Ranking efficient DMUs using cooperative game theory

The problem of ranking Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) has been widely studied in the literature. Some of the proposed approaches use cooperative game theory as a tool to perform the ranking. In this paper, we use the Shapley value of two different cooperative games in which the players are the efficient DMUs and the characteristic function represents the increase in the discriminant power of DEA contributed by each efficient DMU. The idea is that if the efficient DMUs are not included in the modified reference sample then the efficiency score of some inefficient DMUs would be higher. The characteristic function represents, therefore, the change in the efficiency scores of the inefficient DMUs that occurs when a given coalition of efficient units is dropped from the sample. Alternatively, the characteristic function of the cooperative game can be defined as the change in the efficiency scores of the inefficient DMUs that occurs when a given coalition of efficient DMUs are the only efficient DMUs that are included in the sample. Since the two cooperative games proposed are dual games, their corresponding Shapley value coincide and thus lead to the same ranking. The more an ef- ficient DMU impacts the shape of the efficient frontier, the higher the increase in the efficiency scores of the inefficient DMUs its removal brings about and, hence, the higher its contribution to the overall discriminant power of the method. The proposed approach is illustrated on a number of datasets from the literature and compared with existing methods

Internet Based Benchmarking

We discuss the design of interactive, internet based benchmarking using parametric (statistical) as well as non-parametric (DEA) models. The user receives benchmarks and improvement potentials. The user is also given the possibility to search different efficiency frontiers and hereby to explore alternative improvement strategies. An implementation of both a parametric and a non parametric model are presented.Research and Development/Tech Change/Emerging Technologies,

The Chinese position as a global player in international comparison with the WTO members: Efficiency analysis and 4IR

During the last quarter-century, globalisation processes affected changes in the world economy in the form of intensifying competition in the international and internal markets. The result is the creation of a global marketplace that is mostly indifferent to national borders and governmental influences. This development has generated widespread interest in competitiveness. Competitiveness affects international relations, especially nowadays, given the changing position of the global leaders and the growth of new economic powers such as China. China has come a long way and has the opportunity to be a global leader in several required fields that will be the cornerstones of global growth in the next decades. Led by China, emerging economies are increasing their share in the worldwide economy and intensifying competition in nearly all sectors. It creates new threats and challenges for players in the global economy, and growing competitiveness must be efficient. The article evaluates the Chinese competitiveness in comparison with the World Trade Organization members by the Data Envelopment Analysis in the pre-in-post crisis period and considering the Fourth Industrial Revolution shifting humanity into a new phase.Web of Science6148

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

Sustainable R&D portfolio assessment.

Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality etc. Even if these data are available, then consistent decision support tools are not ready available. Based on the findings from an industry review, we developed a DEA model that permits to support strategic R&D portfolio management. We underscore the usability of this approach with real life examples from two different industries: consumables and materials manufacturing (polymers).R&D portfolio management; Data envelopment analysis; Sustainable R&D;

Agricultural Economics Education in Ukrainian Agricultural Universities: An Efficiency Analysis Using Data Envelopment Analysis

Ukraine's transition from a centrally-planned to a market economy has had a profound effect upon its agricultural sector and agricultural universities. A substantial reduction in state financing has forced universities to adopt a range of survival strategies, with varying degrees of success. In this paper we use data envelopment analysis to examine the technical efficiency of 44 agricultural economics programs from 19 Ukrainian universities during the 2002/03 academic year. Our empirical results indicate wide disparities in performance, ranging from 36% to 100% technical efficiency. A second-stage analysis suggests that factors such as student demand, commercial activities and staff quality help explain a portion of this variation.higher education, Ukraine, efficiency, data envelopment analysis, Teaching/Communication/Extension/Profession, I21, C14, Q16,