A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the...A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.展开更多
To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute d...To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.展开更多
The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker＇s preference information on alternatives taking the form of interva...The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker＇s preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging （C-OWA） operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.展开更多
[目的/意义]为解决当前作物管理中个性化需求难以捕捉、决策过程缺乏灵活性难题,本研究提出了一种基于大语言模型的个性化作物生产智能决策方法[方法]通过自然语言对话收集用户在蔬菜作物管理过程中的个性化需求,涵盖产量、人力资源消...[目的/意义]为解决当前作物管理中个性化需求难以捕捉、决策过程缺乏灵活性难题,本研究提出了一种基于大语言模型的个性化作物生产智能决策方法[方法]通过自然语言对话收集用户在蔬菜作物管理过程中的个性化需求,涵盖产量、人力资源消耗和水肥消耗等方面。随后,将作物管理过程建模为多目标优化问题,同时考虑用户个性化偏好和作物产量,并采用强化学习算法来学习作物管理策略。水肥管理策略的训练通过与环境的交互持续更新,学习在不同条件下采取何种行动以实现最优决策,从而实现个性化的作物管理。[结果和讨论]在gym-DSSAT(Gym-Decision Support System for Agrotechnology Transfer)仿真平台上进行的实验,结果表明,所提出的个性化作物生产智能决策方法能够有效地根据用户的个性化偏好调整作物管理策略。[结论]通过精准捕捉用户的个性化需求,该方法在保证作物产量的同时,优化了人力资源与水肥资源的消耗。展开更多
基金supported by the National Natural Science Foundation of China(51405499)
文摘A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.
基金supported by the National Natural Science Foundation of China(70771041)Chinese Astronautics SupportTechnology Foundation and the Excellent Youth Project of Hubei Provincial Department of Education(Q20082705)
文摘To study the problems of multi-attribute decision making in which the attribute values are given in the form of linguistic fuzzy numbers and the information of attribute weights are incomplete, a new multi-attribute decision making model is presented based on the optimal membership and the relative entropy. Firstly, the definitions of the optimal membership and the relative entropy are given. Secondly, for all alternatives, a set of preference weight vectors are obtained by solving a set of linear programming models whose goals axe all to maximize the optimal membership. Thirdly, a relative entropy model is established to aggregate the preference weight vectors, thus an optimal weight vector is determined. Based on this optimal weight vector, the algorithm of deviation degree minimization is proposed to rank all the alternatives. Finally, a decision making example is given to demonstrate the feasibility and rationality of this new model.
文摘The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker＇s preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging （C-OWA） operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.
文摘[目的/意义]为解决当前作物管理中个性化需求难以捕捉、决策过程缺乏灵活性难题,本研究提出了一种基于大语言模型的个性化作物生产智能决策方法[方法]通过自然语言对话收集用户在蔬菜作物管理过程中的个性化需求,涵盖产量、人力资源消耗和水肥消耗等方面。随后,将作物管理过程建模为多目标优化问题,同时考虑用户个性化偏好和作物产量,并采用强化学习算法来学习作物管理策略。水肥管理策略的训练通过与环境的交互持续更新,学习在不同条件下采取何种行动以实现最优决策,从而实现个性化的作物管理。[结果和讨论]在gym-DSSAT(Gym-Decision Support System for Agrotechnology Transfer)仿真平台上进行的实验,结果表明,所提出的个性化作物生产智能决策方法能够有效地根据用户的个性化偏好调整作物管理策略。[结论]通过精准捕捉用户的个性化需求,该方法在保证作物产量的同时,优化了人力资源与水肥资源的消耗。
