Optimal Cost Research Articles

Cost-optimization based target reliabilities for design of structures exposed to fire

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment. Cost-benefit analyses, however, are complicated and computationally challenging, and hence impractical for application to individual projects. To address this issue, design guidance proposes target reliability indices for normal design conditions, but no target reliability indices are defined for structural fire design. We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures. We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions. The optimum safety level is expressed as a function of a new dimensionless variable named “Damage-to-investment indicator” (DII). The cost optimization approach is validated for the target reliability indices for normal design condition. The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures. Two case studies are presented: (i) a one-way loaded reinforced concrete slab and (ii) a steel column under axial loading. This study thus provides a framework for deriving optimum (target) reliability index for structural fire design which can support the development of rational provisions in codes and standards.

Multi-microgrids system’s resilience enhancement

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs.

Cost optimization of electricity in energy storage system by dynamic programming

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.

Construction Planning and Scheduling of Residential Buildings Using Primavera

Abstract: For the successful completion of a project, planning and scheduling are two most important factors. The demand of the construction industry requires precise planning, scheduling, and management which can allow the overall optimization of cost, time, and resources. Due to the increase in workloads and shrinking resources public work department found new technology that helps to manage the project easily. Project management software is used as a tool for managing and organizing work which helps industries to grow rapidly. There are so many computer software are available in market now a days which is such as MSP, Primavera p6,etc…for doing project management. With the help for these software proper planning and controlling of project can be done. Primavera can easily compare the planned progress of construction work and the actual progress of the construction project. Project management software Primavera P6, includes collecting, recording, monitoring, controlling, and reporting information concerning project performance. In this study, project controlling and monitoring can be done and the causes of delays can be found.

A novel distributed finite-time economic dispatch for DC microgrids with time delays

This paper delineates an advanced distributed control paradigm for finite-time generation cost optimization in DC Microgrids (MGs), taking into account time delays. Firstly, to enhance system stability with time delays, Artstein's reduction approch is utilized to convert the time-delayed system into an equivalent delay-free model. Subsequently, a finite-time consensus-based distributed algorithm for cost optimization is formulated to equilibrate the incremental costs (ICs) of all distributed generators (DGs) in a predefined convergence time, while adhering to the DGs' capacity limits. Moreover, a distributed control mechanism for finite-time voltage stabilization is developed for sustaining the balance between the generated powers and consumptions across the MG. Rigorous convergence analysis proves that the proposed controller maintains finite-time stability. The efficacy of this innovative control strategy is substantiated through a series of comprehensive case studies.Top of Form

Assessment of reliability measures and cost optimization of a solar seed sowing machine

PurposeIn this research work, the general form of reliability measures, which include availability, mean time to failure (MTTF), and sensitivity analysis, are investigated with their graphical representation, which would help designers and engineers improve the reliability of the system. Along with reliability assessment, a mathematical model is developed and solved to achieve the minimum cost of the system as well as maximum reliability.Design/methodology/approachIn the proposed work, a general model of a solar seed sowing machine is considered for reliability evaluation using the Markov model process. The proposed system is a series-parallel arrangement of components where three components, namely the solar panel, batteries and direct current (DC) motor, are connected in series while all the operators are connected in parallel. The implemented Markov model approach assesses several parameters of reliability, which opens the scope for improvement in reliability and other measures like mean time to failure (MTTF) and sensitivity of the proposed system. So that the machine can deliver the desired output on the field. Also, the particle swarm optimization (PSO) algorithm is applied to optimize the cost of the system with the desired level of reliability.FindingsImplementation of PSO provides the optimal cost for the proposed system with a predetermined level of reliability, which shows the relationship between reliability and cost of the system. Also, the Markov process approach provides the availability function, reliability function, reliability at different time values, MTTF and sensitivity of the proposed system.Originality/valueThis work evaluates the crucial characteristics of reliability for the proposed solar seed sowing machine using the Markov model which is a stochastic model. The assessment of reliability measures such as MTTF, availability and sensitivity plays a vital role in measuring and improving the performance of the machine in the actual environment. Examining the data obtained in this research is of great importance for the manufactures and system designers. Also, the powerful optimization technique PSO is implemented to solve a non-linear mixed-integer programming problem that provides the optimal cost for the system with desired reliability.

Real-time channel selection in multi-mode multiplexing optical interconnection implemented by hybrid algorithm and material system

Multi-mode multiplexing optical interconnection (MMOI) has been widely used as a new technology that can significantly expand communication bandwidth. However, the constant-on state of each channel in the existing MMOI systems leads to serious interference for receivers when extracting and processing information, necessitating introducing real-time selective-on function for each channel in MMOI systems. To achieve this goal, combining several practical requirements, we propose a real-time selective mode switch based on phase-change materials, which can individually tune the passing/blocking of different modes in the bus waveguide. We utilize our proposed particle swarm optimization algorithm with embedded neural network surrogate models (NN-in-PSO) to design this mode switch. The proposed NN-in-PSO significantly reduces the optimization cost, enabling multi-dimensional simultaneous optimization. The resulting mode switch offers several advantages, including ultra-compactness, rapid tuning, nonvolatility, and large extinction ratio. Then, we demonstrate the real-time channel selection function by integrating the mode switch into the MMOI system. Finally, we prove the fabricating robustness of the proposed mode switch, which paves the way for its large-scale application.

A Sensor to Monitor Soil Moisture, Salinity, and Temperature Profiles for Wireless Networks

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted.

Design and validation of the evolved version of the tactical separation system

Purpose The small air transport (SAT) domain is gaining increasing interest over the past decade, based on its perspective relevance in enabling efficient travel over a regional range, by exploiting small airports and fixed wing aircraft with up to 19 seats (EASA CS-23 category). To support its wider adoption, it is needed to enable single pilot operations. Design/methodology/approach An integrated mission management system (IMMS) has been designed and implemented, able to automatically optimize the aircraft path by considering trajectory optimization needs. It takes into account both traffic scenario and weather actual and forecasted condition and is also able to select best destination airport, should pilot incapacitation occur during flight. As part of the IMMS, dedicated evolved tactical separation system (Evo-TSS) has been designed to provide elaboration of both surrounding and far located traffic and subsequent traffic clustering, to support the trajectory planning/re-planning by the IMMS. Findings The Clean Sky 2-funded project COAST (Cost Optimized Avionics SysTem) successfully designed and validated through flight demonstrations relevant technologies enabling affordable cockpit and avionics and supporting single pilot operations for SAT vehicles. These technologies include the TSS in its baseline and evolved versions, included in the IMMS. Originality/value This paper describes the TSS baseline version and the basic aspects of the Evo-TSS design. It is aimed to outline the implementation of the Evo-TSS dedicated software in Matlab/Simulink environment, the planned laboratory validation campaign and the results of the validation exercises in fast-time Matlab/Simulink environment, which were successfully concluded in 2023.

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

Cost Optimization in Cloud Computing: Capacity Reservation for Intermittent Random Demand Surges

The adoption of cloud computing has been accelerating over the last decade, while enterprise cloud users (“firms”) are struggling to manage their growing cloud expenditures in the face of intermittent digital demand surges caused by planned or random events. To deal with such challenges, a firm can employ reserved instances with the standard contract length (e.g., one year) to meet the stationary base demand, which we refer to as the base contracts, complemented by additional reserved instances with either standard or shorter contract lengths, which we refer to as the supplementary contracts, to cope with the demand surges. We first analyze a model whereby the surge and inter-surge durations are deterministic, demand magnitude is random, and cancellation of the reservations is allowed. We develop a capacity management plan for the firm including not only the optimal capacity levels, which follow a newsvendor-type solution, but also the optimal policy for managing the purchase, renewal, cancellation, or expiration of the supplementary contracts, which can be characterized as a two-threshold policy. Due to the complexity of the structure of the optimal policy, we also construct an effective heuristic policy by excluding the renewal option from the action space, which can be applied to a more general setting where the surge and inter-surge durations are random. We examine two model extensions: (1) when trades of reserved instances are allowed in a secondary marketplace; (2) when the firm does not have exact information about the distributions of the surge magnitude and duration while it can adjust the capacity levels as data unveils. Our analysis shows that the optimal policy for managing the supplementary contracts depends on the relative magnitude of the surge and inter-surge durations in relationship to the cancellation fee. Moreover, our numerical results show that cloud platforms that offer a secondary marketplace are more attractive to firms from a cost standpoint than those that allow cancellation only. The latter, without the secondary marketplace, however, can achieve parity with the former by offering a deeper discount rate for the reserved instances, thereby bypassing the cost of administering the secondary marketplace.

Adaptive event-triggered optimal [formula omitted] tracking control for uncertain nonlinear systems: Comparative analysis applied to autonomic tractor-trailer system

In this paper, an event-triggered tracking optimization control method is proposed for nonlinear uncertain systems with H∞ discounted cost. First, the linearization method using the infinite series expansion theorem is given. And the tracking control design problem is solved by appropriate state augment. Secondly, the zero sum differential game strategy is introduced to address the control design problem of uncertain systems with event-triggered inputs. The actor-critic-disturbance networks framework is used to approximate the optimization of event- triggered controller strategy, optimization costs, and the disturbance strategy, respectively. Due to the consideration of discrete time and continuous time dynamics, we put the impulsive system method to use and choose appropriate Lyapunov functions to prove stability and boundedness, at the same time, there will be no infinite triggering situation. Finally, the effectiveness of the proposed scheme is demonstrated by an example of a practical tractor system with n trailers through simulation compared with another typical design method.

A Comparative Study of Fertilizer Prices Purchased by Farmers from Rythu Bharosa Kendras (RBKs) and Other Agencies in Andhra Pradesh

Rythu Bharosa Kendras - a one stop solutions for all agriculture & allied issues, was launched by state government of Andhra Pradesh in 2020. It addresses various issues and provide services to the farmers at the village level. Fertilizers are one among the services offered at Rythu Bharosa Kendras. The current study was carried out during the year 2022 and 2023 to find any significant variation in the average price of the fertilizers purchased by the farmers from Rythu Bharosa Kendras versus other agencies in Tirupati and Chittoor districts of Andhra Pradesh. A representative sample of 120 farmers in two districts of Andhra Pradesh i.e., Tirupati and Chittoor were selected for the study. Through a pre-structed interview schedule, prices of fertilizers purchased by the farmers from Rythu Bharosa Kendras vs other agencies were collected. In the study area, other agencies from where farmers purchased fertilizers included private fertilizer wholesalers, retailers, fellow farmers, non-institutional financers, etc. To study the average price comparison, the set of fertilizers that were commonly purchased at Rythu Bharosa Kendras and other agencies were selected. They were Urea, Diammonium phosphate (DAP), Muriate of Potash (MOP), N20:P20:K0:S13 (N:P:K:S fertilizer grade), N10:P26:K26 (N:P:K fertilizer grade). From the data collected and through descriptive statistical analysis it was clear that for all the common fertilizers the average amount of fertilizer per bag at Rythu Bharosa Kendras was comparatively lower than that of fertilizers purchased at other agencies. The average price variation of fertilizer per bag at Rythu Bharosa Kendras to other agencies for Urea, DAP, MOP, N20:P20:K0:S13 grade, N10:P26:K26 grade were Rs. 27.5, Rs. 40, Rs. 33, Rs. 44 & Rs. 92 with a variation in percentage by 10.3%, 2.96%, 2.84%, 3.26% & 6.31% respectively. By ensuring the prompt availability of required set of fertilizers in line with the cropping pattern of the farmers and by streamlining the supply chain aspects of fertilizes, especially from fertilizers hub to Rythu Bharosa Kendras, will be beneficial & results in cost optimization to the farmers.

A Comparative Smart Computation Algorithm for Economic And Emission Dispatch Optimization of Tanjung Jati Power Plant

This research delves into optimization strategies for enhancing power generation efficiency and reducing costs in PLTU Tanjung Jati. The study explores three computational algorithms for optimization: Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), focusing on optimizing power generation costs while considering penalty costs and emissions. Through extensive simulations and evaluations, the results indicate that the GWO algorithm achieves the most optimal outcomes in terms of cost optimization, demonstrating fast convergence and lesser simulation time compared to the other algorithms. The research provides valuable insights into selecting optimal algorithms for complex optimization problems like Combined Economic Emission Dispatch (CEED) in power generation systems, ultimately contributing to more efficient and cost-effective power generation.

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.

Identifying solutions to minimize meal tray waste: A mixed-method approach

Background & aimsOver $150,000 are lost annually due to meal tray waste in a large hospital in the United States. This study aims to explore the root causes of meal tray waste within a decentralized foodservice model and strategies to mitigate waste. MethodsA mixed methods sequential explanatory design was used by first identifying hospital units high and low in meal tray waste using recorded food management data from January 2021 through September 2022, then conducting observations, mobile-interviews (n=16), and in-depth interviews with hospital foodservice staff (n=6) and nurses (n=6) in each unit. Lastly, hospital and topic experts (n=10) were engaged in strategizing solutions to reduce meal tray waste. ResultsFindings indicate meal tray waste is increased when patients discharge and when standard trays (i.e., also known as house trays, which include items not requested by patients) are provided. Meal tray waste points to the unpredictability in a hospital that often arises due to patient circumstances, lack of coordination between nursing and foodservice staff, patients' food preferences, and the need for system and workflow improvements in a decentralized foodservice model. ConclusionsFindings highlight considerations that may be applicable across diverse medical institutions seeking to reduce meal tray waste. Hospitals should choose the best foodservice model that suits their institution to manage operations efficiently, focusing on reducing waste, cost optimization, patient satisfaction, and sustainability.

Design approach for carbon-neutral and resilient residential communities: Case study of Tabuk region

In this paper, a systematic analysis approach is outlined to design carbon neutral and resilient residential communities in Tabuk region, Saudi Arabia. The design used cost optimization approach to determine the required capacities for on-site power generation and energy storage using renewable energy resources to achieve desired carbon neutrality and energy resiliency thresholds. Moreover, different energy efficiency levels for the housing units are considered to design grid-connected residential communities based on prevalent grid electricity prices and carbon emissions. The analysis indicates that combinations of energy efficiency measures, PV systems, and storage batteries can be utilized to achieve both desired carbon neutrality and energy resiliency levels. The implementation of proven energy efficiency measures can reduce by over 45 % both the annual electricity demands, and the on-site PV capacities needed to reach carbon neutral designs for the residential communities. The cost-effectiveness of carbon-neutral and resilient designs depends significantly on installation costs of PV systems and wind turbines as well as electricity prices purchased and sold back to the grid. The levelized cost of energy for net-zero energy communities can be as low as 0.06 USD/kWh to reach carbon neutrality.

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.