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The Spherical Tokamak for Energy Production (STEP) programme is a world-leading fusion power plant programme that has embedded a cost conscience in its design from the early phases. This firmly addresses the attitude of cost complacency of which many major infrastructure projects have historically been accused. While a detailed and highly accurate whole life cycle cost analysis is not possible, or even valuable, during the conceptual design stage, this early design phase is still the most critical programme phase where a focus on costs can drive longer term reductions and impact whole life cycle costs at the high level. Consequently, appropriate estimating methods for these early-stage designs and lessons learned from other industries are used to inform design decisions and ensure cost is part of the overall option analysis. Hence, while the overall programme cost estimate is too immature to be a reliable indicator for the final programme costs, significant effort has been undertaken to understand the major cost drivers and take action to make the STEP design as cost-effective as possible. This article is part of the theme issue 'Delivering Fusion Energy – The Spherical Tokamak for Energy Production (STEP)'. [ABSTRACT FROM AUTHOR]

Today, reverse logistics (RL) is still considered as an emerging field, despite the fact that it has drawn increasing attention over the last decade and now there is a growing interest in the domain of RL than ever before. Although many firms have yet to recognize the capability of effective RL, some firms have realized the strategic importance of RL and initiated deployment of thoughtful investments in functions and systems of their RL practices. Nowadays, RL is considered as a crucial activity for the survival of companies. This article deliberates on RL, cost optimization (CO) and return strategies (RS), along with their implications for small and medium enterprises (SMEs). Information is collected from 420 SMEs using judgemental sampling. On the basis of the literature, the framework of the study is proposed and further analysis is performed using a reliability assessment, normality and adequacy examination, evaluation of the homogeneity of variances, development of the structural model with coefficients and testing of the model through fitment analysis. The outcome of the study highlights that there is no direct causal association between RL and CO; however, there is an indirect association through RS. It is observed that there is a significant cause-and-effect association between RL and RS, as well as a significant cause-and-effect association between RS and CO, in SMEs. [ABSTRACT FROM AUTHOR]

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives. [ABSTRACT FROM AUTHOR]

The cost optimization of structures is one of the options employers consider when designing and analyzing them. Shear walls are one of the factors that influence the total cost of a structure. In this paper, an optimization model based on the BP Neural Network optimized by the genetic algorithm is proposed to optimize the dimensions and location of shear walls, which greatly improves the performance of the BP network and the accuracy can reach more than 96%. At first, ETABS software was used to design and analyze a nine-story concrete building based on ACI318-11 regulations for different shear wall positions and dimensions. In the following, the software outputs have been used for pseudo-neural training to develop a suitable model for estimating seismic performance and shear wall costs of reinforced concrete frames. The cost of the shear wall is considered as a function of wall width, wall length, wall height, concrete characteristic strength, steel yield stress, transverse reinforcement compression ratio, and longitudinal reinforcement compression ratio. To determine the optimal parameters for the shear wall, including its length and position, the genetic algorithm has been used after training and testing the neural network. The results of the present study show that with the proposed method, the presented design can reduce shear wall costs by about 40%. The outcomes of the presented method indicate that using the mentioned method decreases the wall length for walls 1 to 4 with the values of 27%, 11%, 40%, and 19.3%, respectively. [ABSTRACT FROM AUTHOR]

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This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO's tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO's performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE's effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an F r value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE's performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE's advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

This paper provides an analysis of challenges and available solutions for exterior insulated panels suitable for deep energy retrofits of existing building envelopes. The analysis covers a review of available technologies that provide flexible retrofit insulated panels suitable for multiple climates and building typologies. Moreover, the paper proposes a new design for insulated retrofit panels that account for the majority of identified technical risks including cost, architectural diversity, climate variations, structural concerns, moisture resilience, air sealing, and water sealing. Additionally, the proposed design can be easily installed with minimal disruption to the occupants. A series of parametric and optimization analyses is carried out to identify the optimal design specifications for insulated panels suitable for deep retrofits of existing US housing stocks. The analysis results show that the optimal design criteria for the insulated panels can reduce heating and cooling energy consumption by up to 80% and HVAC capacities by 70%. Moreover, the results indicate that these insulated panels are highly cost effective for retrofitting US housing units located in cold climates. [ABSTRACT FROM AUTHOR]

As more and more workflow applications are deployed in the cloud, how to optimize resource scheduling cost while satisfying workflow deadline constraints is a popular research area. In this paper, we propose a cost optimization with deadline constraint (CODC) algorithm. Firstly, workflow tasks are merged to reduce the data transfer overhead between different instances. Secondly, paying attention to the impact of parent and subtasks on the priority of the current task, the case of unmet task sub-deadlines is considered to select the instance that completes task execution earliest. Finally, the experimental results are compared with the existing algorithms on five workflows. CODC algorithm has a lower workflow execution cost compared to other three comparison algorithms. [ABSTRACT FROM AUTHOR]

Dense network deployment is now being evaluated as one of the viable solutions to meet the capacity and connectivity requirements of the fifth-generation (5G) cellular system. The goal of 5G cellular networks is to offer clients with faster download speeds, lower latency, more dependability, broader network capacities, more accessibility, and a seamless client experience. However, one of the many obstacles that will need to be overcome in the 5G era is the issue of energy usage. For energy efficiency in 5G cellular networks, researchers have been studying at the sleeping strategy of base stations. In this regard, this study models a 5G BS as an M [ X ] / G / 1 feedback retrial queue with a sleeping strategy to reduce average power consumption and conserve power in 5G mobile networks. The probability-generating functions and steady-state probabilities for various BS states were computed employing the supplementary variable approach. In addition, an extensive palette of performance metrics have been determined. Then, with the aid of graphs and tables, the resulting metrics are conceptualized and verified. Further, this research is accelerated in order to bring about the best possible (optimal) cost for the system by adopting a range of optimization approaches namely particle swarm optimization, artificial bee colony and genetic algorithm. [ABSTRACT FROM AUTHOR]

A distributed archetype, the concept of fog computing relocates the storage, computation, and services closer to the network's edge, where the data is generated. Despite these advantages, the users expect proper load management in the fog environment. This has expanded the Internet of Things (IoT) field, increasing user requests for the fog computing layer. Given the growth, Virtual Machines (VMs) in the fog layer become overburdened due to user demands. In the fog layer, it is essential to evenly and fairly distribute the workload among the segment's current VMs. Numerous load-management strategies for fog environments have been implemented up to this point. This study aims to create a hybridized and optimized approach for load management (HOGWO), in which the population set is generated using the Invasive Weed Optimisation (IWO) algorithm. The rest of the functional part is done with the help of the Grey Wolf Optimization (GWO) algorithm. This process ensures cost optimization, increased performance, scalability, and adaptability to any domain, such as healthcare, vehicular traffic management, etc. Also, the efficiency of the enhanced approach is analyzed in various scenarios to provide a more optimal solution set. The proposed approach is well illustrated and outperforms the existing algorithms, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), etc., in terms of cost and load management. It was found that more than 97% jobs were completed on time, according to the testing data, and the hybrid technique outperformed all other approaches in terms of fluctuation of load and makespan. [ABSTRACT FROM AUTHOR]

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing. [ABSTRACT FROM AUTHOR]

Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things applications. In order to meet the users' demands and provide a cost-effective solution, cloud service providers offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

Cutting production costs is a crucial instrument for companies to increase profitability and remain competitive. However, companies aim to cut costs without compromising performance requirements of the products. Commonality between components reduces the costs, while diversity between them differentiates the key attributes of the products in a product family. This commonality-diversity trade-off is the essence of the product family design optimization. Current model-based methodologies consider optimizing the design for commonality rather than cost. This article proves that higher commonality does not always amount to minimal cost and, therefore, a simple commonality index cannot replace a cost model. A tunable cost model that includes simplified formulas for standardization benefits is introduced to be used by cost optimization techniques. Current product family optimization methods are often combinatorial and perform inefficiently due to searching large design space. These methods also optimize the product family design from scratch. This limits the applicability of the current methods in industrial settings that are typically complex and brownfield. This article proposes a model-based cost optimization methodology that accelerates the cost optimization by starting from an existing design. The proposed methodology is a two-stage nested optimization algorithm, in which the commonality matrix is optimized with sensitivity analysis on component types in the outer loop and the associated design variables are optimized in the inner loop. The methodology is numerically demonstrated on an industrial example and benchmarked against state-of-the-art cost optimization methods. The proposed methodology ensures a gradual improvement in cost reduction with a significant acceleration in performance. [ABSTRACT FROM AUTHOR]

Government allocated funds earmarked toward the improvement of recreational amenities like parks and playgrounds in developing nations are usually scarce, leading to their disrepair and encroachment. Provision of amenities and vegetation development are vital features of open spaces and the associated costs form a major share of the budget allocations. With the limited resources available for open spaces' development, it is crucial to utilize the said funds judiciously. This paper investigates the cost implications of providing amenities and vegetation in urban Indian open spaces and devices a framework to improve the open space performance score cost-effectively. The associated fund utilization is optimized for select open spaces using the graphical representation of pinch analysis. Four scenarios of development namely greenfield, brownfield, vegetation-focused development and amenities-focused development are tested for four open spaces of diverse sizes and performance scores. The inefficiency associated with fund utilization is highlighted using the pinch diagram. Furthermore, in most cases, it is observed that the provision of amenities prior to vegetation development provides swifter increase in ROS performance. The study demonstrates a novel method to evaluate the cost implications of open space development for decision-makers and suggests pertinent policy recommendations [ABSTRACT FROM AUTHOR]

Meeting Germany's climate targets urgently demands substantial investment in renewable energies such as hydrogen, as well as tackling industrial CO2 emissions with a strong CO2 transport infrastructure. This is particularly crucial for CO2-heavy industries such as steel, cement, lime production, power plants, and chemical plants, given Germany's ban on onshore storage. The CO2 transport network is essential for maintaining a circular economy by capturing, transporting, and either storing or utilizing CO2. This study fills gaps in CO2 pipeline transport research, examining pipeline diameters, costs, and pressure drop, and providing sensitivity analysis. Key findings show that the levelized cost of CO2 transport (LCO2T) ranges from 0.25 €/t to 55.82 €/t based on varying transport masses (1000 t/day to 25,000 t/day) and distances (25 km to 500 km), with compression costs pushing LCO2T to 33.21 €/t to 92.82 €/t. Analyzing eight pipeline diameters (150 mm to 500 mm) and the impact of CO2 flow temperature on pressure loss highlights the importance of selecting optimal pipeline sizes. Precise booster station placement is also crucial, as it significantly affects the total LCO2T. Exploring these areas can offer a more thorough understanding of the best strategies for developing cost-effective, efficient, and sustainable transport infrastructure. [ABSTRACT FROM AUTHOR]

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly. [ABSTRACT FROM AUTHOR]

The transient and metaheuristic cost analysis of a MX/G(a, b)/1 retrial queue with random failure during an extended Bernoulli vacation with impatient clients is covered in this study. Any batch that arrives and discovers the server is busy, down, or on vacation joins an orbit. In the alternative, only one new customer from the group joins the service right away, while the others join the orbit. After providing each service, the server either waits to serve the following customer with probability (1 - θ) or goes on vacation with probability θ. It has been found that these systems express steady-state solutions and are dependent on time probability generating functions in consideration of their Laplace transforms. We also discuss a few exceptional and particular instances. After that, the impact of different parameters on the system's effectiveness is evaluated. We are also talking about ANFIS. Additional approaches employed in this study to swiftly determine the system's optimum cost include genetic algorithms (GA), artificial bee colonies (ABC), and particle swarm optimization (PSO). We also examined the graph-based convergence of several optimization algorithms. [ABSTRACT FROM AUTHOR]

In response to the construction of green data centers for sustainable development, reducing data center power consumption and carbon dioxide emissions has become an urgent issue. A data center model considering cooling systems was established. The model transforms cost functions of data center power costs, bandwidth costs, carbon emission costs, and cooling costs into a cost minimization problem. The data center operational costs are reduced by effective workload scheduling and rational servers on-off strategies. The electricity prices and carbon emission rates are difficult to predict accurately due to the fluctuation. A feature of the Lyapunov optimization framework was utilized to propose an online control strategy. The strategy only requires knowledge of the current system information. An alternating direction method of multipliers (ADMM) is introduced to enable the control center to coordinate workload distribution among various data centers. Data centers only need to exchange workload decisions, effectively reducing computational complexity and safeguarding user privacy. Simulation results based on real-world data demonstrate that the proposed algorithms can effectively reduce data center operational costs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the widespread adoption of electric vehicles (EVs), the demand for public charging services is steadily increasing. Consequently, the development of effective charging scheduling strategies, aimed at optimizing the utilization of limited charging infrastructure, has become a key problem. Considering the diversity of user demands, we propose a Cost-Aware Charging Scheduling Architecture (CASA). This architecture considers both urgent and nonurgent charging customers by designing two charging modes with different power levels and associated costs. However, optimizing multiple objectives simultaneously while ensuring the interests of all parties involved in the charging demand response presents a challenge. Moreover, the uncertainty in customer charging demands and Time-of-Use (TOU) tariff further complicates the establishment of the model. To address the aforementioned challenges, this study formulates EV charging scheduling as a Markov Decision Process (MDP) based on deep reinforcement learning (DRL), employing the Deep Q-Network (DQN) algorithm for solution derivation. The objective is to minimize the operational costs of charging stations while ensuring the quality of service (QoS) requirements for customers. The simulation results demonstrate that CASA exhibits superior performance in optimizing both the average response time and service success rate, compared to commonly used baselines for charging scheduling. Furthermore, the CASA approach achieves a significant reduction in operating costs of EV charging station. [ABSTRACT FROM AUTHOR]

This study proposes an ammonia-fed cascade recirculation system comprising: (1) two stacks connected in series and (2) an anode off-gas recirculation configuration to enhance electrical efficiency of the system. The proposed system is both energetically and economically investigated. Under energetic analysis, the effects of fuel utilization, temperature, and recirculation rate, are clarified. Information from energetic analysis is employed to investigate levelized cost of electricity (LCOE) value in the entire working range. Due to the high potential of the cascade configuration, a cascade system without recirculation is also investigated using the same methods. The results reveal that the cascade-only and cascade recirculation systems obtain electrical efficiency up to 67.78% and 68.80%, respectively. The minimum LCOEs are 0.485 and 0.486 $/kWh, respectively, which are both higher than grid electricity cost; however, compared with other ammonia-fed SOFC systems, the two cascade configuration-based systems show significant improvements in both efficiency and cost. • A novel cascade recirculation configuration was proposed for ammonia SOFC system. • The system was investigated in both thermodynamics and economic points of views. • Net electrical efficiency of the proposed system can reach to 68.80%. • Minimum levelized cost of electricity (LCOE) of the proposed system is 0.486 $/kWh. • The system is superior than the others in both cost and efficiency. [ABSTRACT FROM AUTHOR]

The application of Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) as meta-heuristic techniques for cost optimization of composite beams (CBs) was evaluated. The performance of PSO and GWO was compared to the existing techniques in the literature, and the results demonstrated that the cost-saving achieved with PSO and GWO was significantly better. The PSO and GWO were also compared to Social Harmony Search (SHS) and the finding indicated that GWO generated better solutions. A parametric study and sensitivity analysis were conducted using GWO to investigate the effectiveness of different load combinations, beam spans, and slab thicknesses on optimal total cost. The results showed that the optimum cost was sensitive to slab thickness, highlighting the importance of selecting an appropriate thickness in CBs' design. [ABSTRACT FROM AUTHOR]

The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city's economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain. [ABSTRACT FROM AUTHOR]

Soil contamination is a significant environmental issue, and a cost-effective and environmentally friendly remediation method is essential for effective management. This paper has presented a cost analysis of the hybrid ultrasonication/ozonation (HUO) technology for the remediation of industrial sites contaminated with polycyclic aromatic hydrocarbons (PAHs). The economic feasibility of this HUO technology was evaluated and then compared to other soil remediation methods proposed by the EPA. Using the experimental and mathematical optimization, 98 optimal modes of the proposed technology, achieving a removal efficiency of 71 ± 1%, in terms of both capital expenses (CAPEX) and operational expenses (OPEX), were identified. It turned out that the average annual costs for the HUO technology were divided into 33% for OPEX and 67% for CAPEX. The most expensive component in the CAPEX was ultrasound units at 45%, followed by uncertainty, exclusive equipment, installation engineering, contractor, and site work. Regarding the OPEX, components replacement was the most contributor at 39%, followed by electrical energy, labor costs, and laboratory analysis at 36%, 23%, and 2%, respectively. The study found that the proposed method was less costly than soil washing and comparable to thermal desorption at $160/ton. It was concluded that the proposed technology was cost-effective and environmentally friendly for the remediation of industrial sites contaminated with PAHs. The paper provides valuable insights into the economic feasibility of the proposed technology, which can be significant for researchers, managers, and governmental authorities, who are responsible for selecting appropriate remediation methods for contaminated industrial sites. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. This paper presents EnergyShare AI, a technology that connects consumers and prosumers through solar arrays, energy storage systems (ESS), and electric vehicles (EVs). Using Deep Reinforcement Learning (DRL) algorithms, Energy Share AI significantly improves energy management efficiency and substantially reduces costs. Our approach offers several advantages over traditional linear integer programming models, particularly in optimizing bidirectional energy transfer involving EVs and highlighting the critical role of ESS and photovoltaic (PV) systems in facilitating efficient P2P energy trading. Our research results show that successful P2P exchange can lead to significant cost savings and improved sustainability, thereby increasing the amount of energy transferred between different household profiles and stages of human development.

Batch production is a modern strategy for manufacturing, especially for products unsuitable for continuous assembly lines. This study proposes a method for analysing one such production facility. Here, we characterize a batch production system as a unique sort of quorum feedback retrial queue deployed under Bernoulli working vacation and outfitted with a secondary, stand-by server. The system’s dynamics are thoroughly examined using the supplementary variable technique, providing insights into its behaviour and performance. Key metrics are determined, and the impact of specific parameters is demonstrated through numerical examples. Furthermore, this research aims to achieve the optimal system cost by employing a variety of optimization approaches. Thus, optimizing costs and identifying key metrics are crucial for enhancing efficiency and profitability in batch production systems. This process ensures that resources are used effectively, service quality is maintained, and overall operational costs are reduced, leading to a more sustainable and profitable production system which helps to make informed decisions.

The growing demand for easily available healthcare in recent years has fuelled the digitization of healthcare services. The Hospital Management System (HMS) software stands out as a comprehensive solution among the software systems and tools that hospitals and clinics are developing in tandem with this trend. In order to effectively manage many facets of hospital operations, in this paper, we propose an approach for investigating software of this kind. Thus, we characterise the HMS software as a unique sort of batch arrival retrial queueing system (QS) that can handle both ordinary and priority patient demands. Furthermore, it permits patient resistance (balk) and departure (renege) in specific circumstances. The proposed model is additionally deployed within the framework of Bernoulli working vacation. The supplementary variable technique (SVT) has been utilised to obtain the necessary results. ANFIS, a soft computing tool, is used to validate the analytical results as well. Finally, this study seeks to enhance the cost-effectiveness of software creation by employing four unique optimization methods, aiming to achieve optimal efficiency in resource utilization.

The manufacturing industry has been pursuing an efficient and economical assembly system. By considering assembly line balancing (ALB) and part feeding (PF) as an integrated problem and programming them simultaneously opens additional opportunities to improve the performance of the entire assembly system. However, the integrated ALB and PF problem is a non-deterministic polynomial (NP) hard problem. This implies that exact solutions cannot be obtained in a reasonable computation time and its near-optimal solutions can only be realised by meta-heuristics. In this study, we propose a novel bi-level multi-objective genetic algorithm (NBMGA) to solve the integrated ALB and PF problem. First, a bi-level mathematical model is established to simultaneously minimise the number of stations and workload smoothness of ALB in the upper level as well as the number of supermarkets of PF in the lower level. Second, the NBMGA with two modified strategies, including extending fitness evaluation and adaptive termination condition, is designed for problem solving. Finally, a series of computational experiments are conducted to demonstrate the efficacy of the proposed algorithm. The computational results indicate that the proposed algorithm outperforms the bi-level nondominated sorting genetic algorithm (NSGA) II in terms of the approximation to the true frontier without sacrificing computational efficiency. [ABSTRACT FROM AUTHOR]

. Electrical power is the second most important commodity in electrical energy markets. For consumers, the charged amount of “generator” power is determined as the average value of hourly consumption amounts on working days during peak hours in the region. The cost of power in some regions can reach 40 % of the final tariff, so reducing the load during peak hours by 10 % can lead to a decrease in monthly consumer payments by 3 %. However, such a way of saving money is not available to the consumer since the commercial operator of the wholesale market of electricity and capacity publishes the peak hours of the regions after the 10th day of the next month, when this information is no longer relevant. Timely forecasting of peak hours will make it possible, on the one hand, to reduce consumer costs for payments for electric power, and on the other hand, to smooth out the daily schedule of electric load of the power system, thereby optimizing the operation of generating equipment of stations and networks of the system operator. The article presents a study of the effectiveness of machine learning methods in the context of forecasting the peak hour of a regional power system. The study concerns the period from November 2011 to October 2023, covers 76 regions of the Russian Federation, including subjects of price (1st and 2nd) and non-price zones and includes 10 machine-learning methods. The results of the study showed that statistically, the K-nearest neighbors clustering method turns out to be the most accurate, although not universal. Support Vector Classifier and Decision Tree Classifier have demonstrated high efficiency (in terms of accuracy and speed). The study also refuted the assumption that the closest data in terms of time series has the greatest value in predicting peak hours.

This paper presents a peer-to-peer community cost optimization approach based on a single-prosumer energy management system. Its objective is to optimize energy costs for prosumers in the community by enhancing the consumption efficiency. This study was conducted along two main axes. The first axis focuses on designing a digital twin for a residential community microgrid platform. This phase involves data collection, cleaning, exploration, and interpretation. Moreover, it includes replicating the functionality of the real platform and validating the results. The second axis involves the development of a novel approach that incorporates two distinct prosumer behaviors within the same community microgrid, while maintaining the concept of peer-to-peer energy trading. Prosumers without storage utilize their individual PV systems to fulfill their energy requirements and inject excess energy into a local microgrid. Meanwhile, a single prosumer with a storage system actively engages in energy exchange to maximize the community’s profit. This is achieved by optimizing battery usage using a cost optimization solution. The proposed solution is validated using the developed digital twin.

The study focuses on optimizing urban distribution in the dairy industry, specifically at Bhopal Sanchi dairy, to enhance efficiency and minimize costs. It evaluates existing routes, identifies inefficiencies, and proposes new distribution strategies. By implementing these changes, significant improvements are achieved, reducing total distribution costs and dead kilometers while improving vehicle capacity utilization. The study emphasizes the importance of addressing urban distribution challenges to meet increasing milk demand efficiently. It contributes to developing sustainable dairy distribution systems by optimizing costs and minimizing losses. ArcGIS software is utilized for route and fleet optimization analysis. Overall, the research addresses the critical need for efficient urban dairy distribution, particularly in India’s context as a major milk producer.

The distributed matrix multiplication problem with an unknown number of stragglers is considered, where the goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across N servers. There are up to N - R stragglers but the exact number is not known a priori. Motivated by reducing the computation load of each server, a flexible solution is proposed to fully utilize the computation capability of available servers. The computing task for each server is separated into several subtasks, constructed based on Entangled Polynomial codes by Yu et al. The final results can be obtained from either a larger number of servers with a smaller amount of computation completed per server or a smaller number of servers with a larger amount of computation completed per server. The required finite field size of the proposed solution is less than 2N. Moreover, the optimal design parameters such as the partitioning of the input matrices are discussed. Our constructions can also be generalized to other settings such as batch distributed matrix multiplication and secure distributed matrix multiplication.

In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators' attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable. [ABSTRACT FROM AUTHOR]

Steep slopes in soft rock are characterized by their susceptibility to instability (rockfall, rockslide) due to weathering and erosion of the slope surface. This article deals with the problem of adapting to the increasing height of the scree slope. The construction of a retaining wall in a scree slope in front of a slope of soft rock with a steep face, where a very rapid weathering and erosion process of weathered material takes place, and the simultaneous deposition of material in front of the steep slope is a common solution. Changes in the geometry of the slope and the front scree are taken into account, and at the same time, sufficient safety against rockfall must be ensured. The analysis is shown on a specific example of a steep flysch slope near Split, Dalmatia. The retaining wall solutions are compared in terms of function, cost and sustainability. The construction of a single colossal, reinforced concrete retaining wall shows that this solution is not feasible due to the high construction costs and CO2 emissions of the retaining wall. A model was therefore developed to determine the height of the retaining walls for different construction time intervals and distances from the original rock face. The critical failure modes were investigated for various retaining wall solutions with regard to the highest degree of utilization of the resistance, which also allows the cost-optimized solutions to be determined. By building two or more successive retaining walls at suitable intervals and at an appropriate distance from the original rock face, construction costs and CO2 emissions can be significantly reduced. [ABSTRACT FROM AUTHOR]

Over the years, acceptance sampling plans have been crucial to quality assurance in manufacturing. Sample plans are designed using operating characteristic curve conditions to safeguard producers and customers. We propose a conditional probability-based Bayesian generalized multiple-dependent state sampling technique in this paper. The technique relies on Gamma-Poisson distribution. Other performance indicators and acceptance probability are calculated. Also, the new plan's operational method is discussed. The proposed technique is also compared to current attribute sampling schemes for efficacy. Optimal plan parameters for the plan's economic structure are also generated, adding managerial insights to the suggested plan. The entire cost study showed that the suggested plan is cheaper than existing sample plans under identical conditions. To account for inspection flaws, the plan is adjusted. We examine how Type I and Type II errors affect sampling plan outcomes. The plan is demonstrated with numerical examples and a data-driven application. [ABSTRACT FROM AUTHOR]