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Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs' weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach. [ABSTRACT FROM AUTHOR]

We present a cohesive generalized framework for an aggregation of the Nerlovian profit indicators and of the directional distance functions, frequently used in productivity and efficiency analysis in operations research and econometrics (e.g., via data envelopment analysis or stochastic frontier analysis). Our theoretical framework allows for greater flexibility than previous approaches, and embraces many other approaches as special cases. In the proposed aggregation scheme, the aggregation weights are mathematically derived from assumptions made about the optimization behavior and about the chosen directions of measurement. We also discuss various interesting special cases of popular directions, including the case of Debreu-Farrell-type efficiency. [ABSTRACT FROM AUTHOR]

Because of hyper-complexity, a difficulty to define, multiple stakeholders with conflicting perspectives, and a lack of clear-cut solutions, wicked problems necessitate innovative and adaptive strategies. Operations research (OR) has been a valuable tool for managers to make informed decisions for years. However, as we face increasingly complex and messy problems, it has become apparent that relying solely on either hard or soft OR approaches is no longer sufficient. We need to explore more innovative methodologies to address these wicked problems effectively. This study has bridged the research gap by proposing a structured process encompassing a subdivision-based problem structuring method for defining the wicked problem, a multi-attribute decision-making (MADM) for prioritizing subproblems, and a hard OR technique, data envelopment analysis (DEA) for tackling one of the most critical subdivisions. The proposed methodology, the subdivision-based problem structuring method (SPSM), implemented in a case study, focuses on a higher education institution experiencing a decline in student admissions and involves five steps. First, a diverse group of stakeholders is formed to ensure the comprehensive consideration of perspectives. Second, the wicked problem is defined, considering long-term consequences, multiple stakeholders, and qualitative stakeholder opinions. Third, a hierarchical structure is created to break down the wicked problem into manageable subproblems. Fourth, a multi-criteria decision-making (MCDM) method prioritizes subproblems. Finally, the subproblems are addressed one by one using a combination of soft and hard OR tools. The findings highlight the benefits of integrating hard and soft OR approaches. The study concludes with reflections on the implications of using a combined OR approach to tackle wicked problems in higher education and beyond. [ABSTRACT FROM AUTHOR]

Food banks have played a crucial role in mitigating food insecurity in affluent countries for over four decades. Throughout the years, academics have researched food banks for a variety of operational problems, resulting in several research papers on the topic. However, despite significant academic interest, the operational challenges and optimization of food bank operations remain under-researched. This study aims to conduct a systematic literature review on food bank operations and provide evidence-based recommendations for addressing prevalent challenges, and provide decision-makers with practical recommendations. In addition, this investigation seeks to investigate the impact of the COVID-19 pandemic on food bank operations. We conducted a comprehensive analysis of academic publications on food bank operations using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in order to get a deeper comprehension of the problems confronting food bank operations. Using a keyword search strategy with the logical operators "AND" and "OR," two search methods were utilized to identify relevant articles on food bank operations management, supply chain, distribution, and production in our first search. In our second search, we discovered articles in the "Operations Research & Management Science" (OR &MS) category of Web of Science containing food bank-related keywords such as food charity, food donation, and food aid. The database searches yielded 246 hits, and the article content was scanned to eliminate irrelevant articles by removing non-English articles and duplicated studies, leaving 55 articles for further examination. Our extensive examination of Operations Research (OR) methodologies reveals that Mixed-Integer Linear Programming (MILP) models are the most commonly used methodology, followed by Linear Program (LP), Dynamic Program (DP), and Data Envelopment Analysis (DEA) techniques. The key findings of this study emphasize the operational challenges food banks encountered during and after the COVID-19 pandemic, including supply chain disruptions, increased demand, and volunteer shortages. To address these issues, effective solutions, including the management of food donations and volunteer scheduling, were proposed. Our findings have practical implications for decision-makers in food bank management, highlighting the importance of adopting evidence-based solutions. Finally, Limitations and prospective research directions in food bank management are discussed, with an emphasis on the need for ongoing research in this crucial area. [ABSTRACT FROM AUTHOR]

The artificial bee colony algorithm is one of the population-based optimization methods inspired by the evolutionary principles of the social behavior of bees. On the other hand, one of the sub-fields of operations research science is data envelopment analysis. There are some difficulties in DEA models for selecting the appropriate numerical value for an infinitesimal non-Archimedean epsilon. So far, various methods have been proposed to solve this problem and choose the suitable non-Archimedean epsilon. In order to solve the problem, the artificial bee colony algorithm (ABC), and modification of the original ABC algorithm (MABC) are adopted and proposed in this paper. The impacts of our proposed algorithms on the suitable non-Archimedean epsilon by solving only one linear programming (LP), instead of n LP are investigated. Finally, the performance of the proposed algorithms is evaluated by comparing the solutions obtained from GAMS software based on the presented examples. [ABSTRACT FROM AUTHOR]

This paper surveys the increasing use of statistical approaches in non-parametric efficiency studies. Data Envelopment Analysis (DEA) and Free Disposable Hull (FDH) are recognized as standard non-parametric methods developed in the field of operations research. Kneip et al. (Econom Theory, 14:783–793, 1998) and Park et al. (Econom Theory, 16:855–877, 2000) develop statistical properties of the variable returns-to-scale (VRS) version of DEA estimators and FDH estimators, respectively. Simar & Wilson (Manag Sci 44, 49–61, 1998) show that conventional bootstrap methods cannot provide valid inference in the context of DEA or FDH estimators and introduce a smoothed bootstrap for use with DEA or FDH efficiency estimators. By doing so, they address the main drawback of non-parametric models as being deterministic and without a statistical interpretation. Since then, many articles have applied this innovative approach to examine efficiency and productivity in various fields while providing confidence interval estimates to gauge uncertainty. Despite this increasing research attention and significant theoretical and methodological developments in its first two decades, a specific and comprehensive bibliometric analysis of bootstrap DEA/FDH literature and subsequent statistical approaches is still missing. This paper thus, aims to provide an extensive overview of the key articles and their impact in the field. Specifically, in addition to some summary statistics such as citations, the most influential academic journals and authorship network analysis, we review the methodological developments as well as the pertinent software applications. [ABSTRACT FROM AUTHOR]

Wang, and J.-T. Teng in their paper develop and compare the seller's profit per unit time under each of three payment methods - advance, cash, and credit. In their paper, S. Sarkar, S. Tiwari, and B. C. Giri present a continuous-review vendor-buyer supply chain (SC) model and uncovers the best policy that minimizes the system's total expected cost. The authors investigate the contracts that the manufacturer offers to the retailers and finds the optimal pricing and ordering decisions made by retailers and the best contract that includes maximum profit for the supply chain. The analysis of large-scale data generated by humans, online activity in different web and social platforms is a challenging problem faced by researchers, practitioners, and academicians. [Extracted from the article]

Data envelopment analysis (DEA) is one the most successful techniques in the field of Operations Research. DEA is a non-parametric and objective approach for evaluating the relative efficiency of a set of decision-making units. The original DEA proposal contemplated the total freedom of variation of weights. This free variation may lead to situations with non-realistic weights and to the impossibility of incorporating the judgments of decision-makers. This work studies the links between multicriteria decision analysis (MCDA) and DEA by introducing weight restrictions in a DEA model using a methodology developed to obtain criteria weights in a MCDA context: the so-called Revised Simos' Procedure. The presented approach is suitable to be applied in the field of economics and management, being an intuitive and simple enough method for decision-makers who are not familiar with working with DEA models or multicriteria decision analysis. A classic example is presented, where the results found with this approach are compared with the results of other approaches which also use multicriteria decision analysis as a tool to obtain weight restrictions for a DEA model. [ABSTRACT FROM AUTHOR]

Due to the challenges of implementing environmental responsibility, sustainable development is often criticized for its promise to "square the circle". This paper incorporates sociological theories into operational research to enhance our understanding of the issue. It proposes a framework for evaluating eco-efficiency that captures the decision-making process from a macro perspective rather than focusing on a single technique or employing a multimethodology. By utilizing 272 Chinese cities as case studies, the evaluation process is effectively implemented. From three perspectives - region, city size, and policy - we draw some interesting conclusions. Finally, a vision for future research is presented in the concluding section. • This study incorporates sociological theories into operational research to enhance the understanding of eco-efficiency evaluations. • By utilizing 272 Chinese cities as case studies, we implement a comprehensive and effective evaluation process. • We propose a visionary framework and four potential directions for further exploration. [ABSTRACT FROM AUTHOR]

Big Data brings about a mix of opportunities for and challenges to the development of intelligent transportation and logistics industries. Incorporating the tremendous trove of data and data analytics into operations research greatly increases capabilities to solve real-world complex problems. "DEA under big data: data enabled analytics and network data envelopment analysis" by Zhu, proposes that data envelopment analysis (DEA) should be viewed as a method for data-oriented analytics in performance evaluation and benchmarking, and demonstrate that network DEA can be different from conventional DEA in many aspects. [Extracted from the article]

Identification of various sources of inefficiency plays an important role in the performance analysis aimed at developing plans for the improvement of decision making. In this regard, not only technical, cost, and allocative efficiency can be estimated by information on inputs and outputs and their prices, but losses due to the lack of profit and revenue and optimal cost can also be calculated based on the relevant inefficiency. The present paper aimed at providing new estimation of cost efficiency and sources of losses in the total efficiency in a non-competitive environment where there is the possibility of change in prices of inputs and outputs from one DMU to another. In line with studies (K. Tone, A Strange Case of the Cost and Allocative Efficiencies in DEA, Journal of the Operational Research Society 53 (2002) 1225-1231) and (K. Tone et al., Ecomposition of Cost Efficiency and its Application to Japanese-Us Electric Utility Comparisons, Socio-Economic Planning Sciences 47 (2007) 91-106), the present study sought to introduce new sources of inefficiency and related losses by presenting new price-based and cost-based production possibility set. [ABSTRACT FROM AUTHOR]

Chen et al. Omega 41:621–625, 2013 propose a DEA model which measures the super-efficiency with directional distance function. Their model avoids the infeasibility when the inputs have zero and it also prevents the negative projection. However, their model might be infeasible when there is zero in the outputs. Besides, their model relies on complex predetermined parameters. Recently, Lin and Chen (Lin and Chen Journal of the Operational Research Society 66:1506–1510, 2015) propose a new model which prevents infeasibility and negative projection. However, Lin and Chen's method introduces a constant which might dominate the input of the target DMU. To overcome the drawbacks of the models proposed in Chen et al. Omega 41:621–625, 2013 and Lin and Chen Journal of the Operational Research Society 66:1506–1510, 2015, we will develop a new DEA model to compute the super-efficiency, which prevents negative projection and the infeasibility when zero data occurs and works in the same way as the N-L model (Ray, Ray Journal of the Operational Research Society 59:788–797, 2008) when no abnormal input occurs. [ABSTRACT FROM AUTHOR]

In this work, we illustrate the application of a wide variety of Operations/operational research (OR) tools in higher education through three case studies based on practical applications conducted in the Engineering School of the Universidad de Antioquia. These case studies focus, respectively, on capacity planning, resource allocation, and performance measurement. In the first case study, we model and predict the flow of students enrolled in the industrial engineering program through the new curriculum using system dynamics and algebraic modeling of dropout rates, finding the number of sections for the courses, and the corresponding number of faculty positions needed to support the program structure. The second case study is a course covering model for the new curriculum that considers preferences and capacities of the teaching staff in an integer programming model, to find the uncovered courses of the new curriculum. Finally, the third case study presents a data envelopment analysis tool currently used to evaluate and rank the faculty of the Engineering School according to their teaching performance. The case studies presented in this work showcase how helpful OR tools are to rationalize the decision-making process in higher education institutions. [ABSTRACT FROM AUTHOR]

The research on operational efficiency focuses on the macro-level research. However, there are relatively few studies on the industry level. In particular, there are fewer studies on the logistics industry, which has a leading and fundamental significance in the national economic system and is regarded as the third important source of profit. Moreover, scholars are more focused on the research on the operational performance and profitability of logistics enterprises. In order to study the efficiency of the logistics industry, this paper uses machine learning technology as the foundation and self-service data envelopment analysis to construct a comprehensive efficiency analysis model for the logistics industry. Moreover, this paper adopts a combination of qualitative and quantitative analysis to conduct empirical research on the operational efficiency and influencing factors of the logistics industry to explore the factors that affect the operational efficiency of logistics enterprises. In addition, this article optimizes the model data through statistics, and compares the model analysis data with the actual situation. It can be seen from the research results that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

We undertake a systematic comparison of existing models measuring and decomposing the economic efficiency of organizations. For this purpose we introduce the package BenchmarkingEconomicEfficiency.jl for the open-source Julia language including a set of functions to be used by scholars and professionals working in the fields of economics, management science, engineering, and operations research. Using mathematical programming methods known as Data Envelopment Analysis, the software develops code to decompose economic efficiency considering alternative definitions: profit, profitability, cost and revenue. Economic efficiency can be decomposed, multiplicative or additively, into a technical (productive) efficiency term and a residual term representing allocative (or price) efficiency. We include traditional decompositions like the radial efficiency measures associated with the input (cost) and output (revenue) approaches, as well as new ones corresponding to the Russell measures, the directional distance function, DDF (including novel extensions like the reverse DDF, modified DDF, or generalizations based on Hölder norms), the generalized distance function, and additive measures like the slack based measure, their weighted variants, etc. Moreover, regardless the underlying economic efficiency model, many of these technical inefficiency measures are available for calculation in a computer software for the first time. This article details the theoretical methods and the empirical implementation of the functions, comparing the obtained results using a common dataset on Taiwanese Banks. • We present an open-source Julia package for measuring economic efficiency. • Economic efficiency is decomposed into technical and allocative efficiencies. • Each economic efficiency model is decomposed with different technical measures. • We consider Farrell's measures, Russell, weighted additive, directional functions, etc. • We discuss the pros and cons of each decompositions based on desirable properties. [ABSTRACT FROM AUTHOR]

The study considers the semiconductor industry's business process to be made up of two stages. In the business development process, a company generates profit and consumes energy while polluting the environment. After the two-stage data envelopment analysis approach was employed for calculating the operational efficiency and environmental efficiency, social network analysis was used to compare the manner in which the internal advantages or individual process factors of 28 semiconductor companies contribute to efficiency. A network graph was plotted to visualize relationships, with each node in the network graph representing a company. This graph was plotted to help decision-makers and manufacturers understand information communication among companies and the importance of the company in the network and help companies develop a mutual understanding to improve operational efficiency. The results of the study indicated that having an efficient company does not necessarily mean that the company plays a key role in the entire industry. The results provide decision-makers with references for improvements and information for learning from these references. [ABSTRACT FROM AUTHOR]

The study considers the semiconductor industry's business process to be made up of two stages. In the business development process, a company generates profit and consumes energy while polluting the environment. After the two-stage data envelopment analysis approach was employed for calculating the operational efficiency and environmental efficiency, social network analysis was used to compare the manner in which the internal advantages or individual process factors of 28 semiconductor companies contribute to efficiency. A network graph was plotted to visualize relationships, with each node in the network graph representing a company. This graph was plotted to help decision-makers and manufacturers understand information communication among companies and the importance of the company in the network and help companies develop a mutual understanding to improve operational efficiency. The results of the study indicated that having an efficient company does not necessarily mean that the company plays a key role in the entire industry. The results provide decision-makers with references for improvements and information for learning from these references. [ABSTRACT FROM AUTHOR]

2018 saw the 40th anniversary of the publication of the seminal paper on Data Envelopment Analysis (DEA) (Charnes et al. [1]). The participants included, apart from academics researching in the field of DEA, non-academic practitioners using DEA in the course of their employment. Indeed, the conference featured a stream dedicated to non-academic practitioners from the field of Regulation, a major area of use of DEA. [Extracted from the article]

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention. [ABSTRACT FROM AUTHOR]

With the development of air transport, the capacity of airports increasing in use day by day is insufficient. Therefore, it has been thought that airports should be used effectively and efficiently. A number of studies have been conducted by airport managers and competent authorities to improve effectively and efficiently in airports. Many performance measurement methods are used in these studies. Data Envelopment Analysis (DEA), where many input and output variables are used, is one of the most widely used performance measurement methods in airports. In the first part of this study, the importance of performance measurement at airports is mentioned. In the second part, the previous studies with the DEA in the airports were researched. In the third part, information about the DEA was given. In the last part, operational efficiency measurement of 20 airports which can be accessed within the first 25 airports of the world in terms of number of passengers was done with DEA. In the analysis phase, input variables such as runway number, aircraft number, gate number and terminal area size are used. Total number of flights, total freight and total number of passengers were used as output variables. In the conclusion section, suggestions for ineffective airports are presented. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic required managerial and structural changes inside hospitals to address new admission demands, frequently reducing their care capacity for other diseases. In this regard, this study aims to support the recovery of hospital productivity in the post-pandemic context. The major challenge will be to make use of all the resources the institution has obtained (equipment, beds, temporarily hired human resources) and to increase production to meet the existing repressed demand. To support evidence-based decision-making at a major university hospital in Rio de Janeiro, hospital managers and operations research analysts designed an approach based on multiple methodologies. Besides multimethodology, one important novelty of this study is the application of a productivity frontier function to future scenario planning through the quantitative DEA methodology. Concept maps were used to structure the problem and emphasize stakeholders' perspectives. In sequence, data envelopment analysis (DEA) was applied, as it combines benchmarking best practices and assigns weights to inputs and outputs. To guarantee that the efficiency measurement considers all inputs and outputs before any inclusion of expert judgment, the scope was redirected to full dimensional efficient facet, if any, or to maximum efficient faces. The results indicate that production scenarios proposed by stakeholders based on the Ministry of Health parameters overestimate the viable production framework and that the scenario that maintains temporary human resource contracts is more compatible with quality in health provision, teaching, and research. These findings will serve as a basis for decision-making by the governmental agency that provided temporary contracts. The present methodology can be applied in different settings and scales. • Coronavirus pandemic demanded hospital structural and human resource changes. • Planning post-pandemic hospital production requires multimethodological approach. • Concept maps facilitate integration between hospital stakeholders and OR analysts. • DEA and GP adjust scenarios towards a feasible and efficient production framework. • Full Dimension Efficient Faces consider positive weights for all model's variables. [ABSTRACT FROM AUTHOR]

Setting realistic but challenging sustainability targets is critical for motivating business plan changes and guiding corporations toward meaningful contributions to sustainable development and the 2030 Agenda for Sustainable Development Goals (SDGs). However, aside from the conceptual barriers to sustainability, as well as its universalization and materialization, which make defining sustainability targets difficult, there are few tools for setting such targets, and those that do exist are usually theme-specific. Therefore, this paper proposes a methodology for setting corporate sustainability targets that is free of thematic constraints and based on the integration of Operational Research methods Data Envelopment Analysis, cluster analysis, time series forecasting, and Goal Programming. An application for defining energy and water consumption targets related to SDGs 6, 7, 8, and 12 is presented for a Brazilian electricity company and its nine business units. As a result, more challenging-yet-achievable targets were set for the company and its business units based on data, benchmarking, and the assessment of its decision-makers. [ABSTRACT FROM AUTHOR]

• Benchmarking with nonconvex production possibility set (PPS) is emphasized. • Correlation between nonconvex PPS and convex PPS is presented. • The closest benchmark with least effort is obtained with nonconvex PPS. • A new efficiency measure is introduced and a benchmark index is conceptualized. • An empirical application to China's transportation system is investigated. Convex production possibility set (PPS) is an implicit assumption in previous benchmarking studies. Some scholars point out that the implicit assumption is inconsistent with the classical economic theory although it facilitates operational analysis. Hence, the present study develops a new benchmarking approach on the basis of technology that assumes convexity on consumption correspondence instead of imposing convexity on PPS, in which the closest benchmark with the least effort is identified. First, the correlation between two classical technologies in benchmarking (convex PPS and free disposal hull technology), and two nonconvex PPS (production correspondence and consumption correspondence), are identified. Next, the extreme efficient DMUs are identified to describe Pareto-efficient points on the frontier of the consumption correspondence. Then, the closest benchmark is determined by selecting a Pareto-efficient point that minimizes the distance between the evaluated unit and the efficient frontier of consumption correspondence. In addition, this study employs the Manhattan distance for benchmarking, and the proposed approach is flexible to accommodate other distances to derive different economic interpretations. Besides, the proposed approach introduces a new non-radial efficiency measure and conceptualizes a benchmark index by relaxing the convexity assumption on PPS. Finally, the proposed approach is applied to select benchmarks and measure performance for China's transportation system. The proposed approach contributes to relaxing convexity assumption on PPS in benchmarking using DEA and provides a powerful managerial instrument to improve the performance of China's transportation system. [ABSTRACT FROM AUTHOR]

Turkey’s airport industry has experienced substantial growth over the recent years, but few studies have analysed their operational efficiency. This paper uses Malmquist productivity index (classical and bootstrapping) to assess the operational performance of 21 Turkey airports during the period of 2009 through 2014. The findings indicated that the efficiency and productivity of the majority of the Turkish airports increased during the period under investigation. However, in the period of 2011–2012, a significant decline was observed in efficiency. The main reason of this stagnation is the significant increase in the physical capacity of the Turkish airports in 2011. The non-reflection of the increasing physical capacity to passenger and cargo traffic caused a decline in 2012. In spite of declining in the period of 2011–2012, efficiency values of Turkish airports have increased again since 2013. Moreover, decomposition of the Malmquist index showed that most Turkey airports experienced losses in efficiency; however, in terms of technology, they have progressed. Two significant factors (i.e. operating hours and percentage of international traffic) were identified by the Simar-Wilson double bootstrapping regression analysis as explaining variations in airport efficiency. [ABSTRACT FROM AUTHOR]

Efficiency evaluation is vital in determining whether a production line can achieve its goals or not. The present study aims at assessing the efficiency of five blowing machines in a plastics industry using DEA window analysis in both day and night shifts for the period Feb., 2014 to Jun., 2014. The production quantity is set as the output, whereas the defect quantity and idle time (units) are set as the inputs for all windows. Utilizing the DEA models, the technical, pure technical and scale efficiency values are then calculated in both day and night shifts for each machine. A comparison is conducted between the day and night shifts for each machine. Moreover, comparisons are performed among the efficiency of five machines in both day and night shifts. Improvement actions are then suggested to reduce the inefficiency. Results showed that significant differences exist between day and night shifts for each machine. Directions for improvement are then suggested, which requires managerial as well as operational actions to improve process performance. In conclusions, the thorough analysis and discussion of the results in the present paper provide a valuable evaluation to production managers for improving the performance of blowing processes. [ABSTRACT FROM AUTHOR]

This research analyses the performance of Portuguese metros in the European context. By means of two non-parametric benchmarking techniques, respectively performance indicators and data envelopment analysis, the efficiency of 37 European metros is computed. In order to provide statistical inference and robustness to the results, the recent technique of bootstrapping is applied. Also the partial frontiers (order-m) is used to identify outliers and the double-bootstrap procedure in a second stage methodology to take into account the influence of the operational environment. The results show important levels of inefficiency both in Portuguese metros and in other European metros. [ABSTRACT FROM AUTHOR]

Agricultural crop and livestock production systems are complex, essential to human well-being, and fraught with sustainability challenges. In light of intrinsic variability in agricultural production systems and the high number of decision variables, decision support for optimization of sustainability outcomes should be supported by rigorous operations research. Several operations research (OR) methods such as evolutionary algorithms, multi-objective optimization, and data envelopment analysis (DEA) have been applied to optimization in agricultural contexts, taking into account different objective functions and decision variables, and life cycle-based evaluation of environmental outcomes in agriculture have become widespread. The current review evaluates the methods used for optimization of agricultural and livestock systems for life cycle-based environmental sustainability goals. A Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) systematic review method and three complementary search strategies were used to identify relevant articles. Strengths, weaknesses, and performance issues for each method are considered and compared. Farm benchmarking, output prediction and resource use management are the three most commonly considered decision types in crop-livestock production systems. To guide selection and implementation of appropriate OR methods, a framework (decision tree) is proposed. The proposed decision tree provides an indication of necessary method-specific methodological choices. Methodological choices with respect to each method are discussed. [Display omitted] • Intrinsic variability in agricultural production systems and the high number of decision variables call for a rigorous DSS. • OR methods used for optimization of agricultural and livestock systems for environmental goals were reviewed. • Three most commonly considered decision types were farm benchmarking, output prediction and resource use management. • A framework (decision tree) considering methodological choices with respect to each method was proposed. • It helps farmers to make informed decisions based on their tresources, and environmental impacts of their production system. [ABSTRACT FROM AUTHOR]

Abstract: DEAHP as a weight derivation procedure for analytic hierarchy process (AHP) has been found suffering from some significant drawbacks. Recently, Mirhedayatian and Saen (2011) proposed a new procedure entitled Revised DEAHP for AHP weight derivation [S.M. Mirhedayatian, R.F. Saen, A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process, Journal of the Operational Research Society 62 (2011) 1585–1595]. This paper provides a detailed note to reveal that (1) the Revised DEAHP cannot derive true weights from perfectly consistent pairwise comparison matrices, (2) it may produce irrational weights for inconsistent pairwise comparison matrices, (3) it still suffers from rank reversal problem when an efficient decision criterion or alternative is added or removed, (4) the use of the super-efficiency model in data envelopment analysis (DEA) for AHP weight derivation is redundant and meaningless when there exist multiple decision criteria or alternatives that are efficient in a pairwise comparison matrix, and (5) it may produce a completely reversed ranking that is totally opposite to the rank obtained by the eigenvector method in the case of hierarchical structures, leading to a wrong decision being made. [Copyright &y& Elsevier]

The application of Data Envelopment Analysis (DEA) as an alternative multiple criteria decision making (MCDM) tool has been gaining more attentions in the literatures. Doyle (Organ. Behav. Hum. Decis. Process. 62(1):87-100, ) presents a method of multi-attribute choice based on an application of DEA. In the first part of his method, the straightforward DEA is considered as an idealized process of self-evaluation in which each alternative weighs the attributes in order to maximize its own score (or desirability) relative to the other alternatives. Then, in the second step, each alternative applies its own DEA-derived best weights to each of the other alternatives (i.e., cross-evaluation), then the average of the cross-evaluations that get placed on an alternative is taken as an index of its overall score. In some cases of multiple criteria decision making, direct or indirect competitions exist among the alternatives, while the factor of competition is usually ignored in most of MCDM settings. This paper proposes an approach to evaluate and rank alternatives in MCDM via an extension of DEA method, namely DEA game cross-efficiency model in Liang, Wu, Cook and Zhu (Oper. Res. 56(5):1278-1288, ), in which each alternative is viewed as a player who seeks to maximize its own score (or desirability), under the condition that the cross-evaluation scores of each of other alternatives does not deteriorate. The game cross-evaluation score is obtained when the alternative's own maximized scores are averaged. The obtained game cross-evaluation scores are unique and constitute a Nash equilibrium point. Therefore, the results and rankings based upon game cross-evaluation score analysis are more reliable and will benefit the decision makers. [ABSTRACT FROM AUTHOR]

Abstract: The theory of participatory action research (PAR) grew out of the practice of problem-solving in groups and organization by involving the participation of all pertinent stakeholders in decision-making process through empowerment. The ontological assumption is that “the world out there” is defined by the participating stakeholders as they understand and perceive it. The actionable knowledge thus produced is mostly used subjectively in the hope of a favorable organizational change or a transformation for betterment. On the other hand, Management Science/Operations Research (MS/OR) is more concerned with epistemological objectivity in identifying and defining managerial issues and finding solutions. In this paper we shall show that MS/OR can benefit from and contribute to PAR in complex decision making contexts. This kind of mutual benefit will be illustrated through a study reported in the area of R&D planning. It will be shown that PAR “contextualizes” problem structuring whereas MS/OR “optimizes” the consensual decision-making process in a multi-organizational context. [Copyright &y& Elsevier]

Abstract: In many applications of data envelopment analysis (DEA), there is often a fixed cost or input resource which should be imposed on all decision making units (DMUs). Cook and Zhu [W.D. Cook, J. Zhu, Allocation of shared costs among decision making units: A DEA approach, Computers and Operations Research 32 (2005) 2171–2178] propose a practical DEA approach for such allocation problems. In this paper, we prove that when some special constraints are added, Cook and Zhu’s approach probably has no feasible solution. The research of this paper focuses on two main aspects: to obtain a new fixed costs or resources allocation approach by improving Cook and Zhu’s approach, and to set fixed targets according to the amount of fixed resources shared by individual DMUs. When such special constraints are attached, our model is proved to be able to achieve a feasible costs or resources allocation. Numerical results for an example from the literature are presented to illustrate our approach. [Copyright &y& Elsevier]

Abstract: In this paper, we present a DEA-based method for allocating fixed resources or costs across a set of decision making units. In addition, we show how output targets can be set at the same time as decisions are made about allocating input resources. Numerical examples are presented to illustrate the application procedure of the proposed approach. [Copyright &y& Elsevier]

One class of models introduced in DEA is called multiplicative models, in which, as shown by Banker and Maindiratta (Manag. Sci. 32:126–135, ), the piecewise linear frontiers usually employed in DEA are replaced by a frontier that is piecewise Cobb-Douglas(=log linear). Banker and Maindiratta (Manag. Sci. 32:126–135, ) introduced a model to identify the most productive scale size pattern, and Banker et al. (Eur. J. Oper. Res. 154:345–362, ) presented a two-stage method for the identification of returns to scale (RTS) in multiplicative models. In this paper it is shown that both the RTS situation and the MPSS pattern could be determined by a single model in one step. The new method is important in the computational point of view. [ABSTRACT FROM AUTHOR]

This paper describes a method for the assessment of retail store performance based on Data Envelopment Analysis (DEA). The assessment considers the stores as complex organizations that aggregate several subunits, corresponding to sections with management autonomy. This structure motivated an analysis at two different levels: the section level and the store level. The performance assessment of the sections envolves a comparison among similar sections located in different stores, and evaluates efficiency spread. This is followed by an analysis at the store level to define targets for the sections. This analysis takes into account the interdependencies of the sections composing a store, as they share limited resources such as the floor area. This is achieved using a Network DEA model, which determines the maximum store sales allowing for reallocations of area among the sections within a store. The method developed is illustrated using a case study consisting of a Portuguese chain of supermarkets. [ABSTRACT FROM AUTHOR]

Cazals et al. (J. Econom. 106: 1–25, ), Daraio and Simar (J. Prod. Anal. 24: 93–121, ; Advanced Robust and Nonparametric Methods in Efficiency Analysis, ; J. Prod. Anal. 28: 13–32, ) developed a conditional frontier model which incorporates the environmental factors into measuring the efficiency of a production process in a fully nonparametric setup. They also provided the corresponding nonparametric efficiency measures: conditional FDH estimator, conditional DEA estimator. The two estimators have been applied in the literature without any theoretical background about their statistical properties. The aim of this paper is to provide an asymptotic analysis (i.e. asymptotic consistency and limit sampling distribution) of the conditional FDH and conditional DEA estimators. [ABSTRACT FROM AUTHOR]

Based on a two-stage analysis of a panel of data on 12 outlets of a high-end retailer for 24 months, we investigate how the level of supervisory monitoring affects retail sales productivity. In the first stage, we use Data Envelopment Analysis (DEA) to compute the relative productivity of retail outlets in using their labor and capital resources to generate store sales. In the second stage, we regress the logarithm of DEA scores on contextual variables to obtain consistent estimators of the impact of contextual variables on productivity (Banker and Natarajan in Operation Research 56:48–58, ). Contrary to agency theoretic prediction that supervisory monitoring leads to an increase in retail sales productivity, our empirical results indicate that the higher the level of supervisory monitoring, the lower is the retail sales productivity for high-end retail outlets. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) models with undesirable inputs and outputs have been frequently discussed in DEA literature, e.g., via data transformation. These studies were scatted in the literature, and often confined to some particular applications. In this paper we present a systematic investigation on model building of DEA without transferring undesirable data. We first describe the disposability assumptions and a number of different performance measures in the presence of undesirable inputs and outputs, and then discuss different combinations of the disposability assumptions and the metrics. This approach leads to a unified presentation of several classes of DEA models with undesirable inputs and/or outputs. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA) an inefficient unit can be projected onto an efficient target that is far away, i.e. reaching the target may demand large reductions in inputs and increases in outputs. When the inputs and outputs modifications planned are large, it may be troublesome to carry them out all at once. In order to help an inefficient unit reach a distant target, a strategy of gradual improvements with successive, intermediate targets has been proposed. This paper extends such approach to the variable returns to scale (VRS) case. In the VRS scenario we distinguish between units that are technical efficient and those that are not. On the one hand, for those units that are not technical efficient the proposed approach determines successive intermediate targets leading to the technical efficiency frontier, i.e. the priority for those units is to attain technical efficiency. On the other hand, for those units that are technical efficient but not scale efficient the proposed approach computes a sequence of targets ending in the global efficiency frontier, i.e. when technical efficiency is guaranteed the goal is then to attain global efficiency. In both cases, the successive targets are obtained by iteratively solving specific DEA models that take into account given bounds on the rates of change in inputs and outputs that the unit can implement in each step. [ABSTRACT FROM AUTHOR]

Selecting an appropriate supplier for outsourcing is now one of the most important decisions of the purchasing department. This paper proposes a model for ranking suppliers in the presence of weight restrictions, nondiscretionary factors, and cardinal and ordinal data. A numerical example demonstrates the application of the proposed method. [ABSTRACT FROM AUTHOR]

Imposing additional weight restrictions increases the degeneracy and computational complexity of solving Data Envelopment Analysis (DEA) models, a known class of linear programming models. In this paper some linear transformations to reduce these problems are provided. [ABSTRACT FROM AUTHOR]

Abstract: This article takes 117 branches of a certain bank in Taiwan in 2006 as the research subject and introduces data envelopment analysis (DEA) to evaluate the operating performances of business units of this bank to provide the reference for a bank’s managers in determining operation strategies. The result indicates that, in overall technical efficiency, the case bank has many inefficient branches distinctly; the average overall technical efficiency of branches is 54.8% and the average pure technical efficiency of branches is 67%, which is probably because of lower loan-to-deposit ratio, leading to excessive input waste. The average scale efficiency of the case bank during the sample period is 82%. The ratio of resource waste due to technical inefficiency is 45.2%, of which 55.03% is due to pure technical inefficiency. [Copyright &y& Elsevier]

Abstract: The article aims to investigate how operational managerial practices can contribute to improved farm level efficiency at dairy farms. Operational managerial practices are defined as animal health, breeding, and feeding practices. The main contribution of the article is that it investigates aspects that can be adjusted in the every day management to improve farm efficiency. Aspects describing each of the considered managerial practices were regressed on farm level data envelopment analysis (DEA) input efficiency scores based on farm level data from Sweden. The DEA efficiency scores were based on 507 farms, and the following regression analyses were based on 169 farms. The results showed that changes in breeding and feeding practices can affect efficiency. Breeding exactly the number of heifers that was needed for replacement of the dairy cows negatively affected long-run technical efficiency (i.e. not over-using inputs). On the other hand, analyzing forage positively affected long-run allocative efficiency (i.e. using the cheapest combination of inputs) and analyzing fodder grain positively affected short-run economic efficiency (i.e. a combined measure of technical and allocative efficiencies). Feeding the cows hay instead of only silage, reduced long-run economic efficiency. No significant effects of animal health practices were found. These results suggest that the farms in the sample are homogeneous in terms of animal health practices and that inefficient farms cannot become more efficient by adapting to the animal health practices of more efficient farms. [Copyright &y& Elsevier]

Effective decision-making techniques are essentially dependent on the capacity to balance (symmetry) requirements and their fulfilment, that is, the capacity to accurately identify a collection of factors that have the greatest influence on performance. Data envelopment analysis (DEA) is a useful nonparametric method in operations research for performance estimation by measuring the efficiency scores of the decision-making units. In this paper, we develop a global search method (GSM) for selecting the key input and output variables in DEA models. The GSM measures the effects of variables with respect to the efficiency scores directly, i.e., by considering the average change when a variable is added or removed from the analysis. It aims to produce DEA models that include only the key variables with the largest impact on the results. The effectiveness of the GSM is demonstrated using a case study from 15 US banks, with the results analyzed and discussed. The outcomes indicate that the GSM yields useful insight for decision-makers to make informed decisions in undertaking their problems. [ABSTRACT FROM AUTHOR]

This article aimed to develop an insolvency forecasting model through a mathematical technique that was based on Operational Research: Data Envelopment Analysis (DEA). The research methodology involved the following steps: (1) we selected ten companies that faced a bankruptcy concordat process between 1995 and 2003; (2) for each insolvent company, we chose by lot five companies among those that performed best in the sector and had a similar size, summing up a sample of 50 health companies; (3) the sample companies' accounting information for a three-year period before the bankruptcy/concordat were obtained from Fipecafi/Exame's Melhores e Maiores database, and submitted to statistical analyses; (4) we developed a DEA model and calculated efficiency indicators for the sample companies; (5) we determined the cut-off point to rank companies as insolvent or solvent; (6) the classifications obtained through the DEA indicators were confronted with the company's actual situation. Preliminary results have shown that the developed DEA model was capable of distinguishing between solvent and insolvent companies with a reasonable level of exactness: it correctly classified 90% of the insolvent companies. [ABSTRACT FROM AUTHOR]

This paper attempts to provide a systematic approach to the DEA model building. To this end, we try to identify some essential aspects of DEA modelling. Three key building blocks in a DEA model are identified: they are preference order, production possibility set and performance measure. It is shown that the preferences and performance measurements used in the standard DEA models are only particular examples in this framework. It is also illustrated in this work that this methodology is useful in building new DEA models to handle nonstandard applications such as those involve non-Pareto preferences or undesirable inputs-outputs. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to examine the possibility of measuring efficiency in the context of higher education. The paper begins by exploring the advantages and drawbacks of the various methods for measuring efficiency in the higher education context. The ease with which data envelopment analysis (DEA) can handle multiple inputs and multiple outputs makes it an attractive choice of technique for measuring the efficiency of higher education institutions (HEIs), yet its drawbacks cannot be ignored. Thus, a number of extensions to the methodology, designed to overcome some of the disadvantages, are presented. The paper ends with an application of DEA to a data set of more than 100 HEIs in England using data for the year 2000/01. Technical and scale efficiency in the English higher education sector appear to be high on average. The Pastor, Ruiz, and Sirvent [2002. Operations Research, 50(4), 728–735] test for comparing nested DEA models is useful in reducing the full model to a smaller ‘significant’ set of inputs and outputs. Thus, the quantity and quality of undergraduates, the quantity of postgraduates, expenditure on administration, and the value of interest payments and depreciation are significant inputs to, and the quantity and quality of undergraduate degrees, the quantity of postgraduate degrees and research are significant outputs in the English higher education production process. The possibility of differences in the production frontier (and hence the distribution of efficiencies) of three distinct groups of HEIs is explored using a test proposed by Charnes, Cooper, and Rhodes [1981. Management Science, 27(6), 668–697] but no significant differences are found. Bootstrapping procedures, however, suggest that differences between the most and least efficient English HEIs are significant. [Copyright &y& Elsevier]

Abstract: We present a data envelopment analysis approach for determining ranking indices among failure modes in which the typical FMEA parameters are modeled as fuzzy sets. By this approach, inference rules of the IF THEN kind can be bypassed. The proposed approach is applied to a typical PWR auxiliary feedwater system. The results are compared to those obtained by means of: the risk priority numbers, pure fuzzy logic concepts, and finally the DEA-APGF (profiling of severity efficiency) approach. The results demonstrate the potential of the combination of fuzzy logic concepts and data envelopment analysis for this class of problems. [Copyright &y& Elsevier]

• A managerial use of inverse data envelopment analysis for operational planning is discussed. • The necessity of considering operational changes with environmental factors is addressed. • A computational procedure to properly capture the effective frontier changes is proposed. • The proposed model is demonstrated by applying it to the Korean natural gas industry. Inverse data envelopment analysis (DEA) is a useful planning tool, especially when it is combined with frontier changes that accurately reflect reality. This paper proposes an inverse optimization model for operational planning by taking into account frontier changes in conjunction with environmental factors. The aim is not only to present a computational procedure of a new measure to properly capture the effective frontier changes, but more importantly to demonstrate how frontier changes observed in the past can be utilized to provide insights into estimation of the future production frontier. In essence, the proposed model is intended to help establish realistic goals for operational planning practices. The model is applied to the Korean natural gas industry as an empirical demonstration. [ABSTRACT FROM AUTHOR]

The European Union (EU) energy policy for sustainable development has been the topic of continuous debate, research, and analysis, which frequently focused on objectives and the evaluation of quantitative and qualitative performance. Different approaches can be used for the assessment of sustainable development goals. The authors of the article conducted a literature review of relevant research papers dated 2016–2020. The most common are quantitative methods based on hard data. Some qualitative studies based on soft data are also available but rare. This article proposes hybrid Rough Set Data Envelopment Analysis (DEA) and Rough Set Network DEA models that integrate both approaches. Also, the models allow the inclusion of uncertainty in the underlying data. The article uses hard data of the International Energy Agency (IEA) and the results of the EU survey regarding the influence of the socio-economic environment on CO2 emissions in EU countries. The authors demonstrate that multifaceted and objective assessment is possible by merging concepts from the set theory and operational research. [ABSTRACT FROM AUTHOR]