This research develops two new models for project portfolio selection, in which the candidate projects are composed of multiple repetitive units. To reflect some real situations, the learning effect is considered in t...This research develops two new models for project portfolio selection, in which the candidate projects are composed of multiple repetitive units. To reflect some real situations, the learning effect is considered in the project portfolio selection problem for the first time. The mathematical representations of the relationship between learning experience and investment cost are provided. One numerical example under different scenarios is demonstrated and the impact of considering learning effect is then discussed.展开更多
The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;i...The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.展开更多
Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision make...Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.展开更多
Optimization problem of cardinality constrained mean-variance(CCMV)model for sparse portfolio selection is considered.To overcome the difficulties caused by cardinality constraint,an exact penalty approach is employed...Optimization problem of cardinality constrained mean-variance(CCMV)model for sparse portfolio selection is considered.To overcome the difficulties caused by cardinality constraint,an exact penalty approach is employed,then CCMV problem is transferred into a difference-of-convex-functions(DC)problem.By exploiting the DC structure of the gained problem and the superlinear convergence of semismooth Newton(ssN)method,an inexact proximal DC algorithm with sieving strategy based on a majorized ssN method(siPDCA-mssN)is proposed.For solving the inner problems of siPDCA-mssN from dual,the second-order information is wisely incorporated and an efficient mssN method is employed.The global convergence of the sequence generated by siPDCA-mssN is proved.To solve large-scale CCMV problem,a decomposed siPDCA-mssN(DsiPDCA-mssN)is introduced.To demonstrate the efficiency of proposed algorithms,siPDCA-mssN and DsiPDCA-mssN are compared with the penalty proximal alternating linearized minimization method and the CPLEX(12.9)solver by performing numerical experiments on realword market data and large-scale simulated data.The numerical results demonstrate that siPDCA-mssN and DsiPDCA-mssN outperform the other methods from computation time and optimal value.The out-of-sample experiments results display that the solutions of CCMV model are better than those of other portfolio selection models in terms of Sharp ratio and sparsity.展开更多
This paper proposed a multi-period dynamic optimal portfolio selection model. Assumptions were made to assure the strictness of reasoning. This Approach depicted the developments and changing of the real stock market ...This paper proposed a multi-period dynamic optimal portfolio selection model. Assumptions were made to assure the strictness of reasoning. This Approach depicted the developments and changing of the real stock market and is an attempt to remedy some of the deficiencies of recent researches. The model is a standard form of quadratic programming. Furthermore, this paper presented a numerical example in real stock market.展开更多
基金supported by the National Natural Science Foundation of China (71772060).
文摘This research develops two new models for project portfolio selection, in which the candidate projects are composed of multiple repetitive units. To reflect some real situations, the learning effect is considered in the project portfolio selection problem for the first time. The mathematical representations of the relationship between learning experience and investment cost are provided. One numerical example under different scenarios is demonstrated and the impact of considering learning effect is then discussed.
基金supported by the National Natural Science Foundation of China (7190121471690233)。
文摘The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.
基金the National Key R&D Program of China(2017YFC1405005)the National Natural Science Foundation of China(71901214,71690233).
文摘Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.
基金supported by the National Natural Science Foundation of China(Grant No.11971092)supported by the Fundamental Research Funds for the Central Universities(Grant No.DUT20RC(3)079)。
文摘Optimization problem of cardinality constrained mean-variance(CCMV)model for sparse portfolio selection is considered.To overcome the difficulties caused by cardinality constraint,an exact penalty approach is employed,then CCMV problem is transferred into a difference-of-convex-functions(DC)problem.By exploiting the DC structure of the gained problem and the superlinear convergence of semismooth Newton(ssN)method,an inexact proximal DC algorithm with sieving strategy based on a majorized ssN method(siPDCA-mssN)is proposed.For solving the inner problems of siPDCA-mssN from dual,the second-order information is wisely incorporated and an efficient mssN method is employed.The global convergence of the sequence generated by siPDCA-mssN is proved.To solve large-scale CCMV problem,a decomposed siPDCA-mssN(DsiPDCA-mssN)is introduced.To demonstrate the efficiency of proposed algorithms,siPDCA-mssN and DsiPDCA-mssN are compared with the penalty proximal alternating linearized minimization method and the CPLEX(12.9)solver by performing numerical experiments on realword market data and large-scale simulated data.The numerical results demonstrate that siPDCA-mssN and DsiPDCA-mssN outperform the other methods from computation time and optimal value.The out-of-sample experiments results display that the solutions of CCMV model are better than those of other portfolio selection models in terms of Sharp ratio and sparsity.
文摘This paper proposed a multi-period dynamic optimal portfolio selection model. Assumptions were made to assure the strictness of reasoning. This Approach depicted the developments and changing of the real stock market and is an attempt to remedy some of the deficiencies of recent researches. The model is a standard form of quadratic programming. Furthermore, this paper presented a numerical example in real stock market.