Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge.However,one of key challenges in designing the s...Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge.However,one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge.In this paper,we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics,integrating conceptual analysis,conceptual modeling,quantitative modeling and simulation implementation.We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition,thus classifying the related battle damage knowledge logically.Then,we construct the conceptual model of influencing factors of battle damage by using Entity-Relations hip approach,thus describing their interactions reasonably.Subsequently,we extract the important influencing factors by using social network analysis,thus evaluating their importance quantitatively and further clarifying the elements of simulation.Finally,we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input,and obtain simulation model output,i.e.,new battle damage laws knowledge,thus verifying feasibility and effectiveness of the proposed method.The results show that this method can be used to support human decision-making and action.展开更多
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
基金This research was funded by National Natural Science Foundation of China(grant number 61473311,70901075)Natural Science Foundation of Beijing Municipality(grant number 9142017)military projects funded by the Chinese Army.
文摘Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge.However,one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge.In this paper,we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics,integrating conceptual analysis,conceptual modeling,quantitative modeling and simulation implementation.We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition,thus classifying the related battle damage knowledge logically.Then,we construct the conceptual model of influencing factors of battle damage by using Entity-Relations hip approach,thus describing their interactions reasonably.Subsequently,we extract the important influencing factors by using social network analysis,thus evaluating their importance quantitatively and further clarifying the elements of simulation.Finally,we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input,and obtain simulation model output,i.e.,new battle damage laws knowledge,thus verifying feasibility and effectiveness of the proposed method.The results show that this method can be used to support human decision-making and action.
