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Since airlines that employ their resources effectively will achieve operating profitability, air route resource allocation is significant for airlines. This study aims to investigate an appropriate model to reallocate resources into each air route of an airline company.

This study proposes a network centralized data envelopment analysis (DEA) models with slack-based measure (SBM). The proposed model not only takes into account the two interconnected stages but also considers the nonradial approach with transfer-in and transfer-out slacks for resource reallocating. Furthermore, the authors modify the objective function to an input-oriented function with SBM, and divide the model into passenger and freight parts, which makes the model more realistic for the characteristic of air routes.

The empirical analysis using an airline company's internal data provides airline operators with information on how they increase or decrease input resources, which can serve as a practical guideline of resource reallocation. Specifically, the results indicate that the airline company should increase their input resources into long-haul air routes such as KOR-OCN while decreasing their input resources into short-haul air routes such as Korean-Oceania (KOR-OCN), Korean-Chinese (KOR-CHN), Korean-Southeast Asian (KOR-SEA), Korean-Japanese (KOR-JPN).

Although some papers evaluate air route efficiencies based on the DEA approach, a few existing papers have addressed resource allocation for air routes. This paper is the first to study the resource reallocation for air routes based on the DEA approach, contributing to the literature in expanding the scope of research on resource reallocation.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

The purpose of this paper is to present a holistic approach for measuring overall bank efficiency and its decomposition in intermediation and operating efficiencies.

Recently developed two-stage network data envelopment analysis model by Liang et al. (2008) has been used for obtaining intermediation and operational efficiencies along with overall bank efficiency. The bootstrapped truncated regression algorithm as proposed by Simar and Wilson (2007) has been employed to explore the influential determinants of intermediation and operating efficiencies.

The empirical results reveal that the operating inefficiency is the dominant source of overall bank inefficiency in Indian banking sector. Another interesting finding is that public sector banks are more efficient than private banks in the intermediation stage of production process, while private banks are more efficient in the operating stage of production process. Finally, the results of bootstrapped truncated regression show that variations in intermediation efficiency are explained by bank size, liquidity position, directed lending and intermediation cost, while inter-bank differences in operating efficiency are influenced by profitability and income diversification.

The most significant practical implication that has been derived from the research findings is that at the industry level, overall efficiency enhancement needs improvement both in terms of resource-utilization and income-generating abilities of the banks. However, the relatively easy way to achieve higher bank efficiency is to improve the efficiency of banks in generating incomes from interest and fee-based sources.

This paper is the first to provide a comprehensive assessment of performance of Indian banks by examining the efficiency of individual banks considering both the intermediation and operating approaches simultaneously.

The purpose of this study is to address three managerial issues in a retail network under a centralised regime, including resource allocation, expansion and downsizing. In particular, this study investigates how to optimise resource allocation across retailers and how to decide the optimal size of a retail network for an empirical case, where 27 retailers belong to an automobile parts supplier in Taiwan.

The centralised data envelopment analysis (DEA) approach is used to optimise resource allocation among retailers and to seek the possibility of either an expansion and/or downsizing for a retail network.

The case company knows the best way to (re)allocate its available resources and (re)arrange the outcome goals for its own retailers. The 27 retailers are assigned specific targets for input and output adjustments to become Pareto or technically efficient units. Besides, an experiment is designed to measure how changing the number of retailers affects the group efficiency of the retail network. To keep the current aggregated output level of the retail network, the centralised supplier needs at least 24 retailers. However, the retail network with 37 retailers can achieve maximal group efficiency.

Centralised resource allocation is an inherent feature of most supplier-retailer relationships, where the supplier is usually a central planner with the power to allocate available resources among its own retailers. To the best of our knowledge, this is the first study to assess retail performance under a centralised regime.

It is always of great importance for managers in organizations to evaluate their staff members and create incentive systems, using instruments such as Data Envelopment Analysis (DEA) and DEA-R (DEA models based on ratio analysis). The purpose of this paper is to propose a two-stage network incentives system for commercial banks.

Centralized Resource Allocation (CRA) models make it possible to project all decision-making units (DMUs) onto the efficient frontier by solving a single linear programming model. In this paper, we use our proposed DEA-R-based CRA models to evaluate commercial banks in a two-stage case when the only ratios available are the assets-to-costs and income-to-assets vectors.

Thirteen commercial banks modeled as two-stage networks were evaluated by the models proposed in two different cases of ratio data. Results suggest that the proposed methodology yields more accurate efficiency scores, thus allowing better discrimination among DMUs. Furthermore, evaluating the DMUs when they are structured as two-stage (or even three-stage) networks makes it possible to examine the incentives system in more detail. Therefore, the use of incentive systems by managers would allow a better focus on the priority activities of commercial banks and a faster movement toward the frontier of best practices.

The super-efficiency scores of a number of commercial banks are evaluated based on the CRA model, as a cornerstone criterion for the two-stage evaluation in DEA-R, thus allowing the rank of each commercial bank in terms of the incentives system rather on the performance of the productive process.

This paper aims to present two-stage network models in the presence of stochastic ratio data.

Black-box, free-link and fix-link techniques are used to apply the internal relations of the two-stage network. A deterministic linear programming model is derived from a stochastic two-stage network data envelopment analysis (DEA) model by assuming that some basic stochastic elements are related to the inputs, outputs and intermediate products. The linkages between the overall process and the two subprocesses are proposed. The authors obtain the relation between the efficiency scores obtained from the stochastic two stage network DEA-ratio considering three different strategies involving black box, free-link and fix-link. The authors applied their proposed approach to 11 airlines in Iran.

In most of the scenarios, when alpha in particular takes any value between 0.1 and 0.4, three models from Charnes, Cooper, and Rhodes (1978), free-link and fix-link generate similar efficiency scores for the decision-making units (DMUs), While a relatively higher degree of variations in efficiency scores among the DMUs is generated when the alpha takes the value of 0.5. Comparing the results when the alpha takes the value of 0.1–0.4, the DMUs have the same ranking in terms of their efficiency scores.

The authors innovatively propose a deterministic linear programming model, and to the best of the authors’ knowledge, for the first time, the internal relationships of a two-stage network are analyzed by different techniques. The comparison of the results would be able to provide insights from both the policy perspective as well as the methodological perspective.

The recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.

This paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.

Among various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.

This study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

This paper aims to propose an integrated centralized data envelopment analysis (CDEA)-balanced scorecard (BSC) model to provide a selective approach to determine the most efficient indicators for evaluating the four perspectives of the BSC.

An integer linear programming model based on the efficiency concept of the CDEA method is presented to select the best indicators for evaluating four perspectives of the BSC. The basis for selecting indicators in this method is to maximize the overall performance of each BSC perspective. The modeling is performed on a real case. The considered model is solved using a general algebraic modeling system software for the data set of the real case.

A real-world case is solved using the proposed method. The integration of the CDEA and the BSC seems to be advantageous because it sheds more light on the complexity and tradeoffs inherent in actual performance measurement. It is important to note that there cannot be a unique and universal model of performance measurement applicable in every situation, in every organization and at any time.

The data set of a single organization in the manufacturing industry is used to show the performance of the proposed mathematical model; therefore, generalization of the results should be done cautiously. This framework is based on the Iranian community and experts’ viewpoints; therefore, different results may be obtained if it is applied elsewhere, and the importance of perspectives and their indicators might show different results in other populations and other countries. In addition, because the data is collected in a specific period of time, the results cannot be extended to other periods of time.

The main contribution of this paper lies in the adaption of a new integrated CDEA-BSC model to performance measurement in the industrial sector that technology improves the ultimate results of performance measurement and provides wider opportunities for decision-makers. This paper aids managers and decision-makers to control the efficient indicators in perspectives.

Due to the rapid development and innovation in the Internet-based technology, conventional banks are under pressure and have to compete with Internet-based finance. This has made banks adopt measures to improve operational efficiency and reduce input and increase output.

The authors had proposed a two-stage fairness concern efficiency model based on the classical theory of data envelopment analysis (DEA) and performed an empirical study to measure agricultural loan efficiency in the 20 major Chinese banks.

The findings of the empirical analysis are as follows: (1) peer-induced fairness concern has no impact on deposit efficiency in a centralized bank supply chain; (2) The China Merchants Bank (CMB) has the third lowest deposit efficiency; (3) monotonicity of loan efficiency with input allocation depends on a bank's ownership structure; (4) efficiency ranks are strongly affected by the fairness concern; (5) most Chinese banks show a low agricultural loan efficiency.

This paper contributes to the literature in several ways. First, to the best of the authors’ knowledge, this is the first attempt to analyze agricultural loan efficiency for a bank supply chain system with the fairness concern. This work reveals the hidden factor that restricts loan efficiency of Chinese banks. Second, the proposed fairness concern two-stage DEA model has shown good ability for full ranking. It can provide a new perspective to the classical DEA literature for ranking decision-making units (DMUs). Third, the authors have demonstrated empirical bank efficiency for the 20 major Chinese banks.

A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.