Effective forest regeneration is essential for sustainable forestry practices.In Sweden,mechanical site preparation and manual planting is the dominating method,but sourcing labour for the physically demanding work is...Effective forest regeneration is essential for sustainable forestry practices.In Sweden,mechanical site preparation and manual planting is the dominating method,but sourcing labour for the physically demanding work is difficult.An autonomous scarifying and planting system(Autoplant)could meet the requirements of the forest industry and,for this,a tool for regeneration planning and routing is needed.The tool,Pathfinder,plans the regeneration and routes based on the harvested production(hpr)files,soil moisture and parent material maps,no-go areas(for culture or nature conservation),digital elevation models(DEM),and machine data(e.g.,working width,critical slope,time taken for different turn angles).The overall planting solution is either a set of capacity constrained routes or a continuous route and could be used for any planting machine as well as for traditional scarifiers as disc trenchers or mounders pulled by forwarders.Pathfinder was tested on eleven regeneration areas throughout Sweden,both with continuous routes and routes based on a carrying capacity of 1500 seedlings.The net operation area,species and seedling density suggestions were deemed relevant by expert judgement in the field.The routes provided by Pathfinder were compared with solutions given by two experienced drivers and a third solution based on the actual soil scarification at the site.Total driving distance did not differ significantly between the suggestions,but Pathfinder included less side-slope driving on steep slopes(≥27%or 15°)and medium slopes(15–27%).The chosen threshold value for steep slopes(where side-slope driving should be avoided)affects the routing,and a lower threshold means more turning and longer driving distance.Pathfinder is not only a tool for routing of planting machines,but also helps in planning of traditional regeneration by providing a more correct net area and tree species suggestions based on the growth of the previous stand.It also diminishes the risk of severe soil disturbance by excluding the wettest area in the planning.展开更多
BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability...BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.展开更多
Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and pea...Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.展开更多
The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the...The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.展开更多
The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car...The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.展开更多
Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi hav...Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour （CBT） service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application （app） and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest （PoI） and pickup/dropout points are identified with arcs of the network （instead of nodes）, each of which can be visited at least once （instead of exactly once）. Secondly, the resulting pure integer programming （IP） problem is solved using a leading optimization soft- ware known as General Algebraic Modeling System （GAMS）. The GAMS code developed for this project returns： （1） the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; （2） the augmenting paths corre- sponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne （Australia）. All the computations relative to the initial tests show that the ability of the app to answer users＇ inquiries in a fraction of a minute.展开更多
Manufacturing system, with high level of complexity and with a mix of semi-repetitive and repetitive products, to become productive, should seek the standardization of products and processes to obtain the optimization...Manufacturing system, with high level of complexity and with a mix of semi-repetitive and repetitive products, to become productive, should seek the standardization of products and processes to obtain the optimization of use of production resources. However, it is necessary to measure the productivity, so that the system of measurement and control of manufacturing processes are an element critical as to ensure greater visibility of the flow＇s restrictions, minimized when detected properly. In this case, the automation of factory＇s measurement process can effectively contribute to ensuring the effectiveness of the function control of a manufacturing system. It is important to consider that the automation of the system of measurement and control of manufacturing processes, of complex environment, is heavily dependent of IT tools applied directly in the interface computational between the operation systems and the corporate systems. This heavy reliance, if exploited technically properly, allows that automation of the system of measurement and control of production makes the access to time real of availability of manufacturing process＇s data, such as processing time and setup time that it can export to a specialist software in programming production, for example, feasible. In this paper, the automation of the system of measurement and control of production is approached, in order to identify the main possibilities of the design of an information system capable to integrate the flow of information in an environment internal on manufacturing organizations, with emphasis in the digital manufacturing paradigm.展开更多
Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of...Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.展开更多
Operating Theatre is the centre of the hospital management＇s efforts. It constitutes the most expensive sector with more than 10% of the intended operating budget of the hospital. To reduce the costs while maintainin...Operating Theatre is the centre of the hospital management＇s efforts. It constitutes the most expensive sector with more than 10% of the intended operating budget of the hospital. To reduce the costs while maintaining a good quality of care, one of the solutions is to improve the existent planning and scheduling methods by improving the services and surgical specialty coordination or finding the best estimation of surgical case durations. The other solution is to construct an effective surgical case plan and schedule. The operating theatre planning and scheduling is the two important steps, which aim to make a surgical case programming with an objective of obtaining a realizable and efficient surgical case schedule. This paper focuses on the first step, the operating theatre planning problem. Two planning methods are introduced and compared. Real data of a Belgian university hospital ＂Tivoli＂ are used for the experiments.展开更多
In this paper, the classical economic order quantity （EOQ） inventory model assumption that all items of a certain product received from a supplier are of perfect quality is relaxed. Another basic assumption that the...In this paper, the classical economic order quantity （EOQ） inventory model assumption that all items of a certain product received from a supplier are of perfect quality is relaxed. Another basic assumption that the payment for the items is made at the beginning of the inventory cycle when they are received is also eased. We consider an inventory situation where items received from the supplier are of two types of quality, perfect and imperfect, and a short deferral in payment is allowed. The split between perfect and imperfect quality items is assumed to follow a known probability distribution. Both qualities of items have continuous demands, and items of imperfect quality are sold at a discount. A mathematical model is developed using the net present value of all cash flows involved in the inventory cycle. A numerical method for obtaining the optimal order quantity is presented, and the impact of the short-term financing is analyzed. An example is presented to validate the equations and illustrate the results.展开更多
The optimal planning and operation of multi-type flexible resources(FRs)are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy.However,insuff...The optimal planning and operation of multi-type flexible resources(FRs)are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy.However,insufficient consideration of the multi-dimensional and heterogeneous features of FRs,such as the regulation characteristics of diversified battery energy storage systems(BESSs),poses a challenge in economically relieving imbalance power and adequately sharing feature information between power supply and demand.In view of this disadvantage,an optimal planning and operation method based on differentiated feature matching through response capability characterization and difference quantification of FRs is proposed in this paper.In the planning stage,a model for the optimal planning of diversified energy storages(ESs)including Lithium-ion battery(Li-B),supercapacitor energy storage(SCES),compressed air energy storage(CAES),and pumped hydroelectric storage(PHS)is established.Subsequently,in the operating stage,the potential,direction,and cost of FR response behaviors are refined to match with the power and energy balance demand(PEBD)of power grid operation.An optimal operating algorithm is then employed to quantify the feature differences and output response sequences of multi-type FRs.The performance and effectiveness of the proposed method are demonstrated through comparative studies conducted on an actual regional power grid in northwest China.Analysis and simulation results illustrate that the proposed method can effectively highlight the advantages of BESSs compared with other ESs,and economically reduce imbalance power of the regional power grid under practical operating conditions.展开更多
The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy ...The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.展开更多
We extract a mathematical model to simulate the steady-state charging and discharging behaviors of an electrochemical storage over a 24-hour time interval.Moreover,we develop a model for optimizing the daily operation...We extract a mathematical model to simulate the steady-state charging and discharging behaviors of an electrochemical storage over a 24-hour time interval.Moreover,we develop a model for optimizing the daily operational planning of an interconnected micro grid considering electrochemical storage.The optimization model is formulated to maximize the total benefit of the micro grid via selling power to its end consumers and also exchanging power with the wholesale energy market so that the constraints of distributed energy resources(DERs) and low-voltage grid are met.The optimization problem is solved by a genetic algorithm,and applied on two micro grids operating under different scenarios containing the absence or presence of electrochemical storages.Comparison of the results of the optimization model for this micro grid,with and without electrochemical storage,shows that the electrochemical storage can improve the economical efficiency of the interconnected micro grids by up to 10.16%.展开更多
Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design sp...Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment.While exact mathematical optimisa-tion approaches to the MG capacity planning are highly computationally efficient,they often fail to preserve the associated problem nonlinearities and non-convexities.This translates into the fact that the available MG sizing tools potentially return a sub-optimal(inferior)MG design.This brings to light the importance of nature-inspired,swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem–and better approximate the globally optimum solution–though at the expense of increased computational complexity.Accordingly,this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic,namely the Lévy-flight moth-flame optimisation algorithm(MFOA).An intelligent linear programming-based day-ahead en-ergy scheduling design is,additionally,integrated into the proposed model.A case study is presented for a real grid-tied community MG in rural New Zealand.A comparison of the modelling results with those of the most popular tool in the literature and industry,HOMER Pro,verifies the superiority of the proposed meta-heuristic-based MG sizing model.Additionally,the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature.The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics.Notably,its superiority to the original MFOA,the hybrid genetic algorithm-particle swarm optimisation,and the ant colony optimiser,by at least~6.5%,~8.4%,and~12.8%,is demonstrated.Moreover,comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.展开更多
Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of excee...Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction,a coordinated optimization method is presented in this paper.Firstly,a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix.Secondly,the impact of network structure changes on the multi-infeed DC system is derived.Then the coordinated optimization model is established,which considers the cost and effect of current limiting measures,the tightness of network structure and the voltage support capability of AC system to multiple DCs.Finally,the non-dominated sorting genetic algorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes.Case studies on a planning power system demonstrated the feasibility and speediness of this method.展开更多
Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superi...Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.展开更多
Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment fr...Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.展开更多
Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental ...Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.展开更多
基金funded by Vinnova,the Swedish Innovation Agency as a part of the Autoplant project(Dnr 2020-04202 and 2023-02747).
文摘Effective forest regeneration is essential for sustainable forestry practices.In Sweden,mechanical site preparation and manual planting is the dominating method,but sourcing labour for the physically demanding work is difficult.An autonomous scarifying and planting system(Autoplant)could meet the requirements of the forest industry and,for this,a tool for regeneration planning and routing is needed.The tool,Pathfinder,plans the regeneration and routes based on the harvested production(hpr)files,soil moisture and parent material maps,no-go areas(for culture or nature conservation),digital elevation models(DEM),and machine data(e.g.,working width,critical slope,time taken for different turn angles).The overall planting solution is either a set of capacity constrained routes or a continuous route and could be used for any planting machine as well as for traditional scarifiers as disc trenchers or mounders pulled by forwarders.Pathfinder was tested on eleven regeneration areas throughout Sweden,both with continuous routes and routes based on a carrying capacity of 1500 seedlings.The net operation area,species and seedling density suggestions were deemed relevant by expert judgement in the field.The routes provided by Pathfinder were compared with solutions given by two experienced drivers and a third solution based on the actual soil scarification at the site.Total driving distance did not differ significantly between the suggestions,but Pathfinder included less side-slope driving on steep slopes(≥27%or 15°)and medium slopes(15–27%).The chosen threshold value for steep slopes(where side-slope driving should be avoided)affects the routing,and a lower threshold means more turning and longer driving distance.Pathfinder is not only a tool for routing of planting machines,but also helps in planning of traditional regeneration by providing a more correct net area and tree species suggestions based on the growth of the previous stand.It also diminishes the risk of severe soil disturbance by excluding the wettest area in the planning.
文摘BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.
基金supported by the US Appalachian Regional Commission(ARC)under Grant MU-21579-23。
文摘Grid-scale energy storage systems provide effective solutions to address challenges such as supply-load imbalances and voltage violations resulting from the non-coinciding nature of renewable energy generation and peak demand incidents.While battery and hydrogen storage are commonly used for peak shaving,ice-based thermal energy storage systems(TESSs)offer a direct way to reduce cooling loads without electrical conversion.This paper presents a multi-objective planning framework that optimizes TESS dispatch,network topology,and photovoltaic(PV)inverter reactive power support to address operational issues in active distribution networks.The objectives of the proposed scheme include minimizing peak demand,voltage deviations,and PV inverter VAr dependency.The mixed-integer nonlinear programming problem is solved using a Pareto-based multi-objective particle swarm optimization(MOPSO)method.The MATLAB-OpenDSS simulations for a modified IEEE-123 bus system show a 7.1%reduction in peak demand,a 13%reduction in voltage deviation,and a 52%drop in PV inverter VAr usage.The obtained solutions confirm minimal operational stress on control devices such as switches and PV inverters.Thus,unlike earlier studies,this work combines all three strategies to offer an effective solution for the operational planning of the active distribution network.
基金supported by the National Natural Science Foundation of China(with Granted Number 72271239,grant recipient P.J.)Research on the Design Method of Reliability Qualification Test for Complex Equipment Based on Multi-Source Information Fusion.https://www.nsfc.gov.cn/.
文摘The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.
基金supported by the Scientific&Technical Project of the State Grid(5700--202490228A--1--1-ZN).
文摘The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%.
文摘Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour （CBT） service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application （app） and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest （PoI） and pickup/dropout points are identified with arcs of the network （instead of nodes）, each of which can be visited at least once （instead of exactly once）. Secondly, the resulting pure integer programming （IP） problem is solved using a leading optimization soft- ware known as General Algebraic Modeling System （GAMS）. The GAMS code developed for this project returns： （1） the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; （2） the augmenting paths corre- sponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne （Australia）. All the computations relative to the initial tests show that the ability of the app to answer users＇ inquiries in a fraction of a minute.
文摘Manufacturing system, with high level of complexity and with a mix of semi-repetitive and repetitive products, to become productive, should seek the standardization of products and processes to obtain the optimization of use of production resources. However, it is necessary to measure the productivity, so that the system of measurement and control of manufacturing processes are an element critical as to ensure greater visibility of the flow＇s restrictions, minimized when detected properly. In this case, the automation of factory＇s measurement process can effectively contribute to ensuring the effectiveness of the function control of a manufacturing system. It is important to consider that the automation of the system of measurement and control of manufacturing processes, of complex environment, is heavily dependent of IT tools applied directly in the interface computational between the operation systems and the corporate systems. This heavy reliance, if exploited technically properly, allows that automation of the system of measurement and control of production makes the access to time real of availability of manufacturing process＇s data, such as processing time and setup time that it can export to a specialist software in programming production, for example, feasible. In this paper, the automation of the system of measurement and control of production is approached, in order to identify the main possibilities of the design of an information system capable to integrate the flow of information in an environment internal on manufacturing organizations, with emphasis in the digital manufacturing paradigm.
基金funded by the National Natural Science Foundation of China(71701216,71171200).
文摘Purpose–Under the constraints of given passenger service level and coupling travel demand with train departure time,this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.Design/methodology/approach–The authors optimize the train operational plan in a special network layout,i.e.an urban rail corridor with dead-end terminal yard,by decomposing it into two sub-problems:train timetable optimization and rolling stock circulation optimization.As for train timetable optimization,the authors propose a schedule-based passenger flow assignment method,construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm.For the optimization of rolling stock circulation,the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.Findings–The case study shows that the train operational plan developed by the study’s approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.Originality/value–The example verifies the efficiency of the model and algorithm.
基金part of thoughts of the HRP2(Hospitals:Grouping,Sharing and Piloting)project which involves French laboratories hospitals which was sponsored by the Region Rhne-Alpes.It has been realized in the framework of a research project fulfilled in the Belgian laboratory MAAD(Applied Mathematics Decision-making Aid)with the cooperation of a Belgian Hospital"Tivoli".The original version was presented on ICSSSM’06.
文摘Operating Theatre is the centre of the hospital management＇s efforts. It constitutes the most expensive sector with more than 10% of the intended operating budget of the hospital. To reduce the costs while maintaining a good quality of care, one of the solutions is to improve the existent planning and scheduling methods by improving the services and surgical specialty coordination or finding the best estimation of surgical case durations. The other solution is to construct an effective surgical case plan and schedule. The operating theatre planning and scheduling is the two important steps, which aim to make a surgical case programming with an objective of obtaining a realizable and efficient surgical case schedule. This paper focuses on the first step, the operating theatre planning problem. Two planning methods are introduced and compared. Real data of a Belgian university hospital ＂Tivoli＂ are used for the experiments.
文摘In this paper, the classical economic order quantity （EOQ） inventory model assumption that all items of a certain product received from a supplier are of perfect quality is relaxed. Another basic assumption that the payment for the items is made at the beginning of the inventory cycle when they are received is also eased. We consider an inventory situation where items received from the supplier are of two types of quality, perfect and imperfect, and a short deferral in payment is allowed. The split between perfect and imperfect quality items is assumed to follow a known probability distribution. Both qualities of items have continuous demands, and items of imperfect quality are sold at a discount. A mathematical model is developed using the net present value of all cash flows involved in the inventory cycle. A numerical method for obtaining the optimal order quantity is presented, and the impact of the short-term financing is analyzed. An example is presented to validate the equations and illustrate the results.
基金This work was supported by the Science and Technology Major Project of Tibetan Autonomous Region of China(No.XZ202201ZD0003G).
文摘The optimal planning and operation of multi-type flexible resources(FRs)are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy.However,insufficient consideration of the multi-dimensional and heterogeneous features of FRs,such as the regulation characteristics of diversified battery energy storage systems(BESSs),poses a challenge in economically relieving imbalance power and adequately sharing feature information between power supply and demand.In view of this disadvantage,an optimal planning and operation method based on differentiated feature matching through response capability characterization and difference quantification of FRs is proposed in this paper.In the planning stage,a model for the optimal planning of diversified energy storages(ESs)including Lithium-ion battery(Li-B),supercapacitor energy storage(SCES),compressed air energy storage(CAES),and pumped hydroelectric storage(PHS)is established.Subsequently,in the operating stage,the potential,direction,and cost of FR response behaviors are refined to match with the power and energy balance demand(PEBD)of power grid operation.An optimal operating algorithm is then employed to quantify the feature differences and output response sequences of multi-type FRs.The performance and effectiveness of the proposed method are demonstrated through comparative studies conducted on an actual regional power grid in northwest China.Analysis and simulation results illustrate that the proposed method can effectively highlight the advantages of BESSs compared with other ESs,and economically reduce imbalance power of the regional power grid under practical operating conditions.
基金supported by the Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET)the Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT)
文摘The growing installation of natural gas fired power plants has increased the integration of natural gas and electricity sectors. This has driven the need investigate the interactions among them and to optimize energy resources management from a centralized planning perspective. Thus, a combined modeling of the reservoirs involved in electric power and gas systems and their locations on both networks are essential features to be considered in the operational planning of energy resources.This paper presents a modeling and optimization approach to the operational planning of electric power and natural gas systems, taking into account different energy storage facilities, such as water reservoirs, natural gas storages and line packs of pipelines. The proposed model takes advantage of captures both energy systems synergy and their associated networks. This approach identifies the interactions between the energy storage facilities and their economic impact over their optimal scheduling. The results show the benefits of an integrated operational planning of electric power and natural gas systems, the close interdependency between the energy resources stored in both systems, and the effects of a combined scheduling.
文摘We extract a mathematical model to simulate the steady-state charging and discharging behaviors of an electrochemical storage over a 24-hour time interval.Moreover,we develop a model for optimizing the daily operational planning of an interconnected micro grid considering electrochemical storage.The optimization model is formulated to maximize the total benefit of the micro grid via selling power to its end consumers and also exchanging power with the wholesale energy market so that the constraints of distributed energy resources(DERs) and low-voltage grid are met.The optimization problem is solved by a genetic algorithm,and applied on two micro grids operating under different scenarios containing the absence or presence of electrochemical storages.Comparison of the results of the optimization model for this micro grid,with and without electrochemical storage,shows that the electrochemical storage can improve the economical efficiency of the interconnected micro grids by up to 10.16%.
文摘Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment.While exact mathematical optimisa-tion approaches to the MG capacity planning are highly computationally efficient,they often fail to preserve the associated problem nonlinearities and non-convexities.This translates into the fact that the available MG sizing tools potentially return a sub-optimal(inferior)MG design.This brings to light the importance of nature-inspired,swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem–and better approximate the globally optimum solution–though at the expense of increased computational complexity.Accordingly,this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic,namely the Lévy-flight moth-flame optimisation algorithm(MFOA).An intelligent linear programming-based day-ahead en-ergy scheduling design is,additionally,integrated into the proposed model.A case study is presented for a real grid-tied community MG in rural New Zealand.A comparison of the modelling results with those of the most popular tool in the literature and industry,HOMER Pro,verifies the superiority of the proposed meta-heuristic-based MG sizing model.Additionally,the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature.The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics.Notably,its superiority to the original MFOA,the hybrid genetic algorithm-particle swarm optimisation,and the ant colony optimiser,by at least~6.5%,~8.4%,and~12.8%,is demonstrated.Moreover,comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.
基金This work was supported by State Grid Corporation of China,Major Projects on Planning and Operation Control of Large Scale Grid under Grant SGCC-MPLG020-2012.
文摘Due to increased penetration of renewable energies,DC links and other emerging technologies,power system operation and planning have to cope with various uncertainties and risks.In order to solve the problems of exceeding short circuit current and multi-infeed DC interaction,a coordinated optimization method is presented in this paper.Firstly,a branch selection strategy is proposed by analyzing the sensitivity relationship between current limiting measures and the impedance matrix.Secondly,the impact of network structure changes on the multi-infeed DC system is derived.Then the coordinated optimization model is established,which considers the cost and effect of current limiting measures,the tightness of network structure and the voltage support capability of AC system to multiple DCs.Finally,the non-dominated sorting genetic algorithm II combining with the branch selection strategy,is used to find the Pareto optimal schemes.Case studies on a planning power system demonstrated the feasibility and speediness of this method.
文摘Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.
基金The authors would like to thank Dr.-Ing.Nils Bornhorst for the fruitful discussion.The publication and development of this work was funded by the Hessian Ministry of Higher Education,Research,Science and the Arts,Germany through the K-ES project under reference number:511/17.001.
文摘Solving AC-Optimal Power Flow(OPF)problems is an essential task for grid operators to keep the power system safe for the use cases such as minimization of total generation cost or minimization of infeed curtailment from renewable DERs(Distributed Energy Resource).Mathematical solvers are often able to solve the AC-OPF problem but need significant computation time.Artificial neural networks(ANN)have a good application in function approximation with outstanding computational performance.In this paper,we employ ANN to approximate the solution of AC-OPF for multiple purposes.The novelty of our work is a new training method based on the reinforcement learning concept.A high-performance batched power flow solver is used as the physical environment for training,which evaluates an augmented loss function and the numerical action gradient.The augmented loss function consists of the objective term for each use case and the penalty term for constraints violation.This training method enables training without a reference OPF and the integration of discrete decision variable such as discrete transformer tap changer position in the constrained optimization.To improve the optimality of the approximation,we further combine the reinforcement training approach with supervised training labeled by reference OPF.Various benchmark results show the high approximation quality of our proposed approach while achieving high computational efficiency on multiple use cases.
基金The authors wish to acknowledge financial support provided by the Special Account for Research Funds of the Technological Education Institute of Central Macedonia,Greece,under grant SMF/LG/060219–23/3/19.
文摘Food consumption is constantly increasing at global scale.In this light,agricultural production also needs to increase in order to satisfy the relevant demand for agricultural products.However,due to by environmental and biological factors(e.g.soil compaction)the weight and size of the machinery cannot be further physically optimized.Thus,only marginal improvements are possible to increase equipment effectiveness.On the contrary,late technological advances in ICT provide the ground for significant improvements in agriproduction efficiency.In this work,the V-Agrifleet tool is presented and demonstrated.VAgrifleet is developed to provide a “hands-free”interface for information exchange and an “Olympic view”to all coordinated users,giving them the ability for decentralized decision-making.The proposed tool can be used by the end-users(e.g.farmers,contractors,farm associations,agri-products storage and processing facilities,etc.)order to optimize task and time management.The visualized documentation of the fleet performance provides valuable information for the evaluation management level giving the opportunity for improvements in the planning of next operations.Its vendorindependent architecture,voice-driven interaction,context awareness functionalities and operation planning support constitute V-Agrifleet application a highly innovative agricultural machinery operational aiding system.
