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
Product innovation can be achieved by analyzing leading products patents in the market.Different methods have been proposed for design around patent,commonly using the elimination or replacement of a single patent ele...Product innovation can be achieved by analyzing leading products patents in the market.Different methods have been proposed for design around patent,commonly using the elimination or replacement of a single patent element However,the existing research fails to restore the position and function of the design around object in the original patent portfolio of enterprises,which often leads to the phenomenon of evading one patent and violating another.This paper proposes a method for design around patent through using the fusion of technologies of the evolution theory and bundle-type patent portfolio analysis in the initial stage of product development.The object system is analyzed to select technical opportunities through the evolutionary path of technologies and functional trimming methods to achieve circumvent barriers of bundle-type patents.The bundle patent portfolio is analyzed for the product evolution with a radar map.The technological evolution path is combined with the TRIZ innovation method to identify and solve the design problem.Patentability of the new design is evaluated using the patent system rules for innovative scheme difference from the original patent portfolio.The method is verified in a case study for the design of a glass-wiping robot.The design solution has been patented.展开更多
This paper studies the multi-period mean-variance(MV)asset-liability portfolio management problem(MVAL),in which the portfolio is constructed by risky assets and liability.It is worth mentioning that the impact of gen...This paper studies the multi-period mean-variance(MV)asset-liability portfolio management problem(MVAL),in which the portfolio is constructed by risky assets and liability.It is worth mentioning that the impact of general correlation is considered,i.e.,the random returns of risky assets and the liability are not only statistically correlated to each other but also correlated to themselves in different time periods.Such a model with a general correlation structure extends the classical multiperiod MVAL models with assumption of independent returns.The authors derive the explicit portfolio policy and the MV efficient frontier for this problem.Moreover,a numerical example is presented to illustrate the efficiency of the proposed solution scheme.展开更多
Financial market has systemic complexity and uncertainty.For investors,return and risk often coexist.How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk ...Financial market has systemic complexity and uncertainty.For investors,return and risk often coexist.How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization(PO).At present,due to the influence of modeling and algorithm solving,the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multiobjective models.PO is actually considered as a multi-stage multi-objective optimization problem in real investment scenarios.It is more difficult than the previous single-stage PO model for meeting the realistic requirements.In this paper,the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate(M-ISTARR-MD)PO model which effectively characterizes the real investment scenario.In order to solve the multi-stage multi-objective PO model with complex multi-constraints,the authors designed a multi-stage constrained multi-objective evolutionary algorithm with orthogonal learning(MSCMOEA-OL).Comparing with four well-known intelligence algorithms,the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock dataset.This paper provides a new way to construct and solve the complex PO model.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 National Natural Science Foundation of China(Grant Nos.51675159,51605135)Central Guided Local Science and Technology Development Project(Grant No.1824-1837G)Ministry of Science and Technology(Grant No.2017IM040100)
文摘Product innovation can be achieved by analyzing leading products patents in the market.Different methods have been proposed for design around patent,commonly using the elimination or replacement of a single patent element However,the existing research fails to restore the position and function of the design around object in the original patent portfolio of enterprises,which often leads to the phenomenon of evading one patent and violating another.This paper proposes a method for design around patent through using the fusion of technologies of the evolution theory and bundle-type patent portfolio analysis in the initial stage of product development.The object system is analyzed to select technical opportunities through the evolutionary path of technologies and functional trimming methods to achieve circumvent barriers of bundle-type patents.The bundle patent portfolio is analyzed for the product evolution with a radar map.The technological evolution path is combined with the TRIZ innovation method to identify and solve the design problem.Patentability of the new design is evaluated using the patent system rules for innovative scheme difference from the original patent portfolio.The method is verified in a case study for the design of a glass-wiping robot.The design solution has been patented.
基金partially supported by the National Natural Science Foundation of China under Grant Nos.72201067,12201129,and 71973028the Natural Science Foundation of Guangdong Province under Grant No.2022A1515010839+1 种基金the Project of Science and Technology of Guangzhou under Grant No.202102020273the Opening Project of Guangdong Province Key Laboratory of Computational Science at Sun Yat-sen University under Grant No.2021004。
文摘This paper studies the multi-period mean-variance(MV)asset-liability portfolio management problem(MVAL),in which the portfolio is constructed by risky assets and liability.It is worth mentioning that the impact of general correlation is considered,i.e.,the random returns of risky assets and the liability are not only statistically correlated to each other but also correlated to themselves in different time periods.Such a model with a general correlation structure extends the classical multiperiod MVAL models with assumption of independent returns.The authors derive the explicit portfolio policy and the MV efficient frontier for this problem.Moreover,a numerical example is presented to illustrate the efficiency of the proposed solution scheme.
基金supported by the National Natural Science Foundation of China under Grant No.61973042Beijing Natural Science Foundation under Grant No.1202020。
文摘Financial market has systemic complexity and uncertainty.For investors,return and risk often coexist.How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization(PO).At present,due to the influence of modeling and algorithm solving,the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multiobjective models.PO is actually considered as a multi-stage multi-objective optimization problem in real investment scenarios.It is more difficult than the previous single-stage PO model for meeting the realistic requirements.In this paper,the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate(M-ISTARR-MD)PO model which effectively characterizes the real investment scenario.In order to solve the multi-stage multi-objective PO model with complex multi-constraints,the authors designed a multi-stage constrained multi-objective evolutionary algorithm with orthogonal learning(MSCMOEA-OL).Comparing with four well-known intelligence algorithms,the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock dataset.This paper provides a new way to construct and solve the complex PO model.
