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 con...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.展开更多
Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, b...Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, because PWWF is a variable stiffness multi-agent structure, with non-linear loading and deformation coordination. In this paper, cost optimization method of large-scale PWWF by multiple-island genetic algorithm （MIGA） is presented. Optimization design flow and optimization model are proposed based on variable-tension wire winding theory. An example of the PWWF cost optimization of isostatic equipment with axial load 6 000 kN is given. The optimization cost is reduced by 21.6% compared with traditional design. It has also been verified by the finite-element analysis and successfully applied to an actual PWWF design of isostatic press. The results show that this method is efficient and reliable. This method can also provide a guide for optimal design for ultra-large dimension muti-frame structure of 546 MN and 907 MN isostatic press equipment.展开更多
This paper introduces a new study on cost optimization of surface grinding. In the study, the effects of grinding parameters including the dressing regime parameters, the wheel life and the initial grinding wheel diam...This paper introduces a new study on cost optimization of surface grinding. In the study, the effects of grinding parameters including the dressing regime parameters, the wheel life and the initial grinding wheel diameter on the exchanged grinding wheel diameter which were investigated. In addition, the influence of cost parameters including the machine tool hourly rate and the grinding wheel cost were taken into account. In order to find the optimum exchanged grinding wheel diameter, a cost optimization problem was built. From the results of the optimization problem, a model for determination of the optimum exchanged grinding wheel diameter was found. By using the optimum diameter, both the grinding cost and grinding time can be reduced significantly.展开更多
In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the...In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.展开更多
Due to the state of the real estate industry, still adhere to the "house is used to live, not to fry" overall positioning, and put forward "explore new development model, adhere to rent and, accelerate ...Due to the state of the real estate industry, still adhere to the "house is used to live, not to fry" overall positioning, and put forward "explore new development model, adhere to rent and, accelerate the development of the rental market, promote the construction of affordable housing, support commercial housing market to better meet the reasonable housing demand, stable prices, stability, according to the city to promote virtuous cycle and healthy development", regulation policies, such as sales, purchase, price, etc. Now some cities new policy "centralized land supply" further promote the land auction market competition, resulting in land transaction price to reach a higher level. Therefore, the residential construction cost reduction and efficiency increase has become a breakthrough for real estate companies to seek profits. In the southeast coastal cities, the cost of residential basement accounts for more than 30% of the total construction cost. Therefore, this paper discusses two problems arising in the basement cost optimization process of the real estate development companies in the southeast coastal projects.展开更多
This paper proposes a new discrete-time Geo/G/1 queueing model under the control of bi-level randomized(p,N1,N2)-policy.That is,the server is closed down immediately when the system is empty.If N1(≥1)customers are ac...This paper proposes a new discrete-time Geo/G/1 queueing model under the control of bi-level randomized(p,N1,N2)-policy.That is,the server is closed down immediately when the system is empty.If N1(≥1)customers are accumulated in the queue,the server is activated for service with probability p(0≤p≤1)or still left off with probability(1−p).When the number of customers in the system becomes N_(2)(≥N1),the server begins serving the waiting customers until the system becomes empty again.For the model,firstly,we obtain the transient solution of the queue size distribution and the explicit recursive formulas of the stationary queue length distribution by employing the total probability decomposition technique.Then,the expressions of its probability generating function of the steady-state queue size and the expected steady-state queue size are presented.Additionally,numerical examples are conducted to discuss the effect of the system parameters on some performance indices.Furthermore,the steady-state distribution of queue length at epochs n−,n and outside observer’s observation epoch are explored,respectively.Finally,we establish a cost function to investigate the cost optimization problem under the constraint of the average waiting time.And the presented model provides a less expected cost as compared to the traditional N-policy.展开更多
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,howev...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.展开更多
The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is crit...The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target.This study established an electrolytic hydrogen development mechanism considering the generation mix an...Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target.This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen.Based on the incremental cost principle,we quantified the provincial and national clean hydrogen production cost performance levels in 2030.The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces,with a national average value of less than 2 USD·kg^(-1) at the 40-megaton hydrogen supply scale.Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg^(-1).However,performance is affected by the potential distribution of hydrogen demand.From the supply side,competitiveness of the mechanism is limited to clean hydrogen production,while from the demand side,it could help electrolytic hydrogen fulfil a more significant role.This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.展开更多
It is an important task for airlines to make a reasonable workscope for their engines, which has effects not only on engine performance and reliability, but also on airlines operating cost. Based on the recommendation...It is an important task for airlines to make a reasonable workscope for their engines, which has effects not only on engine performance and reliability, but also on airlines operating cost. Based on the recommendations given in the engine maintenance management manual, and taking the repair levels adopted in the previous shop visits into account, a series of module repair level optimization rules were set up, and a shop visit cost optimization model was also created for engine service life cycle. The particle swarm method was used to optimize the engine workscope and overhaul cost. The method proposed in this paper will be a reference for airlines to make engine workscope and to do engine maintenance management.展开更多
The economic management of colleges and universities has always been a topic of great concern to China’s educational career,therefore,this paper will firstly make the necessary analysis of the current implementation ...The economic management of colleges and universities has always been a topic of great concern to China’s educational career,therefore,this paper will firstly make the necessary analysis of the current implementation of the economic management of colleges and universities in China,and then the reasons for the problems of economic management of colleges and universities in China is realized a detailed investigation,and finally,the economic management of colleges and universities based on capital and cost management optimization strategy is made a full discussion,looking forward to providing the necessary guidance for researchers in this field.展开更多
Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational pro...Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.展开更多
The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a...The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.展开更多
In this work,we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks.The work ...In this work,we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks.The work planned has two stages i)Selection of suitable routing protocol(RP)for given IoT applications using higher cognitive process and ii)Deployment of the corresponding routing protocol.We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability,path delay,energy utilization,and throughput.The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedinglyflexible manner during the second stage.Extensive simulation has been done and results are evaluated to point out the strength of the proposed work,while dynamically varying the specific requirements of IoT applications.We observe that the work proposed is path-breaking the prevailing methods,where a specific routing protocol is employed throughout the period of time.It’s clearly shown that the proposed,Low-cost Context-Aware Protocol Switching(LCAPS)scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.展开更多
<div style="text-align:justify;"> <span style="font-family:Verdana;">Various open source software are managed by using several bug tracking systems. In particular, the open source softw...<div style="text-align:justify;"> <span style="font-family:Verdana;">Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.</span> </div>展开更多
This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital cos...This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.展开更多
Based on the delay-independent rule, the problem of optimal guaranteed cost control for a class of Takagi-Sugeno （T-S） fuzzy descriptor systems with time-varying delay is studied. A linear quadratic cost function is...Based on the delay-independent rule, the problem of optimal guaranteed cost control for a class of Takagi-Sugeno （T-S） fuzzy descriptor systems with time-varying delay is studied. A linear quadratic cost function is considered as the performance index of the closed-loop system. Sufficient conditions for the existence of guaranteed cost controllers via state feedback are given in terms of linear matrix inequalities （LMIs）, and the design of an optimal guaranteed cost controller can be reduced to a convex optimization problem. It is shown that the designed controller not only guarantees the asymptotic stability of the closed-loop fuzzy descriptor delay system, but also provides an optimized upper bound of the guaranteed cost. At last, a numerical example is given to illustrate the effectiveness of the proposed method and the perfect performance of the optimal guaranteed cost controller.展开更多
To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The con...To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.展开更多
This article is concerned with the modeling and control problems of the flexible spacecraft. First, the state observer is designed to estimate the vibration mode on the basis of free vibration models, Then, an optimal...This article is concerned with the modeling and control problems of the flexible spacecraft. First, the state observer is designed to estimate the vibration mode on the basis of free vibration models, Then, an optimal guaranteed cost controller is proposed to stabilize system attitude and damp the vibration of the flexible beam at the same time. Numerical simulation examples show the feasibility and validity of the proposed method.展开更多
基金supported by the Tunisian Ministry of Higher Education and Scientific Research under Grant LSE-ENIT-LR 11ES15funded in part by the PAQ-Collabora(PAR&I-Tk)program。
文摘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.
文摘Prestressed wire winded framework （PWWF） is an advanced structure and the most expensive part in the large-scale equip- ment. The traditional design of PWWF is complicated, highly iterative and cost uncontrolable, because PWWF is a variable stiffness multi-agent structure, with non-linear loading and deformation coordination. In this paper, cost optimization method of large-scale PWWF by multiple-island genetic algorithm （MIGA） is presented. Optimization design flow and optimization model are proposed based on variable-tension wire winding theory. An example of the PWWF cost optimization of isostatic equipment with axial load 6 000 kN is given. The optimization cost is reduced by 21.6% compared with traditional design. It has also been verified by the finite-element analysis and successfully applied to an actual PWWF design of isostatic press. The results show that this method is efficient and reliable. This method can also provide a guide for optimal design for ultra-large dimension muti-frame structure of 546 MN and 907 MN isostatic press equipment.
文摘This paper introduces a new study on cost optimization of surface grinding. In the study, the effects of grinding parameters including the dressing regime parameters, the wheel life and the initial grinding wheel diameter on the exchanged grinding wheel diameter which were investigated. In addition, the influence of cost parameters including the machine tool hourly rate and the grinding wheel cost were taken into account. In order to find the optimum exchanged grinding wheel diameter, a cost optimization problem was built. From the results of the optimization problem, a model for determination of the optimum exchanged grinding wheel diameter was found. By using the optimum diameter, both the grinding cost and grinding time can be reduced significantly.
文摘In recent times, renewable energy production from renewable energy sources is an alternative way to fulfill the increased energy demands. However, the increasing energy demand rate places more pressure, leading to the termination of conventional energy resources. However, the cost of power generation from coal-fired plants is higher than the power generation’s price from renewable energy sources. This experiment is focused on cost optimization during power generation through pumped storage power plant and wind power plant. The entire modeling of cost optimization has been conducted in two parts. The mathematical modeling was done using MATLAB simulation while the hydro and wind power plant’s emulation was performed using SCADA (Supervisory control and data acquisition) designer implementation. The experiment was conducted using ranges of generated power from both power sources. The optimum combination of output power and cost from both generators is determined via MATLAB simulation within the assumed generated output power range. Secondly, the hydro-generator and wind generator’s emulation were executed individually through synchronizing the grid to determine each generator’s specification using SCADA designer, which provided the optimum power generation from both generators with the specific speed, aligning with results generated through MATLAB. Finally, the operational power cost (with no losses consideration) from MATLAB was compared with the local energy provider to determine the cost-efficiency. This experiment has provided the operational cost optimization of the hydro-wind combined power system with stable wind power generation using SCADA, which will ultimately assist in operations of large-scale power systems, remotely minimizing multi-area dynamic issues while maximizing the system efficiency.
文摘Due to the state of the real estate industry, still adhere to the "house is used to live, not to fry" overall positioning, and put forward "explore new development model, adhere to rent and, accelerate the development of the rental market, promote the construction of affordable housing, support commercial housing market to better meet the reasonable housing demand, stable prices, stability, according to the city to promote virtuous cycle and healthy development", regulation policies, such as sales, purchase, price, etc. Now some cities new policy "centralized land supply" further promote the land auction market competition, resulting in land transaction price to reach a higher level. Therefore, the residential construction cost reduction and efficiency increase has become a breakthrough for real estate companies to seek profits. In the southeast coastal cities, the cost of residential basement accounts for more than 30% of the total construction cost. Therefore, this paper discusses two problems arising in the basement cost optimization process of the real estate development companies in the southeast coastal projects.
基金Supported by the National Natural Science Foundation of China(71571127)the Opening Fund of Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things(2023WZJ02)。
文摘This paper proposes a new discrete-time Geo/G/1 queueing model under the control of bi-level randomized(p,N1,N2)-policy.That is,the server is closed down immediately when the system is empty.If N1(≥1)customers are accumulated in the queue,the server is activated for service with probability p(0≤p≤1)or still left off with probability(1−p).When the number of customers in the system becomes N_(2)(≥N1),the server begins serving the waiting customers until the system becomes empty again.For the model,firstly,we obtain the transient solution of the queue size distribution and the explicit recursive formulas of the stationary queue length distribution by employing the total probability decomposition technique.Then,the expressions of its probability generating function of the steady-state queue size and the expected steady-state queue size are presented.Additionally,numerical examples are conducted to discuss the effect of the system parameters on some performance indices.Furthermore,the steady-state distribution of queue length at epochs n−,n and outside observer’s observation epoch are explored,respectively.Finally,we establish a cost function to investigate the cost optimization problem under the constraint of the average waiting time.And the presented model provides a less expected cost as compared to the traditional N-policy.
基金funded by the Ghent University Special Research Fund under grant 01N01219“Multi-objective societal optimization of structural fire safety investments for uncommon projects using advanced regression techniques”.
文摘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.
文摘The advent of microgrids in modern energy systems heralds a promising era of resilience,sustainability,and efficiency.Within the realm of grid-tied microgrids,the selection of an optimal optimization algorithm is critical for effective energy management,particularly in economic dispatching.This study compares the performance of Particle Swarm Optimization(PSO)and Genetic Algorithms(GA)in microgrid energy management systems,implemented using MATLAB tools.Through a comprehensive review of the literature and sim-ulations conducted in MATLAB,the study analyzes performance metrics,convergence speed,and the overall efficacy of GA and PSO,with a focus on economic dispatching tasks.Notably,a significant distinction emerges between the cost curves generated by the two algo-rithms for microgrid operation,with the PSO algorithm consistently resulting in lower costs due to its effective economic dispatching capabilities.Specifically,the utilization of the PSO approach could potentially lead to substantial savings on the power bill,amounting to approximately$15.30 in this evaluation.Thefindings provide insights into the strengths and limitations of each algorithm within the complex dynamics of grid-tied microgrids,thereby assisting stakeholders and researchers in arriving at informed decisions.This study contributes to the discourse on sustainable energy management by offering actionable guidance for the advancement of grid-tied micro-grid technologies through MATLAB-implemented optimization algorithms.
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
基金support provided by the National Science Fund for Distinguished Young Scholars(52325703)Postdoctoral Innovation Talents Support Program(BX20220066)+1 种基金China Postdoctoral Science Foundation(2022M720709)State Key Laboratory of Power System Operation and Control(SKLD23KM06).
文摘Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target.This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen.Based on the incremental cost principle,we quantified the provincial and national clean hydrogen production cost performance levels in 2030.The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces,with a national average value of less than 2 USD·kg^(-1) at the 40-megaton hydrogen supply scale.Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg^(-1).However,performance is affected by the potential distribution of hydrogen demand.From the supply side,competitiveness of the mechanism is limited to clean hydrogen production,while from the demand side,it could help electrolytic hydrogen fulfil a more significant role.This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.
文摘It is an important task for airlines to make a reasonable workscope for their engines, which has effects not only on engine performance and reliability, but also on airlines operating cost. Based on the recommendations given in the engine maintenance management manual, and taking the repair levels adopted in the previous shop visits into account, a series of module repair level optimization rules were set up, and a shop visit cost optimization model was also created for engine service life cycle. The particle swarm method was used to optimize the engine workscope and overhaul cost. The method proposed in this paper will be a reference for airlines to make engine workscope and to do engine maintenance management.
文摘The economic management of colleges and universities has always been a topic of great concern to China’s educational career,therefore,this paper will firstly make the necessary analysis of the current implementation of the economic management of colleges and universities in China,and then the reasons for the problems of economic management of colleges and universities in China is realized a detailed investigation,and finally,the economic management of colleges and universities based on capital and cost management optimization strategy is made a full discussion,looking forward to providing the necessary guidance for researchers in this field.
基金sponsored by the NWO/TTW project Multi-scale integrated Trafc Observatory for Large Road Networks(MiRRORS)under Grant Number 16270.
文摘Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.
文摘The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.
文摘In this work,we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks.The work planned has two stages i)Selection of suitable routing protocol(RP)for given IoT applications using higher cognitive process and ii)Deployment of the corresponding routing protocol.We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability,path delay,energy utilization,and throughput.The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedinglyflexible manner during the second stage.Extensive simulation has been done and results are evaluated to point out the strength of the proposed work,while dynamically varying the specific requirements of IoT applications.We observe that the work proposed is path-breaking the prevailing methods,where a specific routing protocol is employed throughout the period of time.It’s clearly shown that the proposed,Low-cost Context-Aware Protocol Switching(LCAPS)scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Various open source software are managed by using several bug tracking systems. In particular, the open source software extends to the cloud service and edge computing. Recently, OSF Edge Computing Group is launched by OpenStack. There are big data behind the internet services such as cloud and edge computing. Then, it is important to consider the impact of big data in order to assess the reliability of open source software. Various optimal software release problems have been proposed by specific researchers. In the typical optimal software release problems, the cost parameters are defined as the known parameter. However, it is difficult to decide the cost parameter because of the uncertainty. The purpose of our research is to estimate the effort parameters included in our models. In this paper, we propose an estimation method of effort parameter by using the genetic algorithm. Then, we show the estimation method in section 3. Moreover, we analyze actual data to show numerical examples for the estimation method of effort parameter. As the research results, we found that the OSS managers would be able to comprehend the human resources required before the OSS project in advance by using our method.</span> </div>
文摘This paper tries to integrate game theory, a very useful tool to resolve conflict phenomena, with optimal capital cost allocation issue in total emission control. First the necessity of allocating optimal capital costs fairly and reasonably among polluters in total emission control was analyzed. Then the possibility of applying game theory to the issue of the optimal capital cost allocation was expounded. Next the cooperative N person game model of the optimal capital cost allocation and its solution ways including method based on Shapley value, least core method, weak least core methods, proportional least core method, CGA method, MCRS method and so on were delineated. Finally through application of these methods it was concluded that to apply game theory in the optimal capital cost allocation issue is helpful to implement the total emission control planning schemes successfully, to control pollution effectively, and to ensure sustainable development.
基金the National Natural Science Foundation of China (60325311).
文摘Based on the delay-independent rule, the problem of optimal guaranteed cost control for a class of Takagi-Sugeno （T-S） fuzzy descriptor systems with time-varying delay is studied. A linear quadratic cost function is considered as the performance index of the closed-loop system. Sufficient conditions for the existence of guaranteed cost controllers via state feedback are given in terms of linear matrix inequalities （LMIs）, and the design of an optimal guaranteed cost controller can be reduced to a convex optimization problem. It is shown that the designed controller not only guarantees the asymptotic stability of the closed-loop fuzzy descriptor delay system, but also provides an optimized upper bound of the guaranteed cost. At last, a numerical example is given to illustrate the effectiveness of the proposed method and the perfect performance of the optimal guaranteed cost controller.
基金supported by the National Natural Science Foundation of China(61773220,61876192,61907021)the National Natural Science Foundation of Hubei(ZRMS2019000752)+2 种基金the Fundamental Research Funds for the Central Universities(2662018QD057,CZT20022,CZT20020)Academic Team in Universities(KTZ20051)School Talent Funds(YZZ19004)。
文摘To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.
基金the National Natural Science Foundation of China (60574022)
文摘This article is concerned with the modeling and control problems of the flexible spacecraft. First, the state observer is designed to estimate the vibration mode on the basis of free vibration models, Then, an optimal guaranteed cost controller is proposed to stabilize system attitude and damp the vibration of the flexible beam at the same time. Numerical simulation examples show the feasibility and validity of the proposed method.
