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

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

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.

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

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.

This study examines performance of provincial health‐care system in China, using a dynamic network data envelopment analysis (DEA) approach. We focus on efficiency improvements both within provincial health‐care system and within two separate internal sub‐divisions. The administration division engages in financial management while the medical care division provides medical care services. Then there are two main results. First, the average overall efficiency score of provincial health‐care system is 0.9316 from 2010 to 2017. Second, the productivity progress in administration division is lower than that in medical care division, and the technical advances contribute most of the productivity progress. The research gap and the innovation of this paper lie in three aspects: First, this paper opens the black box of health production function and use the network DEA to analyze Chinese provincial health‐care system and its inner two constructs. Second, this paper discusses the efficiency scores as well as dynamic changes in one model. Finally, this paper tries to reveal the regional heterogeneity of static efficiency as well as the dynamic efficiency changes.

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 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.

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

PurposeThe purpose of this paper is to propose a benchmarking framework to evaluate the efficiency and effectiveness of the hotel industry, in a multi‐period context, with consideration of perishable traits and carry‐over activities. The sustained high performers in the case study are identified and their business strategies are discussed.Design/methodology/approachThe dynamic DEA (data envelopment analysis) approach is used to identify the multi‐period sustained high performers. The super‐efficiency DEA approach is employed to conduct a thorough ranking under an input‐output‐consumption structure. The supplementary analysis is further implemented to help elucidate the benchmarking results.FindingsIn total, nine out of 80 international tourist hotels in Taiwan during 2006‐2010 are identified as the sustained high performers. These hotels have diverged business strategies in terms of employees (intensive versus economical labor forces), products (room versus F&B (food and beverage) services), prices (extremely expensive versus very inexpensive room rates), guests (business versus tourism guests), and others (e.g. location advantages).Originality/valueThis study contributes to benchmarking literature and to hotel industry in several aspects. Compared with conventional single‐period benchmarking in efficiency aspect only, the proposed multi‐period benchmarking framework under input‐production‐consumption structure can take into account the carry‐over activities, account for perishable traits, provide more robust results, and add more useful information to facilitate the hotel managers to ameliorate the efficiency and effectiveness. The proposed framework should be readily applied to other service industries (e.g. transport).

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.

<p style='text-indent:20px;'>The conventional data envelopment analysis (DEA) models presume that the values of input-output variables of the decision-making units (DMUs) are precisely known. However, some real-life situations can authoritatively mandate the data to vary in concrete fine-tuned ranges, which can include negative values and measures that are allowed to take integer values only. Our study proposes an integrated dynamic DEA model to accommodate interval-valued and integer-valued features that can take negative values. The proposed one-step model follows the directional distance function approach to determine the efficiency of DMUs over time in the presence of carryovers connecting the consecutive periods. We use the pessimistic and optimistic standpoints to evaluate the respective lower and upper bounds of the interval efficiency scores of the DMUs. We compare our proposed approach with a few relevant studies in the literature. We also validate our model on a synthetically generated dataset. Furthermore, we showcase the proposed procedure's applicability on a real dataset from 2014 to 2018 of airlines operating in India.</p>

A dynamic multi-stage data envelopment analysis model with application to energy consumption in the cotton industry

In China, industrial pollution has become an urgent problem for policy makers and enterprise managers. To better support industrial development, we need to determine the effectiveness of policies through efficiency evaluation. China’s provincial industrial system consists of two stages: production and emission reduction. The emission reduction stage is composed of three parallel sub stages: solid waste treatment, waste gas treatment and wastewater treatment. In this process, the treatment capacity of industrial wastewater treatment facilities can be used as carry forward variable, which is not only the desirable output of the previous emission reduction stage, but also the input of the current emission reduction stage. Therefore, this paper proposes a dynamic hybrid two-stage data envelopment analysis (DEA) model for eco-efficiency evaluation of industrial systems, and applies it to a case study of Chinese regional industry. Applying the data collected from 2011 to 2015 to the model, the following conclusions can be drawn: (1) During the whole survey period, the average eco-efficiency was 0.9027. The overall eco-inefficiency of China’s provincial industrial system during the study period is mainly due to low efficiency of solid waste treatment and waste gas treatment. (2) The average eco-efficiency of provincial industrial system increased steadily from 2011 (0.6448) to 2014 (0.6777), but decreased slightly in 2015 (0.5908). (3) The carry forward treatment capacity of industrial wastewater treatment facilities has a remarkable impact on provincial industrial system efficiency scores, especially at the wastewater treatment stage (0.6002 vs 0.3691). (4) Provincial industrial system exists distinct geographical characteristics of low efficiency. This study has important guiding significance for policy makers and enterprise managers who are concerned about industrial pollution control.

In this study, we focus on an unintended limitation in the conventional specification of budget constraints in dynamic data envelopment analysis (DEA). In particular, it is revealed that in the existing models, the production possibilities tend to be underestimated owing to misleading assumptions, and consequently, the models yield overstated efficiency measures. Besides addressing the relevant issues, we developed a revised dynamic DEA model to properly take budget constraints into account in a dynamic setting, while preserving the major principles of efficiency analysis. We compared the proposed model with the intertemporal cost indirect technology to clarify the differences. Finally, we illustrated, by way of examples, an empirical case of operational assessment in fire departments.