Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de...Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multio...Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form.展开更多
To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail...To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail transit scheduling method based on a flexible train formation strat-egy(FTFS).By constructing interaction parameters that characterize the coupling effects of high passenger flow across multiple hubs,a multiobjective optimization model is developed to minimize passenger waiting time at hub sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity adjustment and efficient rolling stock circulation.A case study on Nanjing Metro Line 1 demonstrates that the FTFS reduces the average waiting time at hub stations by 47.2%,alleviates train congestion by approximately 18.6%,and re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.展开更多
Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli...Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.展开更多
Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufact...Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.展开更多
The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.F...The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.展开更多
This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical ...This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks.A trilevel,two-stage,and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network.In the model,a new metric was designed to evaluate the performance of a road network;resilience was considered from robustness and recovery efficiency of a road network.The proposed model is at least a nondeterministic polynomialtime hardness(NP-hard)problem,which is unlikely to be solved by a polynomial time algorithm.Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm.The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden.Roadside tree retrofit of a provincial highway network on Hainan Island,China was selected as a case area because it suffers severely from tropical cyclones every year,where there is an urgency to upgrade roadside trees against tropical cyclones.We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown,at the same time that it promotes robustness and expected recovery efficiency of the road network.展开更多
Carbon nanotube field-effect transistors(CNTFETs) are reliable alternatives for conventional transistors, especially for use in approximate computing(AC) based error-resilient digital circuits. In this paper, CNTFET t...Carbon nanotube field-effect transistors(CNTFETs) are reliable alternatives for conventional transistors, especially for use in approximate computing(AC) based error-resilient digital circuits. In this paper, CNTFET technology and the gate diffusion input(GDI) technique are merged, and three new AC-based full adders(FAs) are presented with 6, 6, and 8 transistors, separately. The nondominated sorting based genetic algorithm II(NSGA-II) is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits' improvement by about 50% in terms of power-delay-product(PDP) at the cost of area occupation. The Monte Carlo method(MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold(DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit(LSB) parts of more complex arithmetic circuits such as multipliers.展开更多
基金This work was supported by the China Scholarship Council Scholarship,the National Key Research and Development Program of China(2017YFB0306400)the National Natural Science Foundation of China(62073069)the Deanship of Scientific Research(DSR)at King Abdulaziz University(RG-48-135-40).
文摘Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
基金the National Natural Science Foundation of China(Nos.51779135 and 51009087)the Natural Science Foundation of Shanghai(No.14ZR1419500)。
文摘Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form.
基金Key Project of the National Natural Science Foundation of China (No. 52432011)the National Natural Science Foundation of China (No. 524B2153)。
文摘To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail transit scheduling method based on a flexible train formation strat-egy(FTFS).By constructing interaction parameters that characterize the coupling effects of high passenger flow across multiple hubs,a multiobjective optimization model is developed to minimize passenger waiting time at hub sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity adjustment and efficient rolling stock circulation.A case study on Nanjing Metro Line 1 demonstrates that the FTFS reduces the average waiting time at hub stations by 47.2%,alleviates train congestion by approximately 18.6%,and re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2022RC4028in part by the National Natural Science Foundation of China under Grant 62473204+3 种基金in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grants NY221082,NY222144,and NY223075in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunicationsin part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX24_1215.
文摘Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.
基金supported by the National Natural Science Foundation of China(No.62076095)National Key Research and Development Program(No.2022YFB4602104).
文摘Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and flexibility.This paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion time.To address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is proposed.In NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution process.The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations.In addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is adopted.Multiple local search operators based on ideal point are proposed to find a better local solution.To improve the global exploration ability of the algorithm,a dual population restart mechanism is adopted.Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.
基金National Natural Science Foundation of China,Grant/Award Number:52075388。
文摘The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.
基金partially supported by the National Key Research and Development Program of China(2016YFA0602403)the National Natural Science Foundation of China(41621061)the International Center for Collaborative Research on Disaster Risk Reduction(ICCRDRR)
文摘This article focuses on decision making for retrofit investment of road networks in order to alleviate severe consequences of roadside tree blowdown during tropical cyclones.The consequences include both the physical damage associated with roadside trees and the functional degradation associated with road networks.A trilevel,two-stage,and multiobjective stochastic mathematical model was developed to dispatch limited resources to retrofit the roadside trees of a road network.In the model,a new metric was designed to evaluate the performance of a road network;resilience was considered from robustness and recovery efficiency of a road network.The proposed model is at least a nondeterministic polynomialtime hardness(NP-hard)problem,which is unlikely to be solved by a polynomial time algorithm.Pareto-optimal solutions for this problem can be obtained by a proposed trilevel algorithm.The random forest method was employed to transform the trilevel algorithm into a singlelevel algorithm in order to decrease the computation burden.Roadside tree retrofit of a provincial highway network on Hainan Island,China was selected as a case area because it suffers severely from tropical cyclones every year,where there is an urgency to upgrade roadside trees against tropical cyclones.We found that roadside tree retrofit investment significantly alleviates the expected economic losses of roadside tree blowdown,at the same time that it promotes robustness and expected recovery efficiency of the road network.
文摘Carbon nanotube field-effect transistors(CNTFETs) are reliable alternatives for conventional transistors, especially for use in approximate computing(AC) based error-resilient digital circuits. In this paper, CNTFET technology and the gate diffusion input(GDI) technique are merged, and three new AC-based full adders(FAs) are presented with 6, 6, and 8 transistors, separately. The nondominated sorting based genetic algorithm II(NSGA-II) is used to attain the optimal performance of the proposed cells by considering the number of tubes and chirality vectors as its variables. The results confirm the circuits' improvement by about 50% in terms of power-delay-product(PDP) at the cost of area occupation. The Monte Carlo method(MCM) and 32-nm CNTFET technology are used to evaluate the lithographic variations and the stability of the proposed circuits during the fabrication process, in which the higher stability of the proposed circuits compared to those in the literature is observed. The dynamic threshold(DT) technique in the transistors of the proposed circuits amends the possible voltage drop at the outputs. Circuitry performance and error metrics of the proposed circuits nominate them for the least significant bit(LSB) parts of more complex arithmetic circuits such as multipliers.
