Optimal Cost Research Articles

Currency Risk Hedging of International Companies: A Case Study

This study investigates the effectiveness and innovative approaches of currency risk hedging strategies for multinational corporations through the case of The Walt Disney Company's response to Japanese yen depreciation risks. Research findings reveal significant limitations of traditional foreign exchange derivatives (forwards, futures, options) in managing long-term risks. Disney's adoption of the ECU Eurobond swap strategy emerged as a superior solution through its 10-year maturity matching, cost optimization (saving 105 billion in financing costs), and diversified risk dispersion. This strategy innovatively utilized composite currency instruments to hedge yen-denominated revenues against a basket of European currencies, not only locking in long-term exchange rate risks but also reducing the effective financing cost to 6.8% through U.S. tax deductions. The study demonstrates that multinational corporations should adopt a portfolio strategy combining short-term derivatives (futures, options) with long-term structural tools (currency swaps) to balance flexibility and stability. Priority should be given to maturity-matched financial instruments, and proactive exploration of composite currency product innovations is recommended. Policy suggestions include developing dynamic exchange rate forecasting models, strengthening macroeconomic monitoring, and leveraging policy windows to optimize financing structures. This research provides a structured solution for enterprises to address long-term foreign exchange risks and validates the critical role of financial instrument innovation in global operations.

Dynamic Influence of Blockchain Technology Cost on Strategic Choice of Agricultural Product Supply Chain

Given the state of technical advancement in modern society, blockchain technology has obvious potential in improving the transparency and efficiency of agricultural product supply chains. However, its high cost greatly influence enterprise strategy choices and adoption. Additionally, the agricultural supply chain faces several challenges, including asymmetric information, difficulties in traceability, complicated circulation links and high cost. This study explores how the deployment cost of blockchain affects the strategic choice of enterprises and to build a basic theoretical analysis framework. Also, the study analyzes the dynamic impact of blockchain technology cost on the strategic choices of agricultural supply chain by studying the case of Carrefour agricultural supply chain. The research concludes that the cost structure of blockchain technology directly drives enterprises to adopt the gradual strategy of "pilot expansion ecology". With the promotion of strategy, enterprises further mitigate the technology cost through large-scale application and ecological synergy, forming a positive cycle of "technology investment strategy upgrade cost optimization larger-scale investment".

Additive vs. Subtractive Manufacturing: A Comparative Life Cycle and Cost Analyses of Steel Mill Spare Parts

In the context of growing environmental concerns and the demand for more sustainable manufacturing practices, this study evaluates the environmental and economic performance of two production routes for a stainless steel support block used in steel mills. A comparative Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) were conducted to assess a conventional subtractive manufacturing process based on Computer Numerical Control (CNC) machining versus a hybrid approach that combines Plasma Arc-Wire Arc Additive Manufacturing (PA-WAAM) with CNC finishing. The LCA was carried out using ReCiPe 2016 Midpoint and Endpoint methodologies in SimaPro, while the LCC employed a cradle-to-gate cost model. Results showed that the hybrid WAAM-CNC route reduced average environmental impacts by 49% across 18 categories and decreased steel consumption by approximately 70% due to near-net-shape fabrication. Although the hybrid method incurred an approximate 3.5 times increase in unit production cost, this was primarily attributed to equipment investment. In contrast, operational costs such as labor, materials, and consumables were significantly lower—by 66%, 28%, and 45%, respectively. These findings support the hybrid approach as a more sustainable manufacturing alternative with the potential for long-term cost optimization as additive technologies mature.

Two-step beam geometry optimization for volumetric modulated arc therapy gantry angles in breast treatments.

In partial arc volumetric modulated arc therapy (VMAT) for treating breast cancer, setting up the limiting gantry positions of the treatment machine is a nontrivial yet repetitive and time-consuming task during planning. Templatized solutions exist but may not provide adequate plan quality. We have developed a two-step beam geometry optimization (2SBGO) method to set up in an automated manner the VMAT start/stop gantry angles and avoidance sector (AvS) angle limits for breast treatments. We compare our preliminary results of 2SBGO to manually created plans. In the first step of the method (based on patient geometry), the initial angles are obtained either from a template, from machine-learning (ML) predictions or manually. A search range around the initial positions is specified for each angle. In the second step (refinement using dosimetric criteria), the parameters are optimized using generalized simulated annealing (GSA). As objective function for GSA, we used the optimizer cost. We tested the method for deep inspiration breath hold and free breathing patients for left- and right-sided breast treatments. As ML models, we trained convolutional neural networks to predict the angles (start/stop angles for both the partial arc and the avoidance sector limits). The training set size was up to 86 patients, the validation set size was fixed to six patients and the test set size to six patients. The initial input before preprocessing was in DICOM format (RT plan and structure files and CT images). The rationale for using ML as first step is to learn from data the ways the beam angles are set and evaluate how good the initial ML solution would be for the final plan quality. We showed that for all the test patients, the 2SBGO leads to plans that are of a comparable dosimetric quality compared with manual plans while eliminating the complex and time-consuming beam geometry (BG) set-up step. Additionally, with the optimization function we used in our approach the ipsilateral lung doses in right-sided treatments are reduced compared with plans with manual angle selection. The ML models were shown to be most useful in a clinical workflow when integrated in the full 2SBGO scheme. The ML models themselves, before the second optimization step, predicted the medial angle within accuracy 3.6°±2.6° (mean±SD) and the AvS limiting values within 10.8°±8.3°. The Wilcoxon paired signed-rank test indicated that there were no statistically significant differences between the distributions of ML predictions and manual choices. We can obtain in an automatic fashion clinically acceptable breast treatment plans, dosimetrically comparable with the manual ones, by combining an initial BG set-up with a dosimetric refinement step for the beam angles. The initial ML-based plans are a useful starting point while they need further refining which the dosimetric fine-tuning provides. The work paves the way to the automation of the BG setup that has the potential for considerable savings of planner's time, and a decrease in the variation of the quality of the plans.

Incorporating vaccines into vaccination schedules around the world: A scoping review.

Incorporating vaccines into vaccination schedules around the world: A scoping review.

Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting.

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications; 2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy regularization to selectively increase and decrease predicting confidence for confidence re-calibration. Note that EATA-C and EATA are different on the adaptation objective, while EATA-C still benefits from the active sample selection criterion and anti-forgetting Fisher regularization proposed in EATA. Extensive experiments on image classification and semantic segmentation verify the effectiveness of our proposed methods.

Energy Cost Optimisation in a Wastewater Treatment Plant by Balancing On-Site Electricity Generation with Plant Demand

Wastewater treatment plants (WWTPs) consume a considerable amount of energy. They also generate energy in combined heat and power (CHP) units, which utilise biogas from the anaerobic digestion of sewage sludge to produce renewable electricity. Different prices apply to electricity generated on site in CHP units, to the purchase of electricity from the grid, to the sale of surplus electricity to the grid and energy tariffs, which motivates the optimisation of energy costs. This paper presents a strategy for optimising electricity costs by adapting on-site electricity generation in CHP units to the demand of the WWTP. The approach is designed for a CHP system that generates electricity in multiple internal combustion gas engines. It is implemented as a two-level control system, where the lower control level dynamically adjusts the power of the individual gas engines, and the upper control level optimises the desired total power, taking into account the current energy consumption of the WWTP, biogas reserves and electricity tariffs. The proposed concept was implemented at the Domžale-Kamnik WWTP. A six-month evaluation showed that electricity purchased from the grid could be reduced from 8.7% to 3.3% of the WWTP’s electricity consumption. This reduction affects the system economically, as electricity purchased from the grid at low and high tariffs is 35% and 76% more expensive than electricity generated on site (excluding the grid fee). This approach can be extended to balance dispatchable electricity generation at the WWTP to respond to short-term grid demand.

Plasma-Assisted Hydrogen Production: Technologies, Challenges, and Future Prospects

As global demand for clean energy continues to rise, hydrogen, as an ideal energy carrier, plays a crucial role in the energy transition. Traditional hydrogen production methods predominantly rely on fossil fuels, leading to environmental pollution and energy inefficiency. In contrast, plasma-assisted hydrogen production, as an emerging technology, has gained significant attention due to its high efficiency, environmental friendliness, and flexibility. Plasma technology generates high-energy electrons or ions by exciting gas molecules, which, under specific conditions, effectively decompose water vapor or hydrocarbon gases to produce hydrogen. This review systematically summarizes the basic principles, technological routes, research progress, and potential applications of plasma-assisted hydrogen production. It focuses on various plasma-based hydrogen production methods, such as water vapor decomposition, hydrocarbon cracking, arc discharge, and microwave discharge, highlighting their advantages and challenges. Additionally, it addresses key issues facing plasma-assisted hydrogen production, including energy efficiency improvement, reactor stability, and cost optimization, and discusses the future prospects of these technologies. With ongoing advancements, plasma-assisted hydrogen production is expected to become a mainstream technology for hydrogen production, contributing to global goals of zero carbon emissions and sustainable energy development.

Optimizing bone health for the prevention of revision adult spinal deformity surgery:a break-even analysis.

Optimizing bone health for the prevention of revision adult spinal deformity surgery:a break-even analysis.

D2D assisted cooperative computational offloading strategy in edge cloud computing networks

In the computational offloading problem of edge cloud computing (ECC), almost all researches develop the offloading strategy by optimizing the user cost, but most of them only consider the delay and energy consumption, and seldom consider the task waiting delay. This is very unfavorable for tasks with high sensitive latency requirements in the current era of intelligence. In this paper, by using D2D (Device-to-Device) technology, we propose a D2D-assisted collaboration computational offloading strategy (D-CCO) based on user cost optimization to obtain the offloading decision and the number of tasks that can be offloaded. Specifically, we first build a task queue system with multiple local devices, peer devices and edge processors, and compare the execution performance of computing tasks on different devices, taking into account user costs such as task delay, power consumption, and wait delay. Then, the stochastic optimization algorithm and the back-pressure algorithm are used to develop the offloading strategy, which ensures the stability of the system and reduces the computing cost to the greatest extent, so as to obtain the optimal offloading decision. In addition, the stability of the proposed algorithm is analyzed theoretically, that is, the upper bounds of all queues in the system are derived. The simulation results show the stability of the proposed algorithm, and demonstrate that the D-CCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the user cost.

Recent Advances in Microbial Enzyme Applications for Sustainable Textile Processing and Waste Management

Microbial enzymes have revolutionised the textile industry by replacing harmful chemicals with eco-friendly alternatives, enhancing processes such as desizing, scouring, dyeing, finishing, and promoting water conservation while reducing pollution. This review explores the role of enzymes like amylases, pectinases, cellulases, catalases, laccases, and peroxidases in sustainable textile processing, focusing on their ability to mitigate environmental pollution from textile effluents. The review also examines the types and characteristics of hazardous textile waste and evaluates traditional waste treatment methods, highlighting sustainable alternatives such as microbial enzyme treatments for effluent treatment. Recent advancements in recombinant enzyme technology, including enzyme engineering and immobilisation techniques to enhance stability, reusability, and catalytic performance, are also explored. Additionally, the potential of extremozymes in textile processing and effluent treatment is explored, emphasising their stability under harsh industrial conditions. Strategies for reducing textile waste through enzyme-based processes are presented, focusing on principles of the circular economy. The review also addresses challenges such as scalability, cost, and process optimisation, while proposing potential solutions and outlining future directions for the widespread adoption of microbial enzymes in sustainable textile production and waste management. This review underscores the transformative potential of microbial enzymes in achieving greener textile manufacturing practices.

Pengaruh Optimasi Biaya Produksi terhadap Peningkatan Keuntungan (Laba) pada UMKM di Pancing, Kota Medan

This study aims to analyze the effect of production cost optimization on increasing profits (profit) in Micro, Small, and Medium Enterprises (MSMEs) in Pancing, Medan City. MSMEs have an important role in the Indonesian economy, contributing around 60% of Gross Domestic Product (GDP) and absorbing more than 97% of the workforce. However, many MSMEs face challenges in managing production costs efficiently, which can reduce their profit margins and competitiveness in the market. The method used in this study was a survey with a questionnaire distributed to 10 registered MSME respondents. The results of the analysis showed that 80% of respondents had businesses registered as MSMEs, and 60% of them conducted production cost analysis regularly. In addition, 70% of respondents believed that good production cost management could increase their business profits. Correlation analysis showed a strong positive relationship between production cost optimization and increased profits, with a correlation coefficient of 0.75 and a significance value of p <0.05. The regression results showed that every one unit increase in production cost optimization would increase profits by 1.20 units. This study concludes that production cost optimization has a significant effect on increasing profits in MSMEs in Medan. Therefore, it is recommended that MSME owners continue to improve their understanding of the importance of production cost optimization and implement technology that can help efficiency and conduct cost analysis regularly.

A unified risk management framework for cost and resource optimization in housing development projects

The increasing demand for affordable housing has highlighted the need for more efficient project management strategies, especially in the face of rising costs, resource constraints, and regulatory complexities. This paper proposes a unified risk management framework designed to optimize cost and resource allocation in housing development projects. The framework integrates various risk management techniques, including risk identification, assessment, and mitigation, with cost control methods and resource optimization strategies. It emphasizes a dynamic, data-driven approach to evaluating and managing risks throughout the lifecycle of housing projects, from planning and execution to monitoring and completion. By embedding risk management practices into all project phases, this framework enhances project efficiency, reduces the likelihood of cost overruns, and ensures timely delivery of housing projects. Real-world case studies demonstrate the framework's potential to improve housing development outcomes, though challenges such as data limitations, stakeholder alignment, and resistance to change must be addressed for successful implementation. The paper also identifies promising areas for future research, including the integration of AI and machine learning for predictive risk assessment, real-time monitoring systems, and further exploration of strategies for large-scale housing developments. Overall, the unified risk management framework offers a holistic solution to the persistent issues of cost and resource optimization in housing development. Keywords: Risk Management, Housing Development, Cost Optimization, Resource Allocation, Predictive Analytics, Project Management.

Cost and Sustainability Optimization of Desalinated Water Supply Chains

Cost and Sustainability Optimization of Desalinated Water Supply Chains

Effects of green packaging and preservation on deteriorating products with Weibull distribution

In today's environment, sustainability without including eco-friendly products is becoming a challenge for industries. Global warming and greenhouse emissions pose significant concerns worldwide. Burning fossil fuels, deforestation, renovation of industries, transportation, and greenhouse emissions contribute to global warming. This study presents a green integrated production system with a constant demand rate for controllable deterioration. The shortage has occurred with partially backlogged with a constant rate of backlogging. The strategy is to develop a production quantity system with three different production rates for deteriorating products that follow a three-parameter Weibull distribution. This model also incorporates pollution control costs, disposal costs, green transportation costs, green packaging costs, and green technology costs. A solution methodology is proposed to determine the optimal investment for preservation technology and cycle time. A numerical experiment underpins this study. Sensitivity analysis explains the mathematical model’s results using essential parameters. This study aims to determine the optimal cost for preservation technology investment that minimizes the total average cost of the green inventory model. The findings reveal that the proposed three-rate green production economic quantity model offers superior cost efficiency, yielding the lowest preservation technology cost and cycle time compared with other models. The analysis demonstrates the practicality and applicability of the optimal results in optimizing total inventory costs for green packaging businesses.

Distributed Policy Space Response Oracles in Two-Player Zero-Sum Games.

Policy space response oracle (PSRO) is a population-based algorithm that can be used to solve two-player zero-sum games. In the PSRO solution framework, optimizing policy diversity is crucial for addressing nontransitive game problems, helping the agent population avoid exploitation by unfamiliar opponents. In addition, while deep reinforcement learning is highly effective in solving complex game environments, its integration with PSRO remains fragmented and lacking in effective coordination. In this study, we propose distributed PSRO to efficiently solve complex game scenarios. To enhance diversity while managing optimization costs, we introduce TOP-K truncation, which prioritizes high-quality opponents and limits the size of the policy pool during sampling. This approach not only reduces interference from less effective strategies but also ensures computational efficiency by seamlessly integrating with our distributed training framework. We also design the distributed training framework to incorporate diversity estimation directly into the sampling process, achieving diversity optimization without incurring additional computational overhead. Furthermore, we introduce the opponent first (OF) method, which enhances decision-making by leveraging opponent information during interaction sampling. We perform experimental validation using a nontransitive mixture model and AlphaStar888 to confirm the effectiveness of the TOP-K truncation approach. Finally, we demonstrate the feasibility and efficiency of the distributed training framework and the OF approach in a Google Research Football 11 versus 11 scenario.

Resource cost optimization strategies in off-site manufacturing for modern construction methods: a lean theory perspective case study in Ireland

This research presents an innovative approach based on Lean Theory that explores the application of Modern Methods of Construction (MMC) in Ireland, specifically aimed at optimizing resource costs for precast concrete components. The research has developed a supplier evaluation and raw material procurement model rooted in Lean principles, utilizing the Triangular Fuzzy Number-Analytic Hierarchy Process (TFN-AHP) and Kraljic matrix within the Irish construction sector. This research illustrates how these methods significantly enhance the sustainability and efficiency of MMC, to promote the adoption of MMC technologies in construction projects and address Ireland’s severe housing shortage. The paper highlights the originality of applying these integrated methods in the Irish construction industry, setting a precedent for future research and practice.

AI - Driven Airfare Prediction Models for Cost Optimization and Consumer Savings in the U. S.: Integrating ETL and Cybersecurity for Enhanced Data Processing and Protection

AI - Driven Airfare Prediction Models for Cost Optimization and Consumer Savings in the U. S.: Integrating ETL and Cybersecurity for Enhanced Data Processing and Protection

REVIEW OF DYNAMIC ECONOMIC DISPATCH IN MICROGRID

Microgrid provide a comprehensive strategic partnership with conventional methods to fulfil energy need globally. Microgrid provide energy need individually or with association with distributed energy resource such as solar, wind etc. energy supply mechanism should be economically beneficially for distributor and efficient. A Varity of cost optimization method available for reducing the generation cost for conventional as well as microgrid operation. Cost optimization techniques mainly classified with three sub categories such as conventional or traditional, artificial or computerized and hybrid or mixed based (conventional and AI) techniques. This review paper provides a comprehensive view to analysis these techniques.

Disaster Recovery and High Availability Strategies in Oracle Cloud Infrastructure

Oracle Cloud Infrastructure provides comprehensive disaster recovery and high-availability solutions for modern database management. This article examines the critical components and implementation strategies for achieving optimal resilience in cloud-based database environments. Financial impact analysis reveals substantial costs associated with downtime, underscoring the business value of effective DR/HA solutions. Examining OCI's high availability architecture demonstrates how Real Application Clusters, Data Guard, Automatic Storage Management, and Load Balancing work together to eliminate single points of failure. Cross-regional disaster recovery implementations protect against catastrophic events through geographically dispersed resources, while advanced monitoring capabilities enable predictive maintenance and early detection of potential failures. Cost optimization techniques balance resilience requirements with financial constraints through automated scaling, resource rightsizing, and storage tiering. The integration of these components creates a robust framework adaptable to specific business requirements while delivering exceptional availability metrics. This article provides a detailed technical roadmap for Oracle database engineers seeking to design resilient cloud architectures that minimize business disruption and optimize operational expenses by strategically implementing Oracle's Maximum Availability Architecture.