Titlebook: Regional Performance Measurement and Improvement; New Developments and Soushi Suzuki,Peter Nijkamp Book 2017 The Editor(s) (if applicable)

Motion

書目名稱Regional Performance Measurement and Improvement影響因子(影響力)




書目名稱Regional Performance Measurement and Improvement影響因子(影響力)學科排名




書目名稱Regional Performance Measurement and Improvement網絡公開度




書目名稱Regional Performance Measurement and Improvement網絡公開度學科排名




書目名稱Regional Performance Measurement and Improvement被引頻次




書目名稱Regional Performance Measurement and Improvement被引頻次學科排名




書目名稱Regional Performance Measurement and Improvement年度引用




書目名稱Regional Performance Measurement and Improvement年度引用學科排名




書目名稱Regional Performance Measurement and Improvement讀者反饋




書目名稱Regional Performance Measurement and Improvement讀者反饋學科排名

VEST

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ornithology

Book 2017 projection approaches in data envelopment analysis (DEA). The DFM model can produce a most effective solution in efficiency improvement projections for inefficient spatial entities (decision-making units).?.The book provides a set of fresh contributions to a quantitative assessment of the performan

anniversary

Introduction,he necessity of regional performance measurement in the light of the Japanese socioeconomic and spatial development from the end of WW2 up to the present, while we also highlight the future challenges of a changing population in this country. We discuss the main evaluation methods for each era and h

呼吸

Overview of DEA and Its Improvementsnes et al. (Eur J Oper Res, 2:429–444, 1978) and based on the seminal article by Farrell (J Roy Stat Soc 120:253–290, 1957) aims to develop a comparative measure for production efficiency. We present first a brief history of the development of DEA. Next, we make a comparison of DEA and stochastic fr

GLOOM

Significance of DEA for Regional Performance Measurement rules of application to regional performance measurement from the viewpoint of economies of scale, treatment of input and output items, and the number of DMUs, inputs and outputs. Furthermore, in applications of DEA, we usually need to pay attention to the influence on efficiency scores by the numb

小官

The Distance Friction Minimization (DFM) Model in DEA). This model generates an appropriate (nonradial) efficiency-improvement projection model, for both an input reduction and an output increase. In this approach, a generalized distance function, based on a Euclidean distance metric in weighted spaces, is proposed to assist a decision making unit (DM

仲裁者

Prognosis

Performance Measurement of Local Government Finance in Japan: Combination of Goals-Achievement ModelA) approach on the basis of the Charnes-Cooper-Rhodes (CCR) method – in order to generate a more satisfactory efficiency-improvement projection model in conventional DEA. Another novelty of our approach is the introduction of prior goals set by a DMU by using a GA approach. The GA model specifies a