A new method, Collaborative Allocation （CA）, is proposed to solve the large-scale optimum allocation problem in aircraft conceptual design. According to the characteristics of optimum allocation in aircraft conceptu...A new method, Collaborative Allocation （CA）, is proposed to solve the large-scale optimum allocation problem in aircraft conceptual design. According to the characteristics of optimum allocation in aircraft conceptual design. The principle and mathematical model of CA are established. The optimum allocation problem is decomposed into one main optimization problem and several sub-optimization problems. A group of design requirements for subsystems are provided by the main system respectively, and the subsystems execute their own optimizations or further provide the detailed design requirements to the bottom components of aircraft, such as spars, ribs and skins, etc. The subsystems minimize the discrepancy between their own local variables and the corresponding allocated values, and then return the optimization results to main optimization. The main optimization is performed to reallocate the design requirements for improving the integration performance and progressing toward the compatibilities among subsystems. CA provides the general optimum allocation architecture and is easy to be carried out. Furthermore, the concurrent computation can also be realized. Two examples of optimum reliability allocation are used to describe the implementation procedure of CA for two-level allocation and three-level allocation respectively, and to validate preliminarily its correctness and effectiveness. It is shown that the developed method can be successfully used in optimum allocation of design requirements. Then taking weight requirement allocation as example, the mathematical model and solution procedure for collaborative allocation of design requirements in aircraft conceptual design are briefly depicted.展开更多
In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high ...In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high differentiations among the three strata in which this population could be classified. The rural population of Evros Prefecture （Greece） with criterion the mean altitude of settlements was classified in three strata, the mountainous, semi-mountainous and fiat population for the estimation of mean consumption of forest fuelwood for covering of heating and cooking needs in households of these three strata. The analysis of this methodology includes： （1） the determination of total size of sample for entire the rural population and its allocation to the various strata; （2） the investigation of effectiveness of stratification with the technique of analysis of variance （One-Way ANOVA）; （3） the conduct of sampling research with the realization of face-to-face interviews in selected households and （4） the control of forms of the questionnaire and the analysis of data by using the statistical package for social sciences, SPSS for Windows. All data for the analysis of this methodology and its practical application were taken by the pilot sampling which was realized in each stratum. Relative paper was not found by the review of literature.展开更多
基金National Natural Science Foundation of China (10377015)
文摘A new method, Collaborative Allocation （CA）, is proposed to solve the large-scale optimum allocation problem in aircraft conceptual design. According to the characteristics of optimum allocation in aircraft conceptual design. The principle and mathematical model of CA are established. The optimum allocation problem is decomposed into one main optimization problem and several sub-optimization problems. A group of design requirements for subsystems are provided by the main system respectively, and the subsystems execute their own optimizations or further provide the detailed design requirements to the bottom components of aircraft, such as spars, ribs and skins, etc. The subsystems minimize the discrepancy between their own local variables and the corresponding allocated values, and then return the optimization results to main optimization. The main optimization is performed to reallocate the design requirements for improving the integration performance and progressing toward the compatibilities among subsystems. CA provides the general optimum allocation architecture and is easy to be carried out. Furthermore, the concurrent computation can also be realized. Two examples of optimum reliability allocation are used to describe the implementation procedure of CA for two-level allocation and three-level allocation respectively, and to validate preliminarily its correctness and effectiveness. It is shown that the developed method can be successfully used in optimum allocation of design requirements. Then taking weight requirement allocation as example, the mathematical model and solution procedure for collaborative allocation of design requirements in aircraft conceptual design are briefly depicted.
文摘In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high differentiations among the three strata in which this population could be classified. The rural population of Evros Prefecture （Greece） with criterion the mean altitude of settlements was classified in three strata, the mountainous, semi-mountainous and fiat population for the estimation of mean consumption of forest fuelwood for covering of heating and cooking needs in households of these three strata. The analysis of this methodology includes： （1） the determination of total size of sample for entire the rural population and its allocation to the various strata; （2） the investigation of effectiveness of stratification with the technique of analysis of variance （One-Way ANOVA）; （3） the conduct of sampling research with the realization of face-to-face interviews in selected households and （4） the control of forms of the questionnaire and the analysis of data by using the statistical package for social sciences, SPSS for Windows. All data for the analysis of this methodology and its practical application were taken by the pilot sampling which was realized in each stratum. Relative paper was not found by the review of literature.
