregates was carried out using Autodatas. Consumer opinions were assessed through a survey using the Yandex. Forms service. The relevance of the study is confirmed by the lack of domestic models of sequential hybrids on the market and the lack of similar studies, based on the opinion of a potential consumer. The result is the technical parameters of the main components and assemblies, which should ensure the optimal cost of the final product and meet the requirements of the consumer. Conclusion: As a result of the study, a concept of a combined sequential-type power unit was developed based on available components, meeting the main consumer properties, and the technical characteristics of the components were presented.

In this paper, we develop a posynomial programming cost optimization model for road construction project and other construction works in the construction industry. The developed model was applied on 8.1 km road project with the optimal construction cost of 7,906,041.36 naira. The primal design variables and their contributions to the optimal construction cost were determined. The application of the model in the construction industry is supposed to help the contractors and management on cost optimization and improve efficiency.

This research investigates the synergistic relationships between Green Supply Chain Management (GSCM) practices and product innovation in marketing performance and organizational sustainability within Thailand’s processed food industry. Building upon Resource-Based View theory and Stakeholder Theory, this study addresses a critical gap in understanding how environmental practices interact with innovation strategies to create sustainable competitive advantages in emerging markets. The research employs a comprehensive mixed-methods approach, integrating qualitative insights from industry expert interviews with quantitative analysis through Structural Equation Modeling (SEM). Primary data were systematically collected from 300 strategically selected enterprises representing small (≤50 employees), medium (51–200 employees), and large-scale (>200 employees) operations across diverse product categories within Thailand’s processed food sector. The analytical framework examines three core GSCM dimensions—green purchasing, green production, and green distribution—alongside three innovation aspects—quality innovation, safety innovation, and sustainability innovation. Eleven hypothesized relationships were rigorously tested to examine direct and indirect effects on marketing performance indicators (sales growth, market share expansion, brand enhancement, customer satisfaction, and cost optimization) and organizational sustainability metrics (environmental impact reduction, regulatory compliance, competitive positioning, and resource efficiency). SEM results revealed that Green Production practices significantly enhance marketing performance (β = 0.16, p < 0.01), demonstrating the strategic value of environmentally responsible production processes in achieving market success. Conversely, Green Distribution exhibited negative effects on both marketing performance (β = −0.106, p < 0.10) and organizational sustainability (β = −0.152, p < 0.05), indicating potential operational trade-offs and infrastructure limitations that require strategic optimization. The model demonstrated excellent fit indices (GFI = 0.929, CFI = 1.000, TLI = 1.000, RMSEA = 0.000, RMR = 0.034), validating the theoretical framework’s robustness. However, modest explanatory power (R2 MP = 0.050, R2 OS = 0.029) suggests that additional contextual factors, firm-specific capabilities, and market dynamics significantly influence these outcomes, warranting future investigation of mediating and moderating variables.

The rapid adoption of electric vehicles (EVs) has made the strategic deployment of charging infrastructure a critical task for sustainable mobility. This study formulates the siting of EV charging stations as a p-median problem and applies two metaheuristic approaches—genetic algorithm (GA) and ant colony optimization (ACO)—to solve it. The cost function, defined as the combination of transportation and installation costs, was analyzed in various scenarios. The results show that ACO consistently outperforms GA, offering lower total costs and shorter solution times. Crucially, the work uses optimization results published in the literature to expand the comparison beyond GA, using GA as a typical baseline. The suggested framework is adaptable and can be used to solve different spatial planning and facility location issues. This paper offers a data-driven, scientifically based approach for EV charging infrastructure development by combining cost effectiveness and service accessibility. In addition to providing decision-makers with useful tactics for creating dependable and sustainable charging networks, it helps handle the temporal and geographical coordination issues in EV charging.

Abstract With the continuous expansion of wind and solar complementary power generation systems, introducing energy storage systems to ensure their stability has become crucial. To solve the high cost in current methods, a wind-solar hybrid energy storage model is established, and a grey wolf pigeon swarm optimization algorithm for capacity optimization is constructed. The performance is compared with other comparison algorithms. The accuracy and recall of the designed method were 98.67% and 97.74%. Moreover, the F1 value, precision, loss value and AUC value of the AOC curve were 97.68%, 96.88%, 0.34, and 0.974, respectively, all of which were superior to the comparison algorithms. Subsequently, to verify the effectiveness, it was compared with other algorithms. The cost for optimizing the capacity in area A was 155,534 RMB, which was below the comparison algorithm. The algorithm has good performance and excellent capacity optimization effect. It helps to reduce the capacity optimization cost and provides theoretical basis for research on capacity optimization.

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support.

A comparative analysis of cost-utility: Chiglitazar vs. sitagliptin in patients with type 2 diabetes in China.

Oily liquid waste emergency management for offshore oil spill response through a factorial inventory-theory-based mixed-integer approach.

Parametric study for cost optimization of hydrogen pipelines in time varying conditions

Monitoring the technical condition of bridge structures is a crucial aspect of their operation, as it enables early detection of damage and optimization of maintenance costs. Thanks to the implementation of monitoring systems, it is possible not only to extend the service life of bridges but also to increase user safety and reduce repair expenses. Recent publications indicate that the use of machine learning (ML) methods in bridge monitoring can significantly enhance the effectiveness and precision of conducted analyses. ML techniques are widely applied in the assessment of technical condition – they allow for automatic anomaly detection, damage classification, and forecasting of the durability of individual structural elements.

Under the tightening carbon reduction policies, port microgrids face the challenge of optimizing the installed capacity of multiple power generation types to reduce operating costs and increase renewable energy penetration. We develop a bi-level cost-optimization framework in which the upper level decides long-term capacities (PV, wind, gas turbine, bio-fuel unit, and battery energy storage), and the lower level dispatches a multi-energy port microgrid (electricity–heat–cold) on an hourly basis with frequency regulation services. To ensure rigor and reproducibility, we (i) move the methodology upfront and formalize all constraints, (ii) provide a dedicated data–preprocessing pipeline for multi-region 50/60 Hz frequency time series, and (iii) map a policy intensity index to a carbon price and/or an annual cap used in the objective/constraints. The bi-level MILP is solved by a column-and-constraint generation algorithm with optimality gap control. We report quantitative metrics—annualized total cost, CO2 emissions (t), renewable shares (%), and regulation cycles—across scenarios. Results show consistent cost–carbon trade-offs and robust capacity shifts toward storage and biofuel as policy tightens. All inputs and scripts are organized for exact replication.

This study examines decision-making in closed-loop supply chains (CLSCs) for power batteries with blockchain-enabled traceability. A bidirectional game-theoretic model in which one traceability level links the forward (sales) and reverse (recovery) sides. The manufacturer leads in a game; the retailer sets price and collection; contract terms are chosen by Nash bargaining. We analyse two implementable mechanisms: R&D cost sharing and sales revenue sharing. The findings show that blockchain supports modest price pass through while increasing market demand and consumer surplus. When traceability costs are high, R&D cost-sharing and sales revenue sharing improve coordination. Bargaining power matters: greater manufacturer power raises feasible shares, while the optimal cost- and revenue-sharing rates decline as price sensitivity and traceability costs rise. These insights provide practical guidance for deploying blockchain in battery CLSCs and support the sustainable growth of the EV industry.

Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances offer scalable computing resources crucial for various applications. Accurate prediction of CPU utilization is essential for efficient resource management and cost optimization in cloud environments. This study investigates the performance of machine learning models, specifically Long Short-Term Memory (LSTM) networks and AutoRegressive Integrated Moving Average (ARIMA) models, for forecasting CPU utilization of AWS EC2 instances in both development and production environments. By employing historical data from both environments, the research aims to extend predictive horizons and improve forecasting accuracy. We evaluate and compare model performance using Mean Squared Error (MSE) and fitting times. Results reveal that ARIMA models consistently outperform LSTM models in terms of MSE and computational efficiency, demonstrating superior performance in both environments. LSTM models, despite their potential, show higher variability and longer fitting times, especially with hyperparameter tuning. This paper highlights the critical role of model selection and tuning in enhancing forecasting capabilities and operational efficiency in cloud resource management. The findings contribute valuable insights for optimizing resource allocation and cost management in AWS cloud services.