Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmi...In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator’s opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator;in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator’s opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator’s opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.展开更多
<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorith...<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>展开更多
With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to th...With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.展开更多
Production planning is the foremost task for manufacturing firms to deal with, especially adopting Flexible Manufacturing System (FMS) as the manufacturing strategy for production seeking an optimal balance between pr...Production planning is the foremost task for manufacturing firms to deal with, especially adopting Flexible Manufacturing System (FMS) as the manufacturing strategy for production seeking an optimal balance between productivity-flexibility requirements. Production planning in FMS provides a solution to problems regarding part type selection: machine grouping, production ratio, resource allocation and loading problem. These problems need to be solved optimally for maximum utilization of resources. Optimal solution to these problems has been a focus of attention in production and manufacturing, industrial and academic research since a number of decades. Evolution of new optimization techniques, software, technology, machines and computer languages provides the scope of a better optimal solution to the existing problems. Thus there remains a need of research to solve the problem with latest tools and techniques for higher optimal use of available resources. As an objective, the researchers need to reduce the computational time and cost, complexity of the problem, solution approach viz. general or customized, better user friendly communication with machine, higher freedom to select the desired objective(s) type(s) for optimal solution to the problem. As an approach to the solution to the problem, a researcher first needs to go for an exhaustive literature review, where the researcher needs to find the research gaps, compare and analyze the tools and techniques used, number of objectives considered for optimization and need, and scope of research for the research problem. The present study is a review paper analyzing the research gaps, approach and techniques used, scope of new optimization techniques or any other research, objectives considered and validation approaches for loading problems of production planning in FMS.展开更多
High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation ba...High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.展开更多
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
文摘In this paper, a multi-objective load shedding framework on the power system is presented. The frame work is useable in any kind of smart power systems;the word of smart here refers to the availability of data transmission infrastructure (like PLC or power line carrier) in the system, in order to carry the system data to the load shedding framework. This is an open framework that means it can optimize load shedding problem by considering unlimited number of objective functions, in other word, the number of objectives can be as much as the operator decides, finally in the end of frame work just one matrix breaker state is chosen in a way of having the most compatibility with the operator ideas which are determined by objectives importance percentage which are one input groups of the framework. A two-stage methodology is used for the optimal load shedding problem. In the first stage, Discrete Multi-objective Particle Swarm Optimization method is used to find a collection of the best states of load shedding (Pareto front). In the second stage, the fuzzy logic is used as a Pareto front inference engine. Fuzzy selection algorithm (FSA) is designed in a way that it can infer according to the operator’s opinion without the expert interference that means rule base is formed automatically by fuzzy algorithm. FSA is consisted of two parts. Membership functions and rules base are formed automatically in the first part, the former in accordance with the costs of Pareto front particles and the latter in correspondence with importance percentage of objectives which are entered to FSA by operator;in other word, decision matrix is formed automatically in the algorithm according to the cost of Pareto front particles and importance percentage of objectives. In the Second part, Mamdani inference engine scrutinizes the Pareto front particles by the use of formed membership functions and rules base to know if they are compatible to operator’s opinion or not. Getting this approach, cost functions of each particle are considered as the inputs of (FSA), then a fuzzy combined fitness (FCF) is allocated to each Pareto front particle by Mamdani inference engine. In other word, FCF shows how much the particle is compatible to the operator’s opinion. Finding minimum FCF, final inference is done. The proposed method is tested on 30-bus, and 118-bus IEEE systems by considering two or three objective functions and the results are presented.
文摘<div style="text-align:justify;"> In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads. </div>
文摘With the rapid and large-scale development of renewable energy, the lack of new energy power transportation or consumption, and the shortage of grid peak-shifting ability have become increasingly serious. Aiming to the severe wind power curtailment issue, the characteristics of interactive load are studied upon the traditional day-ahead dispatch model to mitigate the influence of wind power fluctuation. A multi-objective optimal dispatch model with the minimum operating cost and power losses is built. Optimal power flow distribution is available when both generation and demand side participate in the resource allocation. The quantum particle swarm optimization (QPSO) algorithm is applied to convert multi-objective optimization problem into single objective optimization problem. The simulation results of IEEE 30-bus system verify that the proposed method can effectively reduce the operating cost and grid loss simultaneously enhancing the consumption of wind power.
文摘Production planning is the foremost task for manufacturing firms to deal with, especially adopting Flexible Manufacturing System (FMS) as the manufacturing strategy for production seeking an optimal balance between productivity-flexibility requirements. Production planning in FMS provides a solution to problems regarding part type selection: machine grouping, production ratio, resource allocation and loading problem. These problems need to be solved optimally for maximum utilization of resources. Optimal solution to these problems has been a focus of attention in production and manufacturing, industrial and academic research since a number of decades. Evolution of new optimization techniques, software, technology, machines and computer languages provides the scope of a better optimal solution to the existing problems. Thus there remains a need of research to solve the problem with latest tools and techniques for higher optimal use of available resources. As an objective, the researchers need to reduce the computational time and cost, complexity of the problem, solution approach viz. general or customized, better user friendly communication with machine, higher freedom to select the desired objective(s) type(s) for optimal solution to the problem. As an approach to the solution to the problem, a researcher first needs to go for an exhaustive literature review, where the researcher needs to find the research gaps, compare and analyze the tools and techniques used, number of objectives considered for optimization and need, and scope of research for the research problem. The present study is a review paper analyzing the research gaps, approach and techniques used, scope of new optimization techniques or any other research, objectives considered and validation approaches for loading problems of production planning in FMS.
基金supported by National Science and Technology Support Program of China (Grant No. 2012BAF15G00)
文摘High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.
