客服热线：400-1616-289
登录注册
伍杨neW

伍杨neW

南开大学 · Jisuanji · 南开大学,Zhejiang University

共 27 篇成果

The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independently of the other mThe conventional constant and variable returns-to-scale models of data11 1envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be reduced independently of the other measures. In this paper we argue that this assumption may not be suitable in applications in which some inputs or outputs are closely related to each other. Assuming strong disposability of such closely related measures may lead to unrealistic input and output profiles, and result in meaningless efficiency scores. Examples include inputs and outputs that are strongly correlated, represent overlapping measures or situations in which one measure is a subset of another. In this paper we develop production technologies that allow the specification of groups of closely related inputs and outputs which are only jointly weakly disposable. This assumption does not change the existing proportions between the closely related measures in the same group. We demonstrate the usefulness of the suggested approach by computational experiments.The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independe

王婷

期刊论文被引：0浏览：0
The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independently of the other mThe conventional constant and variable returns-to-scale models of data11 1envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be reduced independently of the other measures. In this paper we argue that this assumption may not be suitable in applications in which some inputs or outputs are closely related to each other. Assuming strong disposability of such closely related measures may lead to unrealistic input and output profiles, and result in meaningless efficiency scores. Examples include inputs and outputs that are strongly correlated, represent overlapping measures or situations in which one measure is a subset of another. In this paper we develop production technologies that allow the specification of groups of closely related inputs and outputs which are only jointly weakly disposable. This assumption does not change the existing proportions between the closely related measures in the same group. We demonstrate the usefulness of the suggested approach by computational experiments.The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independe

王婷

期刊论文被引：0浏览：0
The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independently of the other mThe conventional constant and variable returns-to-scale models of data11 1envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be reduced independently of the other measures. In this paper we argue that this assumption may not be suitable in applications in which some inputs or outputs are closely related to each other. Assuming strong disposability of such closely related measures may lead to unrealistic input and output profiles, and result in meaningless efficiency scores. Examples include inputs and outputs that are strongly correlated, represent overlapping measures or situations in which one measure is a subset of another. In this paper we develop production technologies that allow the specification of groups of closely related inputs and outputs which are only jointly weakly disposable. This assumption does not change the existing proportions between the closely related measures in the same group. We demonstrate the usefulness of the suggested approach by computational experiments.The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independe

王婷

期刊论文被引：0浏览：0
The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independently of the other mThe conventional constant and variable returns-to-scale models of data11 1envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be reduced independently of the other measures. In this paper we argue that this assumption may not be suitable in applications in which some inputs or outputs are closely related to each other. Assuming strong disposability of such closely related measures may lead to unrealistic input and output profiles, and result in meaningless efficiency scores. Examples include inputs and outputs that are strongly correlated, represent overlapping measures or situations in which one measure is a subset of another. In this paper we develop production technologies that allow the specification of groups of closely related inputs and outputs which are only jointly weakly disposable. This assumption does not change the existing proportions between the closely related measures in the same group. We demonstrate the usefulness of the suggested approach by computational experiments.The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independe

王婷

期刊论文被引：0浏览：0
NBA季后赛，开拓者主场击败雷霆，4-1淘汰对手。利拉德最后时刻完成压哨绝杀，复制了当年绝杀火箭的一幕。50分，3分压哨绝杀，这一刻的利拉德实在太过疯狂。命中绝杀之后，利拉德朝着雷霆替补席做出了挥手再见的手势！本场比赛雷霆背水一战，因此球队开场便拿出额破釜沉舟的气势。乔治开局5中5，很快就帮助雷霆客场领先10分。看到球队处于落后状态，利拉德首节便火力全开。他连续在三分线外命中，帮助球队紧咬比分。首节，利拉德9中6轰下19分，在乔治单节7中6，威少4中3的情况下，帮助开拓者只落后8分。要知道除了利拉德之外，开拓者其余球员的得分都没有超过2分。到了第二节，开拓者其他球员依然难以找到手感，利拉德则继续延续火热手感。突破、三分、2+1，利拉德完全不可阻挡。这一节，利拉德又轰下了15分。在利拉德的率领下，末段赛斯库里连得5分，开拓者竟然神奇地反超比分。半场，开拓者61-60领先。利拉德半场18中12轰下34分，创下了开拓者队史季后赛半场最高分。第三节，利拉德手感终于出现了下滑，但也仅仅是下滑而已。在这一节，利拉德依然命中两记3分，随着底角3分命中之后，开拓者84-75领先9分，此时利拉德的得分也已经来到42分，创下了个人季后赛得分新高。但威少此时大爆发，他连中两记3分率队打出15-4反超。末节，威少连续突破并送出助攻，雷霆将优势一度扩大到15分。此时胜利的天平已经开始向雷霆倾斜。但开拓者实在太过顽强，雷霆连续不中和犯规加上乔治的两罚不中，给了开拓者机会。麦科勒姆中投命中，开拓者扳成113平。乔治中投命中之后，利拉德强打命中。随着威少不中，利拉德最后时刻弧顶持球到1秒钟，中圈附近张手压哨3分命中！绝杀！雷霆出局。全场比赛，利拉德轰下50分，但更加关键的是，他再一次用一个绝杀送走了一个季后赛对手。2014年季后赛，利拉德正是用这样的表现绝杀火箭，送哈登和霍华德出局，这一刻历史重演！NBA季后赛，开拓者主场击败雷霆，4-1淘汰对手。利拉德最后时刻完成压哨绝杀，复制了当年绝杀火箭的一幕。50分，3分压哨绝杀，这一刻的利拉德实在太过疯狂。命中绝杀之后，利拉德朝着雷霆替补席做出了挥手再见的手势！本场比赛雷霆背水一战，因此球队开场便拿出额破釜沉舟的气势。乔治开局5中5，很快就帮助雷霆客场领先10分。看到球队处于落后状态，利拉德首节便火力全开。他连续在三分线外命中，帮助球队紧咬比分。首节，利拉德9中6轰下19分，在乔治单节7中6，威少4中3的情况下，帮助开拓者只落后8分。要知道除了利拉德之外，开拓者其余球员的得分都没有超过2分。到了第二节，开拓者其他球员依然难以找到手感，利拉德则继续延续火热手感。突破、三分、2+1，利拉德完全不可阻挡。这一节，利拉德又轰下了15分。在利拉德的率领下，末段赛斯库里连得5分，开拓者竟然神奇地反超比分。半场，开拓者61-60领先。利拉德半场18中12轰下34分，创下了开拓者队史季后赛半场最高分。第三节，利拉德手感终于出现了下滑，但也仅仅是下滑而已。在这一节，利拉德依然命中两记3分，随着底角3分命中之后，开拓者84-75领先9分，此时利拉德的得分也已经来到42分，创下了个人季后赛得分新高。但威少此时大爆发，他连中两记3分率队打出15-4反超。末节，威少连续突破并送出助攻，雷霆将优势一度扩大到15分。此时胜利的天平已经开始向雷霆倾斜。但开拓者实在太过顽强，雷霆连续不中和犯规加上乔治的两罚不中，给了开拓者机会。麦科勒姆中投命中，开拓者扳成113平。乔治中投命中之后，利拉德强打命中。随着威少不中，利拉德最后时刻弧顶持球到1秒钟，中圈附近张手压哨3分命中！绝杀！雷霆出局。全场比赛，利拉德轰下50分，但更加关键的是，他再一次用一个绝杀送走了一个季后赛对手。2014年季后赛，利拉德正是用这样的表现绝杀火箭，送哈登和霍华德出局，这一刻历史重演！ NBA季后赛，开拓者主场击败雷霆，4-1淘汰对手。利拉德最后时刻完成压哨绝杀，复制了当年绝杀火箭的一幕。50分，3分压哨绝杀，这一刻的利拉德实在太过疯狂。命中绝杀之后，利拉德朝着雷霆替补席做出了挥手再见的手势！本场比赛雷霆背水一战，因此球队开场便拿出额破釜沉舟的气势。乔治开局5中5，很快就帮助雷霆客场领先10分。看到球队处于落后状态，利拉德首节便火力全开。他连续在三分线外命中，帮助球队紧咬比分。首节，利拉德9中6轰下19分，在乔治单节7中6，威少4中3的情况下，帮助开拓者只落后8分。要知道除了利拉德之外，开拓者其余球员的得分都没有超过2分。到了第二节，开拓者其他球员依然难以找到手感，利拉德则继续延续火热手感。突破、三分、2+1，利拉德完全不可阻挡。这一节，利拉德又轰下了15分。在利拉德的率领下，末段赛斯库里连得5分，开拓者竟然神奇地反超比分。半场，开拓者61-60领先。利拉德半场18中12轰下34分

王婷

期刊论文被引：0浏览：3
中文

王婷

Abstracts of Hospital Management Studies, 2018.

期刊论文被引：0浏览：8
43243243

王婷

期刊论文被引：0浏览：0
反3

王婷

Abstracts of Hospital Management Studies, 2018.

期刊论文被引：0浏览：3