Optimal Cost Research Articles

Optimization of Network Pavement Life-Cycle Cost: A Piecewise Linearized Approach

Optimization of Network Pavement Life-Cycle Cost: A Piecewise Linearized Approach

Conceptualizing the MNEs Transfer Pricing Behavior: Indonesian Tax Authority Perspective

Indonesian tax authorities currently encounter a lack of cooperative compliance among companies engaged in transfer pricing practices with their affiliated entities across borders. Transfer pricing is commonly associated with tax purposes, such as tax planning or avoidance, which is considered standard practice for business entities. Hence, there is a significant necessity to assess and evaluate the prevalence of tax planning or avoidance through transfer pricing in multinational enterprises operating in Indonesia. To gauge the extent of transfer pricing used for tax avoidance purposes, this study examines the perspective of tax authorities on companies' transfer pricing optimization behavior. Employing a qualitative approach, the study aims to comprehend the utilization of transfer pricing and its implications. The findings provide insights into transfer pricing behavior within companies over time. According to tax authorities, companies are motivated by the benefits of transfer pricing, including revenue optimization, corporate profit allocation, and cost optimization. However, these practices are influenced by various factors that facilitate or constrain transfer pricing activities. Multinational enterprises demonstrate limited awareness of normative beliefs regarding international and domestic transfer pricing regulations. Notably, companies affiliated with large multinational enterprises, possessing a strong understanding of transfer pricing, exhibit a high potential for tax planning or avoidance through transfer pricing arrangements with related parties. These findings contribute to the existing body of research on transfer pricing practices from the perspective of tax authorities as part of the Indonesian government.

Dynamical modelling and cost optimization of a 5G base station for energy conservation using feedback retrial queue with sleeping strategy

Dynamical modelling and cost optimization of a 5G base station for energy conservation using feedback retrial queue with sleeping strategy

Energy, exergy, and exergoeconomic cost optimization of wind-biomass multi-energy systems integrated for hydrogen production

Energy, exergy, and exergoeconomic cost optimization of wind-biomass multi-energy systems integrated for hydrogen production

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

A multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints

AbstractThis study develops a multi‐stage stochastic model to forecast the issuance of green bonds using the Filtered Historical Simulation (FHS) method to identify the most cost‐effective conditions for issuing these bonds amid various risk factors. Drawing on historical yield data and financial metrics of corporate green bonds from December 2014 to June 2023, the model considers fluctuating elements such as risk probabilities, financial risks in worst‐case scenarios, and liquidity risks at upcoming issuance moments. Our findings reveal the model's effectiveness in pinpointing the lowest possible costs of issuing new green bond portfolios in the future, while also addressing expected financial risk, risk occurrence probability, and liquidity issues. The results provide issuers with the insights needed to accurately time the market, tailor bond maturities according to a corporation's future risk profile, and enhance liquidity management. Notably, our model indicates that refining the estimated probability of future risk occurrences can lead to significant savings in green bond issuance costs. This approach allows for adaptable bond issuance strategies, addresses inherent debt, and enables detailed risk management, offering substantial benefits for green enterprises navigating the complexities of future financial landscapes.

Optimal design of an LCC-S WPT3 Z1 SAE J2954 compliant system, using NSGA-II with nested genetic algorithms for simultaneous local optimization

Wireless Power Transfer (WPT) for electric vehicles is one of the most promising methods that, given its advantages, will drive the deployment of electric vehicles. This paper presents a mathematical optimization method applied to the complete design of an LCC-S WPT3 Z1 11 kW system that complies with the SAE J2954 standard (Wireless Power Transfer for Light-Duty Plug-in/Electric Vehicles and Alignment Methodology, 2020). A design method based on three phases is proposed, allowing the complete inductor system, including ferrites shielding and compensation circuit components, to function in any relative primary and secondary position. In Phase 1, a multi-objective NSGA-II algorithm is designed, utilizing three nested genetic algorithms. The goal is simultaneously searching for the local optimum between the primary and secondary systems in three positions. This is achieved by modeling the circuit’s electrical and electromagnetic parameters with equations, enabling an iterative process with reduced computational time. The NSGA-II algorithm yields three scenarios: primary copper volume minimization, secondary copper volume minimization, and a compromise solution that optimizes the total volume. The result is then modeled in Phase 2 using a 3D finite element program that includes ferrite and optimal shielding, obtaining the values of inductances and mutual inductance in the three positions, as well as design data for manufacturing. This result is introduced in Phase 3 to optimize compensation circuit components using a second NSGA-II algorithm with three nested genetic algorithms. Again, three scenarios are obtained based on the desired system behavior and the optimal cost of the components. The result is validated through simulation with Matlab-Simulink and experimentally using a prototype constructed for this purpose.

DEVELOPMENT OF INNOVATIVE TECHNOLOGIES IN CONSTRUCTION AND CIVIL ENGINEERING IN THE CONTEXT OF DIGITALIZATION OF THE UKRAINIAN ECONOMY

Purpose. Construction and civil engineering, traditionally perceived as conservative industries, also feel the need to adapt to the demands of the time and introduce innovative technologies. Ukraine, entering the era of digital transformation, is actively developing and implementing new technologies in the construction industry. Therefore, systematization and coverage of key aspects of the impact of digital Transformation into the construction industry and civil engineering in Ukraine is the main task today. Methodology. The introduction of Internet of Things technologies in the construction industry expands the possibilities of building control and management. Findings. Sensors located in different parts of the structures allow you to monitor the condition of the building in real time, identify possible problems and ensure the safety of objects. Artificial intelligence is becoming an integral part of the digital transformation of construction. Machine learning algorithms help predict and optimize construction processes, which increases Efficiency of planning and resource management. The 3D printing system in construction is one of the promising and revolutionary innovations that is already influencing traditional approaches to construction processes. This digital technique will bring significant changes to the field of construction, accelerating and optimizing a number of stages from design to execution Originality. Analysis of current problems, especially now, in the difficult times of war, industrial construction in Ukraine, as well as around the world, is a promising direction for the development of other industries in general. Industrial construction is an important branch of the construction industry that specializes in the creation and operation of buildings and infrastructure facilities for industrial needs. Practical value. The introduction of innovative technologies in construction and civil engineering in the context of digitalization of the Ukrainian economy has a significant practical impact on various aspects of the industry: Improving Construction Productivity and Quality: Use of digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), allows you to increase the efficiency of construction processes, reduce the time of work and avoid errors; Cost optimization: Using machine learning algorithms to predict resource needs, such as materials and labor, allows you to rationalize costs and reduce construction costs.

Multi‐response optimization of characteristics for graphite reinforced biodegradable PVA‐fumaric acid cross‐linked composite: A gray‐fuzzy logic‐based hybrid approach

AbstractIn the current investigation, enhancement in the mechanical properties, thermal stability, and decrement in the moisture absorption capabilities of polyvinyl alcohol was observed by cross‐linking it with different weight percentages (wt. %) of fumaric acid and reinforcing them along with sulfuric acid‐modified graphite particles. The effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design method. As in this contemporary era of rapid industrial expansion and fierce competition, the primary goals of manufacturers revolve around the production of high‐quality goods at optimal costs within a specified timeframe. Therefore, to save both cost and time, the authors have utilized a technique known as “gray‐fuzzy optimization” to balance competing goals between mechanical (toughness, elongation and strength) and physical (water intake capacity) attributes. The present approach aimed to reduce the experimentation expenses and increase the effectiveness of composite manufacturing by considerably reducing the intricacy of practical multi‐response optimization issues. Response graphs and analysis of variance tests were also utilized to calculate the percentage contribution of the input variables and provide model significance.Highlights Polyvinyl alcohol is cross‐linked with different weight percentages (wt. %) of fumaric acid, and reinforced them with sulfuric acid‐modified graphite particles. Enhancement in mechanical properties, thermal stability, and decrease in moisture absorption capabilities were perceived for the fabricated composite. Effects of altering the cross‐linking duration and fumaric acid wt. % were investigated in the resulting composite using the Taguchi L16 experimental design. Gray‐fuzzy optimization technique was utilized to balance competing goals between water intake and mechanical attributes like toughness, elongation and strength. Response graphs and analysis of variance (ANOVA) tests were also utilized to evaluate the percentage contribution of input variables and provide model significance.

Building a Greener Future: Advancing Concrete Production Sustainability and the Thermal Properties of 3D-Printed Mortars

The integration of waste materials in extrudable cement mixtures has the potential to make the construction industry more sustainable by reducing carbon footprints and developing eco-friendly materials. This along with advancements in 3D concrete printing (3DCP) provides engineering and architectural benefits by reducing material waste and costs. In this paper, the impact of waste incorporation on properties of mortar and concrete is examined. The use of waste materials, such as pumice, coal slag, agricultural lignocellulosic residues, and recycled rubber tyres, to improve thermal insulation and durability of cementitious composites is discussed. In addition, the incorporation of air-entraining admixtures with surfactant activity is explored for their indirect effect on thermal behaviour, pore size reduction, and enhancement in concrete properties. This review includes important topics such as a strength resistance to freezing and thawing, fire resistance, plasticising effect, and delay in cement hydration. These findings highlight the benefits of using diverse waste materials in construction, providing a multidimensional approach to waste management, cost optimization, and enhanced construction materials in the context of 3DCP.

Chemical Leasing (Ch.L.) and the Sherwood Plot

Although the Circular Economy (CE) has made remarkable technological progress by offering a wide range of alternative engineering solutions, an obstacle for its large-scale commercialization is nested in the adoption of those business and financial models that accurately depict the value generated from resource recovery. Recovering a resource from a waste matrix conserves natural reserves in situ by reducing demand for virgin resources, as well as conserving environmental carrying capacities by reducing waste discharges. The standard business model for resource recovery is Industrial Symbiosis (IS), where industries organize in clusters and allocate the process of waste matrices to achieve the recovery of a valuable resource at an optimal cost. Our work develops a coherent microeconomic architecture of Chemical Leasing (Ch.L.) contracts within the analytical framework of the Sherwood Plot (SP) for recovering a Value-Added Compound (VAC) from a wastewater matrix. The SP depicts the relationship between the VAC’s dilution in the wastewater matrix and its cost of recovery. ChL is engineered on the SP as a financial contract, motivating industrial synergies for delivering the VAC at the target dilution level at the market’s minimum cost and with mutual profits. In this context, we develop a ChL market typology where information completeness on which industry is most cost-efficient in recovering a VAC at every dilution level determines market dominance via a Kullback–Leibler Divergence (DKL) metric. In turn, we model how payoffs are allocated between industries via three ChL contract pricing systems, their profitability limits, and their fitting potential by market type. Finally, we discuss the emerging applications of ChL financial engineering in relation to three vital pillars of resource recovery and natural capital conservation.

Routine 2-Week Postoperative Radiographs After Geriatric Hip Fracture Fixation: Low Utility and High Cost

Background: Geriatric hip fractures are associated with a large financial burden on both patients and payors, yet minimal data exist regarding postoperative cost optimization and guidelines for delivering high-value care. We assessed the utility and cost of routine radiographs at the first postoperative visit (FPOV) after fixation of geriatric hip fractures. Methods: We retrospectively evaluated patients with isolated geriatric hip fractures treated with internal fixation between January 2018 and September 2020. Medical records were reviewed to assess whether radiographs at the FPOV changed management. Direct costs of radiographs at the FPOV were estimated using Medicare Fee Schedule data. Indirect costs were estimated by assessing transportation costs. National costs were estimated by extrapolating institutional and Medicare data to the estimated 300,000 to 500,000 annual hip fractures in the United States. Results: Two hundred forty-one patients were included. A majority had intertrochanteric fractures (80%), were injured because of a ground-level fall (94%), and received long intramedullary nails (73%). One patient (1/241, 0.41%) had their postoperative management changed by FPOV radiographs, and that patient had an acute reinjury before their FPOV. Patients discharged home (50/241, 21%) traveled mean 51.3 miles each way, and those discharged to another facility (191/241, 79%) traveled mean 24.1 miles each way. The national estimated direct cost of routine radiographs at the FPOV totaled $10.9 to $18.2 m annually. The national indirect costs are estimated to be $1.2 to $1.9 m annually for patients discharged home and $63.4 to $105.7 m annually for patients discharged to a facility. Conclusions: Routine radiographs at the FPOV after internal fixation of geriatric hip fractures may not change management and should only be obtained when specifically indicated. Elimination of routine radiographs and conduction of the FPOV virtually by telemedicine could result in national cost savings of estimated $75.5 to $125.8 m annually without compromising quality of care.

Enhancing Efficiency in Industrial Metal Roof Replacement: Utilizing Goal Programming for Cost Optimization

This study explores the use of Goal Programming (GP) to optimise project management decisions in the construction industry, focusing on a case study of a roof replacement project. The construction industry is crucial to a country's development, contributing significantly to economic growth and employment rates. However, construction projects are often complex and resource-intensive. Project management is essential in ensuring the successful completion of construction projects. The study uses GP to address the time-cost trade-off in project management, aiming to minimise project costs while meeting project deadlines and quality requirements. The GP model considers various constraints, such as the availability of workers and equipment, project duration, and budget limitations. The study presents two optimisation options: a time-focused option and a cost-focused ed option. The time-focused option prioritises timely project completion, while the cost-focused option aims to minimise project costs. In the case study, the time-focused option is the most suitable choice. This approach allows the project to be completed in time as the alternative option delay delays the project far too unacceptable. Overall, the study demonstrates the effectiveness of GP in optimising project management decisions in the construction industry. By carefully evaluating project constraints and objectives, GP can help construction companies achieve the best possible outcomes in terms of cost, time, and quality. Keywords: Goal Programming, Construction Industry, Project Management, Time-Cost Trade-off, Optimization

Cost optimization of tall buildings having tube composite columns using social spider algorithm

SummaryThis study aims to develop an algorithmic approach to obtain optimum designs for tall buildings having composite columns and investigate the material cost advantages of these buildings over steel structures. The social spider optimization (SSO) algorithm, a new meta‐heuristic optimization method that has shown promising results in optimizing frame structures, was used to obtain the optimum designs. Concrete‐filled steel tube sections were chosen for composite columns. To define the optimization problem, we considered the material cost of the structure as the objective function, the size of columns (strength, deflection, drift, and geometric limitations) as the constraint functions, and ready steel sections as the design variables. We tested eight different frame structures of varying heights and irregularities to analyze how cost varied according to these parameters. Our results demonstrate that composite columns are a more cost‐effective option than steel structures, even for buildings that are not considered high rises. We found that the difference in cost between the two types of structures increases with building height and irregularity. Additionally, our optimization algorithm was unable to find feasible designs for steel structures taller than 180 m using ready steel profiles.

Multi-objective optimization of a hydrogen production process for polymer membrane coupled with electrochemical hydrogen pump from hydrogen-doped natural gas by NSGA-II

In this work, a novel process of polymer membrane coupled with electrochemical hydrogen pump (EHP) was proposed to achieve efficient hydrogen production from hydrogen-doped natural gas (HDNG), and was simulated by UniSim Design. A hydrogen separation unit of two-stage polymer membrane was used for achieving hydrogen enrichment from low H2 concentration HDNG. Due to the hydrogen upgrade, EHP was operated at a H2 concentration much higher than HDNG, resulting in low energy consumption. Benefiting from the synergy between hydrogen upgrade and purification, the coupling process brought substantial effect while improving the hydrogen purity and recovery rate and reducing the hydrogen separation cost. The real natural gas components were considered into HDNG to ensure the effectiveness of process simulation. An optimization strategy considering both product yield and production cost was proposed to achieve the trade-off between hydrogen recovery rate and separation cost. However, the impact of process parameters on the objectives is nonlinear and difficult to describe with mathematical expressions. Therefore, an agent model of the proposed process was established based on back propagation neural network (BPNN), and multi-objective optimization of H2 recovery rate and total annual cost (TAC) of hydrogen separation unit was achieved using non-dominated sorting genetic algorithm-II (NSGA-II). Investigations with HDNG under various operating conditions (feed pressure of 8–40 bar and feed H2 concentration of 5–25 mol%) were performed, which proved that 90 % overall H2 recovery rate and 99.99 mol% H2 purity of hydrogen product can be achieved at low cost, with the lowest hydrogen separation cost of 1.00 $/kg H2 achievable at 40 bar feed pressure and 25 mol% feed H2 concentration. In summary, this coupling process can provide engineering solutions for efficient hydrogen production from HDNG and promote the development of green hydrogen.

Embodied carbon optimisation of concrete pile foundations and comparison of the performance of different pile geometries

Concrete production and construction are responsible for a considerable share of the total carbon emissions annually. With the emerging need to cut carbon emissions from the construction sector, there has been a big interest in optimising the embodied carbon of superstructures. However, limited attention is given to optimising the embodied carbon of foundations as most designers are more interested in addressing the optimal foundations cost instead. For the first time in the open literature, this paper presents a generic genetic-algorithm-based tool to optimise the design of reinforced concrete piles. The algorithm is capable of allocating the optimal design parameters and geometry for reinforced concrete piles at any given soil conditions and required pile capacities. The algorithm is used to compare the environmental impact of different concrete pile types; solid cylindrical piles, hollow cylindrical piles, solid tapered piles and hollow tapered piles at different load capacities, the optimisation results are then compared to common industrial pile designs to quantify the possible carbon and material savings. Results show that a significant cut of more than 70% of the embodied carbon values can be achieved if the optimal pile type is used compared to common industrial design. Moreover, a finite element model built and validated over ABAQUS is used to compare the geotechnical performance of the different pile geometries. Finite element analysis results show a negligible difference between the stiffness of the different pile types when tested in the same soil conditions highlighting an outstanding optimisation efficiency of the proposed optimisation tool.

A machine learning approach for package size estimation using UHF RFID interrogation signature

AbstractThis paper introduces a new approach for performing package classification and sizing using Radio-Frequency Identification (RFID) systems. This technique is applicable when packages are labeled with or contain multiple RFID-tagged items. During the interrogation of the tags, received signal strength (RSS) statistics and other information, such as the frame count or the reading time, are collected by the reader and used to predict the package type from a set of candidate classes using an Artificial Neural Network (ANN). The primary challenge lies in acquiring sufficient training data for a target scenario to ensure reliable predictions. To address this, a two-phase training process based on transfer learning is adopted. Initially, a base model is developed using synthetic data generated from a detailed RFID simulator, designed to suit diverse scenarios, establish detailed link budgets, and comprehensively simulate the communication protocols. This model is then refined using a small dataset collected experimentally in the actual scenario. This method was validated in a real testbed with four different package types. The base model was trained using 1000 synthetic samples per package type (4000 in total), whereas the refined model was trained with a dataset consisting of only 25 real interrogation traces (samples) per package type (100 in total). The experimental samples were obtained using a software-defined radio unit, the Ettus B210 Universal Software Radio Peripheral (USRP) platform. This experiment achieved an accuracy of over 92%. In summary, this approach introduces a new feature to existing RFID setups, demonstrating potential for advanced package handling and cost optimization in the logistics sector.

Asynchronous verifiable information dispersal protocol of achieving optimal communication

The evolution of distributed systems necessitates robust protocols to facilitate secure and efficient data storage and retrieval amid challenges like asynchronous communication and adversarial threats. However, achieving the theoretical lower bounds of communication cost in Asynchronous Verifiable Information Dispersal (AVID) protocols remains an unresolved issue, prompting the need for innovation in this domain.In this paper, we propose a new AVID protocol designed to optimize communication cost. In contrast to previous approaches, we leverage vector commitment (VC) technology combined with erasure codes, replacing conventional hash tree structures or error correction codes. This approach results in significant O(logn) communication savings. Additionally, our research reveals that while ensuring simultaneous output from all honest nodes is desirable to satisfy Totality, it is not necessary for all nodes to receive corresponding shares or proofs during the Dispersal phase. This insight allows us to circumvent the common O(n2κ) communication overhead.Our AVID protocol achieves an optimal dispersal cost of O(|M|+n2) compared to the prior best of O(|M|+κn2). Furthermore, each node in our protocol incurs a storage cost of O(|M|/n+κ) bits, while the retrieval cost per client is O(|M|+nκ).

Gas Turbine Overhaul Planning for optimum cost and duration execution in the field and workshop refurbishments

Gas Turbine Overhaul Planning for optimum cost and duration execution in the field and workshop refurbishments

Determination of Tree Type Selection in Park and Garden Construction by the Value Engineering Method: Sinanoba Beach Park Example

In order to achieve efficient projects when producing any product, the cost, quality, and production time need to be optimized. However, this is not always easy to achieve. The budgets of the projects may be exceeded, they may not be completed in the stipulated and committed time, or the products may not be produced with sufficient quality for various reasons. It is vital to analyze the project functions/steps very closely throughout the design phase to ensure that all feasible alternatives are studied and examined, and that the most appropriate alternative is picked for the functions that can meet the project criteria. Value engineering (DM) can be defined as an organized effort to analyze product features, functions and material selections; is designed to solve problems and/or reduce costs while maintaining or improving performance and quality requirements; and performs essential functions at the required quality, reliability, and life-cycle cost. In this study, value engineering was used for the selection of trees needed in the construction of parks. A value engineering team decided that the trees should be coniferous with the prerequisite that they can remain green without shedding their summer-winter leaves and determined which criteria the coniferous trees required to be located in the park should meet. The team members conducted value engineering after determining which trees met these criteria and were subsequently purchased. As a result, the team determined the most appropriate optimum cost solution with the value engineering method to meet all the criteria among the determined alternative tree species