An improved genetic algorithm and its application to resolve cutting stock problem arc presented.It is common to apply simple genetic algorithm(SGA)to cutting stock problem,but the huge amount of computing of SGA is a...An improved genetic algorithm and its application to resolve cutting stock problem arc presented.It is common to apply simple genetic algorithm(SGA)to cutting stock problem,but the huge amount of computing of SGA is a serious problem in practical application.Accelerating genetic algorithm(AGA)based on integer coding and AGA's detailed steps are developed to reduce the amount of computation,and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem.SGA is adopted to produce individuals within given evolution process,and the variation interval of these individuals is taken as initial domain of the next optimization process,thus shrinks searching range intensively and accelerates the evaluation process of SGA.To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result,fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process.According to the computational experiment,it is observed that this improved GA converges much sooner than SGA,and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.展开更多
To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction.As one of the most importa...To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction.As one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction phase.Excessive cutting waste increases construction costs and generates a considerable amount of CO_(2)emission.This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction.In the proposed algorithm,the integer linear programming algorithm was applied to solve the problem.It was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation.The proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction costs.As the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation efficiency.When the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.展开更多
基金supported by National Natural Science Foundation of China(No.50575153)Provincial Key Technology Projects of Sichuan,China(No.03GG010-002)
文摘An improved genetic algorithm and its application to resolve cutting stock problem arc presented.It is common to apply simple genetic algorithm(SGA)to cutting stock problem,but the huge amount of computing of SGA is a serious problem in practical application.Accelerating genetic algorithm(AGA)based on integer coding and AGA's detailed steps are developed to reduce the amount of computation,and a new kind of rectangular parts blank layout algorithm is designed for rectangular cutting stock problem.SGA is adopted to produce individuals within given evolution process,and the variation interval of these individuals is taken as initial domain of the next optimization process,thus shrinks searching range intensively and accelerates the evaluation process of SGA.To enhance the diversity of population and to avoid the algorithm stagnates at local optimization result,fixed number of individuals are produced randomly and replace the same number of parents in every evaluation process.According to the computational experiment,it is observed that this improved GA converges much sooner than SGA,and is able to get the balance of good result and high efficiency in the process of optimization for rectangular cutting stock problem.
基金funded by Nature Science Foundation of China(51878556)the Key Scientific Research Projects of Shaanxi Provincial Department of Education(20JY049)+1 种基金Key Research and Development Program of Shaanxi Province(2019TD-014)State Key Laboratory of Rail Transit Engineering Informatization(FSDI)(SKLKZ21-03).
文摘To meet the requirements of specifications,intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction.As one of the most important building materials in construction engineering,reinforcing bars(rebar)account for more than 30%of the cost in civil engineering.A significant amount of cutting waste is generated during the construction phase.Excessive cutting waste increases construction costs and generates a considerable amount of CO_(2)emission.This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction.In the proposed algorithm,the integer linear programming algorithm was applied to solve the problem.It was combined with the statistical method,a greedy strategy was introduced,and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation.The proposed algorithm was verified through a case study;the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124%compared with that of traditional distributed processing of steel bars,reducing CO_(2)emissions and saving construction costs.As the scale of a project increases,the calculation quality of the optimization algorithmfor steel bar blanking proposed also increases,while maintaining high calculation efficiency.When the results of this study are applied in practice,they can be used as a sustainable foundation for building informatization and intelligent development.
