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Using data envelopment analysis, we examine the efficiency of Canada's universal health care system by considering a set of labour (physicians) and capital (beds) inputs, which produce a level of care (measured in terms of health quality and quantity) in a given region. Data from 2013–2015 were collected from the Canadian Institute for Health Information regarding inputs and from the Canadian Community Health Survey and Statistics Canada regarding our output variables, health utility (quality) and life expectancy (quantity). We posit that variation in efficiency scores across Canada is the result of regional heterogeneity regarding socioeconomic and demographic disparities. Regressing efficiency scores on such covariates suggests that regional unemployment and an older population are quite impactful and associated with less efficient health care production. Moreover, regional variation indicates the Atlantic provinces (Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick) are quite inefficient, have poorer economic prospects, and tend to have an older population than the rest of Canada. Oaxaca-Blinder decompositions suggest that the latter two factors explain about one-third of this efficiency gap. Based on our two-stage semi-parametric analysis, we recommend Canada adjust their transfer payments to reflect these disparities, thereby potentially reducing inequality in regional efficiency. [ABSTRACT FROM AUTHOR]

Business mergers and partnerships could create opportunities for the decision making units (DMUs) involved to collectively enhance their efficiency. Estimating potential merger gains for a set of given merging DMUs using data envelopment analysis (DEA) and inverse DEA have been discussed in the literature. This paper develops new inverse DEA models for partner selection in a merger. The developed models extend the literature by finding optimal sets of partners that would maximize merger gains among a group of potential merging partners. The results of this study are useful to business managers seeking to merge to improve competitiveness. Data from the top US commercial banks is used to show the applicability of the proposed DEA models in this study. [ABSTRACT FROM AUTHOR]

The article "Parametric Population Volatility: The Role of Educational Attainment" by Fazley K. Siddiq explores the impact of educational attainment on population volatility across different age groups. Population volatility, influenced by factors like migration and economic changes, can have significant effects on social and economic conditions at both national and subnational levels. The study uses a parametric function to assess subnational population volatility and finds that higher educational attainment among working-age populations correlates with greater population volatility, while seniors have no impact on volatility. The research highlights the importance of education in shaping population dynamics and calls for further exploration in this area. [Extracted from the article]

Cross-efficiency (CE) evaluation is an extension of the data envelopment analysis approach that allows decision making units (DMUs) to assess their peers by means of their own appreciation weights. As a result, each DMU is presented with a vector of CE scores, which need to undergo an aggregation operation to yield the ultimate ranking score. The aggregation is commonly carried out through an appropriate aggregation operator. In this paper, we propose an induced ordered weighted averaging (IOWA) operator with dual preference setting (2-IOWA) as a new aggregation device. The 2-IOWA aggregation novelty resides in its twined order inducing variables, which are defined by exploiting exclusively the appreciative properties of the CE matrix. The first-order inducing variable is the voting rank order that characterizes the preference voting system embedded within the CE matrix. The corresponding IOWA-level 1 aggregation produces a composite vote for each DMU by employing as arguments the individual votes assigned to it. The second-order inducing variable is represented by these composite votes, which are adopted to induce a common order on the rows of the CE matrix as a part of the IOWA-level 2 aggregation. The 2-IOWA aggregation process is conducted with OWA weights that are generated through different minimax disparity models by using different optimism level values in order to corroborate the influence of subjectivity on the structure of the ranking patterns besides evaluating the robustness of the proposed methodological framework. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a widely used mathematical programming approach for assessing the efficiency of decision-making units (DMUs) in various sectors. Inverse DEA is a post-DEA sensitivity analysis approach developed initially for solving resource allocation. The main objective of inverse DEA is to determine the optimal quantity of inputs and/or outputs for each DMU under input and/or output perturbation (s), which would allow them to reach a given efficiency target. Since the early 2000s, inverse DEA has been extended theoretically and applied successfully in different areas including banking, energy, education, sustainability and supply chain management. In recent years, research has demonstrated the potential of inverse DEA for solving novel inverse problems, such as estimating merger gains, minimizing production pollution, optimizing business partnerships and more. This paper provides a comprehensive survey of the latest theoretical and practical advancements in inverse DEA while also highlighting potential areas for future research and development in this field. One such area is exploring the use of heuristic algorithms and optimization techniques in conjunction with inverse DEA models to address issues of infeasibility and nonlinearity. Moreover, applying inverse DEA to new sectors such as healthcare, agriculture and environmental and climate change issues holds great promise for future research. Overall, this paper sets the stage for further advancements in this promising approach. [ABSTRACT FROM AUTHOR]

Inverse DEA models may also be improved by considering uncertainty in data, for example developing stochastic inverse DEA or fuzzy inverse DEA. Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by [4] and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. Current state of knowledge in inverse DEA Unlike the conventional DEA, inverse DEA seeks to estimate input and/or output targets for DMUs to achieve efficiency targets. The introduction of inverse DEA created a new direction in the literature of DEA and provided great opportunities to solve novel inverse optimization problems that were beyond the scope of the standard DEA. [Extracted from the article]

Inverse data envelopment analysis (DEA) determines the optimal level of inputs and/or outputs of decision-making units (DMUs) to reach efficiency targets. This paper presents a new inverse DEA model for determining minimum inputs for working capital management. The proposed model is employed in the Indian textile industry to calculate working capital efficiency. Given the working capital efficiency, the decision maker's preferences will be estimating the change in inputs when outputs increase. Furthermore, unlike the standard inverse DEA model, this article discusses the inverse DEA model when negative ratio data exist. The DEA model requires additional attention when ratio data are present; therefore, a novel inverse DEA ratio model is proposed. The input targets obtained from the proposed model are less than the standard inverse DEA model. Also, the proposed model is a closer estimate of the production probability set for ratio data. [ABSTRACT FROM AUTHOR]

Cloud computing is utilised for information technology outsourcing of either industries or organisations. There are several inhibitors and motivations related factors to determine whether one can embrace the cloud services or not. Therefore, this paper presents a holistic and flexible cloud decision framework by taking a wide spectrum weighted factors related to the acceptance or denial of the cloud services. To have sustainable decision and obviating the shortcomings of the existing approaches on the cloud service adoption, a deep understanding of organisation's business process requirements and cost implications is required. To utilise the proposed model, the functional and non‐functional requirements associated to the business process of an adopter organisation must be specified. To reach a concrete decision, a hybrid approach is applied by incorporating the analytic hierarchy process and Delphi methods to prevent subjective outcomes and to have diverse experiences at the same time. To support the decision model, some economic theories and Moore law are used. To verify the proposed model, a Telecommunication Company is considered as a case study for its 6‐year plan of investment. The simulation results of conducted scenarios for the mentioned mid‐scale case study prove that it is logical to establish on‐premises a private datacenter and utilising the hybrid deployment once it encounters abrupt burst of resource demand. Altogether, the proposed holistic model can be customised for different users with different scales. [ABSTRACT FROM AUTHOR]

This study examines the data envelopment analysis (DEA) model for audit risk evaluation which was initially developed by Bradbury and Rouse (2002) and reinterpreted by Davutyan and Kavut (2005). Bradbury and Rouse (2002) apply the standard DEA model for audit risk factors, including judgemental (ordinal) measures. In the presence of ordinal data, efficiency analysis in DEA requires appropriate models to be applied instead of the standard DEA model. Accordingly, audit risk evaluation based on the standard DEA model is not assessed appropriately because the risk factors are qualitative and ordinal measures. Hence, we employ an appropriate DEA model to accurately evaluate audit risk in the presence of ordinal data. In light of the prior two studies, our results demonstrate the appropriateness of the ordinal DEA model. [ABSTRACT FROM AUTHOR]

Mergers and acquisitions are mainly due to financial and technological innovations but could also be due to changes in the structure of the economy, which alters the optimal production functions of banks. Banks that seek to be operationally efficient would focus more on expanding their asset size, in the face of bad loans, leading to the acquisition of less efficient banks. This paper develops two‐stage inverse data envelopment analysis (DEA) models for estimating potential gains from bank mergers for the top US commercial banks. The results show additional intermediate and final outputs at different predefined target levels of technical efficiencies. [ABSTRACT FROM AUTHOR]

Estimating potential gains from mergers is an important strategic decision-making problem. This paper introduces a new inverse data envelopment analysis (DEA) based on a cost efficiency model for estimating potential gains from mergers. There are restructuring scenarios for firms that want to minimize cost. The existing inverse DEA technical efficiency models are not appropriate for estimating merger gains in these situations. It is also shown that the proposed inverse DEA cost efficiency model can reveal more merger gains than the inverse DEA technical efficiency model. The applicability of the proposed method is shown through an application in Canada's banking sector to determine the required level of inputs and outputs for a merged bank to achieve target levels of cost and technical efficiencies. The results highlight the potential financial gains to improving both technical and cost efficiencies as efficiency-seeking banks increasingly become large and complex institutions through growth, mergers and acquisitions in a financial environment that is being shaped by reforms and technological innovation. [ABSTRACT FROM AUTHOR]

This paper introduces a compact form for the maximum value of the non-Archimedean in Data Envelopment Analysis (DEA) models applied for the technology selection, without the need to solve a linear programming (LP). Using this method the computational performance the common weight multi-criteria decision-making (MCDM) DEA model proposed by Karsak and Ahiska (International Journal of Production Research, 2005, 43(8), 1537-1554) is improved. This improvement is significant when computational issues and complexity analysis are a concern. [ABSTRACT FROM AUTHOR]

This paper presents an Improved MCDM Data Envelopment Analysis (DEA) model in order to evaluate the best efficient DMUs in Advanced Manufacturing Technology (AMT). This model is capable of ranking the next most efficient DMUs after removing the previous best one. [ABSTRACT FROM AUTHOR]

Prosperity is one of the key economic indicators of a nation's success. The measure of a country's true prosperity is best achieved by considering a set of criteria and identifying the optimal weights associated with each criterion. This study introduces a novel method for measuring global prosperity by employing a combination of variables that characterize economic wealth and social wellbeing using data envelopment analysis (DEA) and ordered weighted averaging (OWA) operator. It extends the existing global prosperity assessment approach proposed by the Legatum Institute, an international organization that produces a global prosperity index every year. The current Legatum Prosperity Index is obtained by averaging a set of distinct variables, but it fails to identify the optimal variable weights for each country. This is a significant drawback that we address in this study. Using DEA, each country can freely assign optimal weights that are most favorable to achieving maximum prosperity. It provides a flexible and competitive environment in which all countries can present their strengths, thereby creating a level playing field. This study also uses multilevel DEA efficiency frontiers for classifying countries into different groups based on their levels of prosperity score. Additionally, we apply the OWA operator to distinguish further between the countries within each cluster. [ABSTRACT FROM AUTHOR]

This paper aims to address the problem of allocating the CO2 emissions quota set by government goal in Chinese manufacturing industries to different Chinese regions. The CO2 emission reduction is conducted in a three-stage phases. The first stage is to obtain the total amount CO2 emission reduction from the Chinese government goal as our total CO2 emission quota to reduce. The second stage is to allocate the reduction quota to different two-digit level manufacturing industries in China. The third stage is to further allocate the reduction quota for each industry into different provinces. A new inverse data envelopment analysis (InvDEA) model is developed to achieve our goal to allocate CO2 emission quota under several assumptions. At last, we obtain the empirical results based on the real data from Chinese manufacturing industries. [ABSTRACT FROM AUTHOR]

This paper discusses the impact of ganging decision making units (DMUs) on the cross-efficiency evaluation in data envelopment analysis (DEA). A group of DMUs are said to be ganging-together if the minimum and the maximum cross-efficiency scores they give to all other DMUs are identical. This study demonstrates that the ganging phenomenon can significantly influence the cross-efficiency evaluation in favour of some DMUs. To overcome this shortcoming, we propose a gangless cross-efficiency evaluation approach. The suggested method reduces the effect of ganging and generates a more diversified list of top performing units. An application to the Tehran stock market is used to show the benefits of gangless cross-evaluation. [ABSTRACT FROM AUTHOR]

One of the shortcomings in the standard data envelopment analysis (DEA) self-evaluation models is the flexibility of choosing favorable DEA weights on inputs and outputs. This study uses the potential of DEA cross-efficiency evaluation and proposes a new mean-variance goal programming model for minimizing the risk of changing DEA weights for identification of high performed decision making units. The applicability of the proposed method in this paper is demonstrated through an application in Oman fishery, to address peer-judgment risk in fisheries. The suggested model also provides a list of fishers with maximum cross-efficiency scores. [ABSTRACT FROM AUTHOR]

This paper gives a modification on the improved technology selection DEA model proposed by Amin et al. (Amin, G.R., Toloo, M. and Sohrabi, B., An improved MCDM DEA model for technology selection. Int. J. Prod. Res., 2006, 44, 2681-2686). The paper shows the problem of using the existing model and then introduces a new modified one to obtain a single efficient DMU for the technology selection alternatives. [ABSTRACT FROM AUTHOR]

Abstract: The main objective of stock selection is to select a set of assets in the stock market with high‐expected returns. There are many financial variables that affect the performance of stock firms. This paper proposes a novel linear programming model based on the ordered weighted averaging (OWA) operator for identifying superior stocks without requiring the re‐ordering process. The paper first converts a stock selection problem into a preference voting system by considering two different perspectives: an investor perspective in which the goal is to select stocks with the highest return, and a creditor perspective in which the goal is to maximize the repayment ability. The OWA operator is then used to formulate a linear programming model for identifying superior stocks. The usefulness of the proposed method in this paper is shown through an application in the Tehran stock market. [ABSTRACT FROM AUTHOR]

One of the important issues in a merger between two or more decision making units is identification of the levels of inherited inputs and outputs from merging units. This paper introduces new inverse data envelopment analysis models for target setting of a merger in the presence of negative data. It enables the merged entity to identify the levels of required inputs and outputs from merging units to realize an efficiency target. The applicability of the proposed method in this paper is illustrated through an example from banking; however, it can be used in other merger contexts. [ABSTRACT FROM AUTHOR]

One of the main objectives of fund managers in financial service industry is to select superior stocks by analyzing financial ratios. This paper proposes a novel methodology for stock selection by integrating optimistic and pessimistic ordered weighted averaging (OWA) and data envelopment analysis (DEA) methods. The paper first reveals the drawback of using the standard DEA models for stocks evaluation and then proposes a new method by using the OWA operator. Unlike the classical DEA, the proposed method in this paper does not involve the specification of inputs and outputs. The paper incorporates optimistic and pessimistic scenarios and generates interval OWA scores for all stocks. This is followed by using appropriate interval DEA models for selecting superior stocks. The proposed method in this paper is applied to identify high financial performance stocks in the Tehran stock market. [ABSTRACT FROM AUTHOR]

This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

The article discusses the study on choosing the best cricket batsmen using an ordered weighted averaging. The study states using an ordered weighted averaging (OWA) evaluation method for a cricket batsman using multiple attributes related to the performance of a player. The study also mentions the advantage of using proposed OWA method.

For a specific query merging the returned results from multiple search engines, in the form of a metasearch aggregation, can provide significant improvement in the quality of relevant documents. This paper suggests a minimax linear programming (LP) formulation for fusion of multiple search engines results. The paper proposes a weighting method to include the importance weights of the underlying search engines. This is a two-phase approach which in the first phase a new method for computing the importance weights of the search engines is introduced and in the second stage a minimax LP model for finding relevant search engines results is formulated. To evaluate the retrieval effectiveness of the suggested method, the 50 queries of the 2002 TREC Web track were utilized and submitted to three popular Web search engines called Ask, Bing and Google. The returned results were aggregated using two exiting approaches, three high-performance commercial Web metasearch engines and our proposed technique. The efficiency of the generated lists was measured using TREC-Style Average Precision (TSAP). The new findings demonstrate that the suggested model improved the quality of merging considerably. [ABSTRACT FROM PUBLISHER]

This paper clarifies, how a preference aggregation problem can be solved using the prioritized aggregation operators (PAO), where, a given group of candidates should be aggregated based upon their satisfaction for a collection of ranked places. The comparison results show that the proposed method can accurate aggregate a preference ranking system with less computation and without the need to solve any mathematical model. Also, a metasearch application shows the usefulness of applying the PAO for a preference aggregation. © 2010 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This paper introduces a computational efficient form to find the optimal solution of technology selection model. The paper suggests an efficient and simple method to obtain the optimal solution of linear programming (LP) problem used in technology selection without the need for solving any LP. Clearly, the contribution of this study is that it improves further the computational complexity of the literature, which is significant when dealing with complexity. [ABSTRACT FROM AUTHOR]

This paper clarifies the role of non-Archimedean infinitesimal ε in DEA models so that the associated linear programs may be infeasible (for the multiplier side) and unbounded (for the envelopment side) for certain values of ε . It is shown that the bound of ε proposed by Ali and Seiford (1993) is invalid for feasibility and boundedness of the linear programs. A procedure is presented for determining an assurance interval of ε. It is also shown that an assurance value for ε can be found using a single linear program. [ABSTRACT FROM AUTHOR]