Cost-optimal Strategy Research Articles

QoS improvement in multi-cloud system: installation cost optimization strategy

QoS improvement in multi-cloud system: installation cost optimization strategy

Evaluating conventional and renewable energy systems for green buildings: A case study on energy efficiency and cost optimization

This study explores two primary categories of power generation: conventional methods reliant on fossil fuels and renewable energy methods. Fossil fuel-based approaches, including natural gas and heavy fuel oil, contribute to environmental concerns due to their carbon dioxide (CO2) emissions, which are responsible for the greenhouse effect and global warming. Many nations, particularly in Europe, have implemented stringent measures to restrict CO2 emissions, imposing fines on industries that exceed emission limits. In response to these concerns, this study focuses on a green building project that aims to meet its power requirements through renewable energy sources. Various renewable energy resources will be evaluated, considering the merits and drawbacks of each technology, to identify the most suitable option for the building under investigation. Additionally, a comprehensive thermodynamic analysis will be conducted, including calculations of electrical needs such as cooling load, lighting load, and equipment load. A customized power calculator will also be developed for application in similar projects. The ultimate objective of this study is to achieve complete autonomy from the electric grid by utilizing renewable energy resources, transforming the building into a green building. The results indicate that at an outside temperature of 22 °C, the total heat gain decreases from 157.3 to 39 kW, reducing the total cost of electricity from 20,000 to 5100 AED/year. Conversely, at an outside temperature of 50 °C, the total heat gain increases from 157.3 to 196 kW, raising the total cost of electricity from 20,000 to 26,000 AED/year. These findings demonstrate the significant influence of outside temperature on the building's energy requirements and costs, underscoring the importance of considering temperature variations in energy efficiency and cost optimization strategies.

Optimization and simulation of a novel multi-energy complementary heat pump system in cold regions

Solar-assisted ground source heat pump systems(SAGSHPSs) and solar- assisted air source heat pump systems (SAASHPSs) are recognized renewable energy technologies for clean and feasible heating and domestic hot water supply. However, in cold regions, the poor thermal comfort of SAASHPSs and the soil thermal imbalance of SAGSHPSs during heating periods must be resolved urgently. Therefore, to provide an efficient and stable heat source, a multi-energy complementary heat pump system(MECHPS) with seasonal heat storage was designed to meet the needs of building energy consumption. The system comprehensively utilizes solar energy and ambient air for seasonal heat storage during non-heating periods, and then directly provides heat coupled with the ground source heat pump during heating periods. The composition and control strategy of the system were introduced. The system was numerically modeled by the simulation software Transient System Simulation Program (TRNSYS), and the main MECHPS modules were experimentally verified. Additionally, to study the optimal capacity of MECHPS in terms of economy and energy savings, the Hooke-Jeeves algorithm was used for MECHPS capacity optimization with the objectives of minimising the annual cost and maximising the annual savings of standard coal. The results showed that compared with traditional SAASHPSs, the MECHPS exhibited better thermal comfort while maintaining the room temperature above 18 °C. Compared to SAGSHPSs, the MECHPS was able to achieve soil thermal balance, and its soil temperature remained largely constant after 15 years of operation. Finally, compared with the initial scheme, the annual cost reduction rate was 4.98 % under the annual cost optimization scheme and an additional 1310 kg of standard coal could be saved under the annual savings of standard coal optimization scheme. The proposed system provides a way to satisfy the building energy supply for widespread application in cold regions.

Modelling and optimization of ATM cash-loading under stochastic demand

A set of ATMs in commercial areas were considered for business transactions. Customer demand for money requires prompt cash supply; to harmonize optimality options for ATM cash loading to sustain customers. We formulated this tis decision as a multi-stage stochastic process with Markovian demand of ATM cash among customers. The loading and operations of the ATMs were governed by cash demand and supply; with unit (loading, operational, and shortage) costs. The study considered the ATM cash demand of ten ATMs in regions within commercial centres of Kampala city. The relevant data was collected every hour throughout four months. The task focused on optimizing cash-loading decisions that minimize the operational costs of managing ATMs. The optimality of cash loading decisions were determined using the Markov decision processes. The tested model revealed optimal state-dependent options and operational costs of managing ATMs cost-effectively. As a cost optimization strategy for managers in the banking industry, the productivity of ATMs towards improved efficiency and utilization of resources for cash provision to customers emerged; supporting a larger customer base for business transactions.

Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data is costly to both collect and exploit. Determining cost optimal data usage strategies requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in multi-building energy systems using a simulated case study with historic building energy data. The impact on forecast accuracy of measures to improve model data efficiency is quantified, specifically for: reuse of prediction models, reduction of training data duration, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.

Principles of Cloud Computing Infrastructure IaaS

Infrastructure as a Service (IaaS) represents a cornerstone in contemporary cloud computing, providing essential on-demand computing resources, including servers, storage, and networking. This paper explores the foundational principles of IaaS, highlighting its ability to facilitate dynamic server management through practices like auto-scaling and ephemeral server utilization. The use of custom images in IaaS ensures minimal downtime and scalable deployment, while loose coupling enhances fault tolerance and adaptability. Emphasizing high availability through auto-scaling, IaaS supports continuous operations even in geographically vulnerable areas. The security framework of IaaS, encompassing a shared responsibility model, ensures robust data protection and compliance with industry standards. The paper also addresses cost optimization strategies and the integration of hybrid cloud architectures to maximize resource efficiency and maintain data integrity. Overall, IaaS is integral to modern IT infrastructure, offering flexible, scalable, and cost-effective solutions that align with the evolving demands of the digital landscape.

Enhancing financial stability in Islamic banks: An investigation of determinants during the COVID-19 in Indonesia

Purpose — This study investigates the determinants of Islamic banks' stability in Indonesia amidst the Covid-19 pandemic.Method — Employing a causality-associative quantitative approach, the research utilizes purposive sampling and secondary data collection from monthly financial reports on the official websites of OJK (Financial Services Authority) and BI (Bank Indonesia). Statistical techniques including normality test, multicollinearity test, t-test, f-test, R2 test, and multiple linear regression, aided by SPSS version 22 and Microsoft Excel, are employed for data analysis.Result — The results reveal that Islamic banks' total assets significantly positively influence their stability in Indonesia. Additionally, variables such as Operational Costs to Operational Income (OCOI) ratio and BI 7-Day Reverse Repo Rate (BI7DRR) exhibit significant negative effects on Islamic banks' stability. Practical implications — Understanding the influence of total assets, Operational Costs to Operational Income (OCOI) ratio, and the BI 7-Day Reverse Repo Rate (BI7DRR) on Islamic banks' stability in Indonesia during the Covid-19 pandemic can guide policymakers and bank management in implementing measures to strengthen resilience and mitigate risks, such as strategic asset management and cost optimization strategies.

A novel heating strategy and its optimization of solar–air source heat pump heating system for rural buildings in northwest China

In the rural areas of Northwest China, the utilization of clean and renewable energy is deemed a crucial measure for reducing building energy consumption and environmental pollutant emissions. This paper focuses on constructing a simulation platform for a solar-assisted air source heat pump heating system. A rural residential building in Yongshou County, Shaanxi Province, serves as an illustrative example. A novel flexible temperature control method with a feedback controller in sub-area and period is proposed in this paper, alongside the selection of three distinct objective functions aimed at optimizing the heating system. The simulation results indicate an average temperature of 17.0 °C throughout the heating cycle, with a peak temperature of 18.7 °C. Moreover, the solar fraction is measured at 25.11%, underscoring the significance of collector area and heat storage tank volume as primary factors in system design. The results also demonstrate that across various optimization objectives, the life cycle cost optimization scheme yields greater economic benefits, while the target building unit heating cost optimization scheme boasts the shortest static payback period and lowest unit heating cost. Conversely, the solar fraction optimization scheme stands out for its superior environmental benefits. These findings offer valuable insights for the design of heating systems tailored to diverse objectives.

Agroindustrial Firm's Financial Strategy during Pandemic: Case Study in PTPN X, Jember Regency

This qualitative research aimed to explore the financial policies and strategies adopted by companies in the agro-industrial sector in an effort to improve their financial performance, especially during the COVID-19 pandemic. The research used a case study approach conducted at PTPN X Jember. PTPN X Jember is an agro-industry company that produces world-class tobacco as its main product. Through in-depth interviews and periodic observations, data mining was carried out by considering one finance director as the main informant and supplemented by an in-depth review of financial report documents as secondary data reinforcement. Data analysis adopts Miles and Huberman’s technique consisting of three activities, i.e. data reduction, data display, and drawing conclusions/verification. The results of the study indicated that there is a strong preference for PTPN X to carry out a cost optimization strategy to encourage increased performance, which had an impact on increasing the quantity and quality of products. Quality products had a direct effect on increasing profits despite the COVID-19 pandemic situation. This research contributed to the development of financial management literature, especially corporate financial strategies during times of crisis in the agro-industrial sector.
 Keywords: agro-industry, financial performance, Human Resources, cost optimization strategy

ASXC$^{2}$ Approach: A Service-X Cost Optimization Strategy Based on Edge Orchestration for IIoT

ASXC$^{2}$ Approach: A Service-X Cost Optimization Strategy Based on Edge Orchestration for IIoT

THE MANAGEMENT OF FINANCES IN INTERNATIONAL PROJECTS: OPTIMIZING COSTS AND ENSURING SUCCESS THROUGH GLOBAL INVESTMENTS AND FINANCIAL MANAGEMENT

In the course of writing the scientific article, an in-depth study of aspects of financial management in the context of international projects was conducted. The focus was on analyzing cost optimization strategies and achieving success in a global investment and financial management environment. The conducted research shows that in the modern world of globalization, it is important to understand and effectively implement financial management strategies that take into account the unique aspects of international business. Particular attention is paid to the identification of risks related to the external environment, such as currency fluctuations, political and economic risks, which affect the effectiveness of financial decisions. Particular attention is paid to the use of specialized financial instruments to reduce risks, such as risk management and multi-currency settlements. The article analyzes in detail the influence of financial decisions on the effectiveness of international projects, emphasizing the strategic context of profit maximization and ensuring the stability of the financial position. The study of the interaction of financial mechanisms in international transactions takes place in the context of the unique challenges of the global business environment, including currency fluctuations and political and economic risk. In addition, the article summarizes recommendations and strategies aimed at achieving maximum efficiency and sustainability for international projects through careful strategic planning and cost optimization. A detailed analysis of various mechanisms of financial management in the context of global investments has revealed key principles that can serve as a basis for solving complex financial management tasks in international business.

Environmental/Economic Power Dispatch of MicroGrid Using Multiobjective Optimization

This paper proposes a generalized formulation to determine the optimal operating strategy and cost optimization scheme as well as the reduction of the emissions for a MicroGrid (MG). Multiobjective (MO) optimization is applied to the environmental/economic problem of the MG. The proposed problem is formulated as a nonlinear constrained MO optimization problem. Prior to the optimization, models for system components from real data are constructed. The problem formulation takes into consideration the operation and maintenance costs as well as the emissions NOx, SO2, and CO2 reduction. The MG considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the costumer demand and safety of the system. We also add a daily income and outgo from sale or purchased power. The results demonstrate the efficiency of the proposed approach to satisfy the load and to reduce the cost and the emissions. The comparison with other techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the problem.

System modelling and online optimal management of microgrid with battery storage

This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme for a MicroGrid. Prior to the optimization of the microgrid itself, the system model components from some real manufactural data are constructed. The proposed cost function takes into consideration the costs of the emissions NOx, SO2, and CO2 as well as the operation and maintenance costs. The microgrid considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array , a fuel cell, and a battery storage. The optimization is aimed at minimizing the cost function of the system while constraining it to meet the customer demand and safety of the system.

Cost-optimal CO2 capture and transport infrastructure—A case study of Sweden

This work applies a mixed integer cost-minimisation model to identify cost-optimal carbon capture and storage (CCS) infrastructure systems. The modelling applies two types of incentives for CCS implementation: carbon pricing, and binding emissions budgets. Both incentive schemes are applied with and without accounting for CO2 capture from biogenic emissions sources. In the case of CO2 pricing, biogenic CO2 capture is implemented by letting each ton of biogenic CO2 captured generate value for the model equivalent to the cost of emitting one ton of fossil CO2. In the case of emissions budgets, biogenic CO2 capture is included by allowing the model to use both biogenic and fossil CO2 capture to stay within the budget. The main fossil and biogenic emissions sources in Swedish industry are used as a case study.The results show that incentivising carbon removal has a significant impact on the design and development of the cost optimal system for CCS if there are suitable biogenic emission sources available for implementing biogenic CO2 capture. The timing for investments in carbon capture is highly dependent on the discount rate - increasing the discount rate in the modelling from 5 % to 15 % delays the first investments in CO2 capture by three years. To facilitate technology development and timely implementation of CCS on biogenic and fossil sources, it is important to consider that inclusion of carbon dioxide removal into the policy regime controlling fossil fuel emissions, might result in that the cost optimal strategy will be a delay in fossil fuel mitigation.

CONTEMPORARY TRENDS IN THE BUSINESS ENVIRONMENT IN UKRAINE

The article examines the impact of external and internal factors on the macroeconomic situation and business environment in Ukraine. It discusses the peculiarities of the business environment's reaction to unstable conditions, including the presence of financial reserves in companies, cost optimization, and management strategies. The influence of war on the demand and supply of services, changes in the economic structure, and new requirements for businesses are analyzed in detail. The size and basic characteristics of economic sectors are studied, and factors affecting business efficiency in Ukraine are identified. The article focuses on determining the interrelations between the development of the business environment and economic processes in Ukraine. It reviews the specificities of Ukraine's macroeconomic situation during wartime and the impact of external and internal factors on economic development. Moreover, key macroeconomic indicators are analyzed based on statistical data, and a detailed study is conducted on how full-scale war affects Ukraine's economy. The reaction of the business environment to unstable conditions, including the availability of financial reserves in companies, cost optimization, and management strategies, is also analyzed. Special attention is paid to the impact of war on demand and supply. The segments of the economy most affected by the war are examined, as well as changes in the economic structure. The size and basic characteristics of the economy are analyzed, and trends in development and factors influencing its efficiency are identified. Various methods are used in the study, including analysis of statistical data, expert surveys, and comparative analysis with macroeconomic indicators. In particular, the research is oriented towards determining the interrelations between the influence of external factors and economic processes in Ukraine. Based on the obtained data, regularities of economic development in Ukraine are formed, and a forecast for the economy's development in 2024 is constructed. The conclusions of the study may be useful for professionals interested in Ukraine's macroeconomic processes, as well as for businesses and government structures involved in strategic planning.

Development of the consulting services market in Ukraine amid contemporary changes

The article examines the peculiarities of the consulting services market in Ukraine amid wartime conditions, as well as the influence of external and internal factors on the development of the consulting services market in Ukraine. Additionally, the authors analyze key indicators of the Ukrainian consulting services market based on statistical data and conduct a detailed investigation into how full-scale war affects its development. The article also assesses the business environment's response to unstable conditions, including companies' financial reserves, cost optimization, and management strategies. Special attention is given to the impact of war on the demand and supply of consulting services. It investigates which market segments suffered the most due to the war, how the structure of client requests has changed, and what new requirements have emerged for consulting services. The article thoroughly analyzes the influence of supply and demand factors on the functioning of the consulting services market in Ukraine, based on statistical data, key operational aspects, and regulatory frameworks. It examines the size and main characteristics of the consulting services market, delving into its structure and key participants. Particular focus is placed on evaluating trends in the industry's development and identifying factors affecting its effectiveness. The research employs various methods, including statistical data analysis, expert surveys, and comparative analysis with macroeconomic indicators. Specifically, the study aims to determine the interrelation between the development of the consulting market and economic processes in Ukraine. Based on the findings, patterns of development in the Ukrainian consulting services market are identified, recommendations for strategic planning and improvement of this sector are formulated, and a forecast for its development is constructed. The conclusions of the study may be valuable for professionals interested in the consulting services market in Ukraine, as well as for businesses and governmental entities involved in regulating this industry.

Ambulatory Surgery Centers Reduce Patient Out-of-Pocket Expenditures for Isolated Arthroscopic Rotator Cuff Repair, but Patient Out-of-Pocket Expenditures Are Increasing at a Faster Rate Than Total Healthcare Utilization Reimbursement From Payers

To categorize and trend annual out-of-pocket expenditures for arthroscopic rotator cuff repair (RCR) patients relative to total healthcare utilization (THU) reimbursement and compare drivers of patient out-of-pocket expenditures (POPE) in a granular fashion via analyses by insurance type and surgical setting. Patients who underwent outpatient arthroscopic RCR in the United States from 2013 to 2018 were identified from the IBM MarketScan Database. Primary outcome variables were total POPE and THU reimbursement, which were calculated for all claims in the 9-month perioperative period. Trends in outcome variables over time and differences across insurance types were analyzed. Multivariable analysis was performed to investigate drivers of POPE. A total of 52,330 arthroscopic RCR patients were identified. Between 2013 and 2018, median POPE increased by 47.5% ($917 to $1,353), and median THU increased by 9.3% ($11,964 to $13,076). Patients with high deductible insurance plans paid $1,910 toward their THU, 52.5% more than patients with preferred provider plans ($1,253, P= .001) and 280.5% more than patients with managed care plans ($502, P= .001). All components of POPE increased over the study period, with the largest observed increase being POPE for the immediate procedure (P= .001). On multivariable analysis, out-of-network facility, out-of-network surgeon, and high-deductible insurance most significantly increased POPE. POPE for arthroscopic RCR increased at a higher rate than THU over the study period, demonstrating that patients are paying an increasing proportion of RCR costs. A large percentage of this increase comes from increasing POPE for the immediate procedure. Out-of-network facility status increased POPE 3 times more than out-of-network surgeon status, and future cost-optimization strategies should focus on facility-specific reimbursements in particular. Last, ambulatory surgery centers (ASCs) significantly reduced POPE, so performing arthroscopic RCRs at ASCs is beneficial to cost-minimization efforts. This study highlights that although payers have increased reimbursement for RCR, patient out-of-pocket expenditures have increased at a much higher rate. Furthermore, this study elucidates trends in and drivers of patient out-of-pocket payments for RCR, providing evidence for development of cost-optimization strategies and counseling of patients undergoing RCR.

IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.

Assessing the carbon footprint of soccer events through a lightweight CNN model utilizing transfer learning in the pursuit of carbon neutrality

IntroductionSoccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming training, and many parameters, making them unsuitable for real-world industrial scenarios. To address this issue, we propose a lightweight CNN model based on transfer learning to study cost minimization strategies for soccer events in a carbon-neutral context.MethodsOur proposed lightweight CNN model uses a downsampling module based on the human brain for efficient information processing and a transfer learning-based module to speed up the training progress. We conducted experiments to evaluate the performance of our model and compared it with existing models in terms of the number of parameters and computation and recognition accuracy.ResultsThe experimental results show that our proposed network model has significant advantages over existing models in terms of the number of parameters and computation while achieving higher recognition accuracy than conventional models. Our model effectively predicts soccer event data and proposes more reasonable strategies to optimize event costs and accelerate the realization of carbon neutral goals.DiscussionOur proposed lightweight CNN model based on transfer learning is a promising method for studying cost minimization strategies for soccer events in a carbon-neutral context. The use of a downsampling module based on the human brain and a transfer learning-based module allows for more efficient information processing and faster training progress. The results of our experiments indicate that our model outperforms existing models and can effectively predict soccer event data and propose cost optimization strategies. Our model can contribute to the realization of carbon-neutral goals in the sports industry.

Desain Rute dan Optimasi Biaya Transportasi Pengisian Tabung Oksigen menggunakan Metode Complete Enumeration pada CV. Tasman Gases

The increasing demand for filling oxygen cylinders makes CV. Tasman Gases has always been consistent in planning transportation cost optimization strategies. The filling of oxygen cylinders in one distribution can transport up to 45 tubes sent to the concerned partner. The purpose of this study is to determine the shortest route and optimal cost in distributing. The method used is Complete Enumeration. The samples used include distribution locations and mileage. The procedure used begins with determining the number of routes, estimated mileage and fuel costs. There is the most optimal route selected from the 12 modeled routes. The selected route has the least transportation costs. This research makes CV. Tasman Gases has the best route, A-D-C-E-F with a distance of 10.9 km worth Rp. 10,423.13. This value is more effective than the existing A-E-C-D-F condition with a distance of 12.8 km obtaining fuel costs of Rp. 12,240.00. Thus, the distribution route and costs that have been optimal make CV. Tasman Gases can implement and identify future transportation constraints.