A revised dynamic data envelopment analysis model with budget constraints

Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach

Nested dynamic network data envelopment analysis models with infinitely many decision making units for portfolio evaluation

This study used a dynamic data envelopment analysis (DEA) model to investigate the learning carry-over effect on the performance of Taiwan’s municipal solid waste (MSW) recycling systems. Regression analysis was used to estimate the latent learning effect in the MSW recycling systems of 23 local governments during 2002–2011. The results were incorporated into a dynamic DEA model that evaluated the relative performance of local government systems. This research makes three major contributions to the field. Firstly, it develops an approach that incorporates the carry-over effects of organizational learning into a dynamic DEA model. Secondly, it evaluates the influence of those carry-over effects on the MSW recycling performance of Taiwan’s local governments. Finally, it identifies policies to help local governments with inefficient MSW recycling systems as they seek to improve and promote recycling activities. In addition, local government should continue to promote its recycling policies.

Efficiency evaluation of Asia's cultural tourism using a dynamic DEA approach

The paper is the first study to combine four-stage approach and dynamic data envelopment analysis (DEA) to investigate the efficiencies of 45 international tourist hotels in Taiwan during 2002–2011. Using the four-stage approach of Fried et al. [1999. Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis, 12(3), 249–267] and the dynamic DEA model by Tone and Tsutsui [2010. Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145–156], the study considers the effects of the external operating environments and carry-over activities between two consecutive terms, and calculates managerial efficiency of international tourism hotels in Taiwan. The empirical results show that dynamic DEA provides more stable pure managerial efficiencies than traditional DEA, reflecting the continuity of the links between terms. Then the study employs Tobit regression to estimate the effects of location, management style, and occupancy rate on input slacks. The location in resort area leads to less slacks of employee and room. The chain system helps reduce slacks in room and area of meal department. The slack of employee and room gets lower while occupancy rate gets higher. After controlling the three external environment variables, the efficiency scores of 28 hotels increase, 9 hotels fall, and other 8 hotels still sustain on the efficient frontier.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and ‎expected values of the objective functions. In the initial proposed†â€DRF-DEA model, the inputs and outputs are assumed to be ‎characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this ‎assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-‎objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective ‎functions are the expected values of functions of normal random variables. In order to improve in computational time, we then ‎convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives ‎programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic ‎Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results ‎show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of ‎runtime and solution quality. ‎

The evaluation of sustainable suppliers is one of the most complex tasks in sustainable supply chain management (SSCM). Classical data envelopment analysis (DEA) and dynamic DEA (DDEA) models are heavily dependent on historical data and do not forecast future efficiencies of decision-making units (DMUs). The primary objective of this paper is to present a new predictive paradigm for ranking sustainable suppliers in SSCM. The proposed model combines goal programming and DDEA in an integrated and seamless paradigm to determine the future efficiencies of DMUs (suppliers). It also shifts the decision maker’s role from monitoring the past to planning the future. A case study is presented to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures and algorithms.

Enhanced prioritisation of prospective scenarios for power generation in Spain: How and which one?

With the development of China's economy, pollution has made serious impact on environment and human health. However, environmental protection and residents' health are becoming more and more important along with the country's social and economic transformation. Most existing studies have analyzed the path of economic impact on the environment and the production, pollution, and health in isolation. This research takes panel data of 30 provinces in China (including autonomous regions and municipalities, excluding Tibet, Hong Kong, Macau, and Taiwan) spanning 2014 to 2017 as an example, builds an evaluation indicator system on the basis of the three stages of economic production, wastewater treatment, and human health, and uses the undesirable three-stage dynamic data envelopment analysis model to empirically evaluate the total efficiency, stage efficiency, and the efficiency of various indicators. The research results show the average efficiency of the three stages in most provinces in four years is below 0.5, indicating the poor coordination of each stage; the efficiency gaps among the eastern, central, and western regions are very large because of the resource endowments, geographical environment, industrial structure, strategic adjustment, and other infactors. The total efficiency of the three stages of production input, wastewater treatment, and health output in the eastern region are higher than that of the central region and the western region. From the perspective of stage efficiency, most of the 30 provinces exhibit production efficiency < health efficiency < wastewater treatment efficiency. For the three-phase input and output indicators, the efficiency values and development trends of different provinces vary. The efficiencies of input variables in the wastewater treatment stage and health stage are low in most provinces. This means that the provinces should implement accurate policies according to their own evaluation results and improve the relevance and coordination among the three stages through reasonable allocation of medical input and arrangement of urban employment.

Purpose– This paper aims to investigate the effect of intellectual capital (IC) on the operating efficiency of non-life insurance firms in China.Design/methodology/approach– The authors use a dynamic data envelopment analysis model called dynamic slacks-based measure (DSBM) model to estimate the operating efficiency of 32 Chinese non-life insurance firms. Using a panel data set for the period from 2006 to 2010, the authors run ordinary least squares (OLS) regressions to find the relationship between IC and efficiency performance.Findings– The authors find that the insurers have almost monotonically decreasing efficiency for the period from 2006 to 2010. Regression results show that human capital, structural capital and relational capital are significantly and positively related to operating efficiency.Research limitations/implications– This study suggests that managers of the Chinese non-life insurers should devote attention to the investments in IC to stay sustainable.Originality/value– This is the first paper to examine the impact of IC on operating efficiency in the Chinese non-life insurance industry. This study differs from prior studies in that the authors use the DSBM model proposed by Tone and Tsutsui (2010) for evaluating the operating efficiency using a dynamic process.

Political governance, corruption perceptions index, and national dynamic energy efficiency

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.

Inverse chance-constrained dynamic data envelopment analysis under natural and managerial disposability: Concerning renewable energy efficiency and potentials

This article reports the evolution of the literature on Dynamic Data Envelopment Analysis (DDEA) models from 1996 to 2016. Systematic searches in the databases Scopus and Web of Science were performed to outline the state of the art. The results enabled the establishment of DDEA studies as the scope of this article, analyzing the transition elements to represent temporal interdependence. The categorization of these studies enabled the mapping of the evolution of the DDEA literature and identification of the relationships between models. The three most widely adopted studies to conduct DDEA research were classified as structuring models. Mapping elucidated the literature behavior through three phases and showed an increase in publications with applications in recent years. The analysis of applications indicated that most studies address evaluations in the agriculture and farming, banking and energy sectors and consider the facilities as transition elements between analysis periods.

Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks