Browsing by Author "Agrell, P. J."
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Item Metadata only Allocating fixed resources and setting targets using a common-weights DEA approach(Elsevier, 2013) Hosseinzadeh Lotfi, F.; Hatami-Marbini, A.; Agrell, P. J.; Aghayi, N.; Gholami, K.Data envelopment analysis (DEA) is a data-driven non-parametric approach for measuring the efficiency of a set of decision making units (DMUs) using multiple inputs to generate multiple outputs. Conventionally, DEA is used in ex post evaluation of actual performance, estimating an empirical best-practice frontier using minimal assumptions about the shape of the production space. However, DEA may also be used prospectively or normatively to allocate resources, costs and revenues in a given organization. Such approaches have theoretical foundations in economic theory and provide a consistent integration of the endowment-evaluation-incentive cycle in organizational management. The normative use, e.g. allocation of resources or target setting, in DEA can be based on different principles, ranging from maximization of the joint profit (score), combinations of individual scores or game-theoretical settings. In this paper, we propose an allocation mechanism that is based on a common dual weights approach. Compared to alternative approaches, our model can be interpreted as providing equal endogenous valuations of the inputs and outputs in the reference set. Given that a normative use implicitly assumes that there exists a centralized decision-maker in the organization evaluated, we claim that this approach assures a consistent and equitable internal allocation. Two numerical examples are presented to illustrate the applicability of the proposed method and to contrast it with earlier work.Item Metadata only Centralized resource reduction and target setting under DEA control(2013) Hosseinzadeh Lotfi, F.; Hatami-Marbini, A.; Agrell, P. J.; Gholami, K.; Ghelej Beigi, Z.Data envelopment analysis (DEA) is a powerful tool for measuring the relative efficiencies of a set of decision making units (DMUs) such as schools and bank branches that transform multiple inputs to multiple outputs. In centralized decision-making systems, management normally imposes common resource constraints such as fixed capital, budgets for operating capital and staff count. In consequence, the profit or net value added of the units subject to resource reductions will decrease. In terms of performance evaluation combined with resource allocation, the interest of central management is to restore the general efficiency value of the DMUs. The paper makes four contributions to the literature: (1) we take into consideration the performance evaluation of the centralized budgeting of hierarchical organizations along with sales and market allocation within manufacturing and distribution organizations; (2) we address the evaluation problems that the central decision maker does not desire to deteriorate the efficiency score of the DMUs after input and/or output reduction; (3) we develop a common set of weights (CSW) method based on the goal program (GP) concept to control the total weight flexibility in the conventional DEA models; (4) we extend a new approach to optimize the inputs and/or outputs contraction such that the efficiency of all DMUs will get bigger than or equal to the efficiency of previous change. We ultimately present a numerical example involving with three inputs and two outputs to illustrate the applicability and efficacy of the proposed approach.Item Metadata only Chance-constrained DEA models with random fuzzy inputs and outputs(Elsevier, 2013-06-06) Tavana, M.; Shiraz, R. K.; Hatami-Marbini, A.; Agrell, P. J.; Paryab, K.Item Metadata only A common set of weight approach using an ideal decision making unit in data envelopment analysis(AIMS, 2012-08) Saati, S.; Hatami-Marbini, A.; Agrell, P. J.; Tavana, M.Data envelopment analysis (DEA) is a common non-parametric frontier analysis method. The multiplier framework of DEA allows flexibility in the selection of endogenous input and output weights of decision making units (DMUs) as to cautiously measure their efficiency. The calculation of DEA scores requires the solution of one linear program per DMU and generates an individual set of endogenous weights (multipliers) for each performance dimension. Given the large number of DMUs in real applications, the computational and conceptual complexities are considerable with weights that are potentially zero-valued or incommensurable across units. In this paper, we propose a two-phase algorithm to address these two problems. In the first step, we define an ideal DMU (IDMU) which is a hypothetical DMU consuming the least inputs to secure the most outputs. In the second step, we use the IDMU in a LP model with a small number of constraints to determine a common set of weights (CSW). In the final step of the process, we calculate the efficiency of the DMUs with the obtained CSW. The proposed model is applied to a numerical example and to a case study using panel data from 286 Danish district heating plants to illustrate the applicability of the proposed method.Item Metadata only A common-weights DEA model for centralized resource reduction and target setting(Elsevier, 2014-11-20) Hatami-Marbini, A.; Tavana, M.; Agrell, P. J.; Hosseinzadeh Lotfi, F.; Ghelej Beigi, Z.Data Envelopment Analysis (DEA) is a powerful tool for measuring the relative efficiency for a set of Decision Making Units (DMUs) that transform multiple inputs into multiple outputs. In centralized decision-making systems, management normally imposes common resource constraints to maximize operating revenues and minimize operating expenses. In this study, we propose an alternative DEA model for centrally imposed resource or output reduction across the reference set. We determine the amount of input and output reduction needed for each DMU to increase the efficiency score of all the DMUs. The contribution of the proposed model is fourfold: (1) we take into consideration the performance evaluation of the centralized budgeting in hierarchical organizations; (2) we use a Common Set of Weights (CSW) method based on the Goal Programming (GP) concept to control the total weight flexibility in the conventional DEA models; (3) we propose a comprehensive approach for optimizing the inputs and/or outputs contractions and improving the final efficiencies of the DMUs while reducing the computational complexities; (4) we compare the proposed method with an approach in the literature; and (5) we demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a numerical example.Item Metadata only Consistent and robust ranking in imprecise data envelopment analysis under perturbations of random subsets of data(Springer, 2014-01) Shokouhi, A. H.; Shahriari, H.; Agrell, P. J.; Hatami-Marbini, A.Data envelopment analysis (DEA) is a non-parametric method for measuring the relative efficiency of a set of decision making units using multiple precise inputs to produce multiple precise outputs. Several extensions to DEA have been made for the case of imprecise data, as well as to improve the robustness of the assessment for these cases. Prevailing robust DEA (RDEA) models are based on mirrored interval DEA models, including two distinct production possibility sets (PPS). However, this approach renders the distance measures incommensurate and violates the standard assumptions for the interpretation of distance measures as efficiency scores. We propose a modified RDEA (MRDEA) model with a unified PPS to overcome the present problem in RDEA. Based on a flexible formulation for the number of variables perturbed, MRDEA calculates the empirical distribution for the interval efficiency for the case of a random number of variables affected. The MRDEA approach also decreases the computational complexity of the RDEA model, as well as significantly increases the discriminatory power of the model without additional information requirements. The properties of the method are demonstrated for four different numerical instances.Item Metadata only Efficiency analysis under imprecise inputs and outputs reduction(World Scientific, 2012) Gholami, K.; Hosseinzadeh Lotfi, F.; Hatami-Marbini, A.; Agrell, P. J.; Ghelej Beigi, Z.Data envelopment analysis (DEA) is a powerful tool for measuring the relative efficiencies of a set of decision making units (DMU) that consume multiple inputs to produce multiple outputs. DEA can also be used in activity planning and economic regulation of decentralized units under a common management. One important management challenge in such systems (e.g. schools, hospitals, utilities) is how to implement reductions in common resources (budget, staff, raw material) as well as centrally allocated outputs (students, patients, service areas) while maintaining a high operational efficiency of the system. We extend earlier work on DEA activity planning under resource and output restrictions for the case in which the centralized planner is facing an imprecise shrinkage factor, here expressed as interval data. The implementation of the proposed method is applied for a numeric example.Item Metadata only Extended symmetric and asymmetric weight assignment methods in data envelopment analysis(Elsevier, 2015-06-27) Hatami-Marbini, A.; Rostamy-Malkhalifeh, M.; Agrell, P. J.; Tavana, M.; Mohammadi, F.Dual weight restrictions are commonly suggested as a remedy to the problem of low discriminatory power and absurd marginal prices in conventional Data Envelopment Analysis (DEA) models. However, weight restriction models also suffer from potential problems of infeasibility, lack of exogenous determination and ambiguous interpretations. The Symmetric Weight Assignment Technique (SWAT) addresses these concerns through a symmetric endogenous weight selection process. In this paper, we extend the SWAT method by proposing four new DEA models. Symmetric and asymmetric weights are rewarded and penalized, respectively, in the proposed models. The first model takes into account the symmetrical weights assigned to the outputs in the input-oriented model. The second model takes into account the symmetrical weights assigned to the inputs in the output-oriented model. The third and fourth models simultaneously take into account symmetric input–output weights in both the input and output orientations. We demonstrate the applicability of the proposed models and the efficacy of the procedures and algorithms with an application to Danish district heating plants.Item Metadata only A facility location model using DEA(2016) Hatami-Marbini, A.; Toloo, M.; Agrell, P. J.In general, facility location problems target at finding the optimal location among a set of candidate facility locations (e.g., plants and warehouses) where the objective is to minimize the total cost of the siting configuration in line with fulfilling the demands. Since facility location decisions normally have significant and long-term impact on the entire supply chain, we require to consider a number of, often conflicting, objectives. In this study, we develop a new facility location model using data envelopment analysis (DEA) with the aim of locating the facility as well as determining its optimal capacity. To validate the proposed model, we prove some theorems and provide an illustrating example.Item Metadata only A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing(Elsevier, 2016-11-02) Hatami-Marbini, A.; Agrell, P. J.; Tavana, M.; Khoshnevis, P.Sustainable sourcing is a recent priority for firms considering customer behavior and societal norms with respect to the supply chain. Customer attitudes, particularly in the developed countries, are affected by the perceived sustainability of products or services regarding environmental, social and economic aspects. Seeking to maximize their market shares, firms frequently require an effective sourcing approach in supply chain management (SCM) by selecting sustainable suppliers (sourcing) and by enforcing standards through continuous supplier evaluations (monitoring) as well as by contract adjustments (retention). Most existing sourcing methodologies are either cost-oriented or ad hoc, without the tools and techniques necessary to deal with sustainability. In this paper, we propose a product-based framework for sustainable supplier sourcing considering different sustainability, operational and organizational criteria based on the type of outsourced products in the evaluation process. We develop a flexible cross-efficiency evaluation methodology based on data envelopment analysis (DEA) for identifying supplier performance. This research also uses fuzzy set theory to tackle the vagueness of information that is often present in the information-gathering step. We present a case study from the semiconductor industry to demonstrate the applicability of the proposed model and the efficacy of the procedures and algorithms.Item Metadata only A Framework for a Sustainable Sourcing: A Flexible Cross Efficiency Evaluation in Fuzzy DEA(2014) Khoshnevis, P.; Agrell, P. J.; Blome, C.; Hatami-Marbini, A.Nowadays, customer attitudes focus on sustainability of the products/services regarding environmental, social and economic aspects. In this study, we propose a product-based framework for sustainable supplier sourcing in SCM, which considers sustainability, operational and organizational criteria in terms of the outsourced product. We develop a flexible cross efficiency evaluation DEA for effectively discriminating suppliers along with using fuzzy sets to tackle the vagueness information.Item Metadata only Frontier analysis of supply chain management: A game-theoretic perspective(2014) Agrell, P. J.; Bogetoft, P.; Hatami-Marbini, A.Supply chains can be seen as an empirically well represented case of decentralized decision making systems with co-existence of common (system) objectives and private (firm-specific) interests. A non-trivial supply chain consists of several stages (tiers) where each tier consists of several firms or plants delivering products and services to downstream tiers. The analysis is further complicated by intertemporal heterogeneity (demand changes) as well as task heterogeneity (product differentiation). Not surprisingly, non-parametric approaches have been attempted to supply chain performance assessment with various success. In this paper, we present a general model for supply chain performance assessment where we highlight the (i) the strategic behavioral perspective (decentralization), (ii) the intertemporal perspective (inventory building) and (iii) the competition-cooperative perspective (system limits). We show that DEA is indeed a useful approach for both system-wide and unit-specific evaluation, including the decomposition of productive efficiency in DMU vs chain level effects. Drawing analogies from the merger analysis framework in Bogetoft and Wang (2005), the analysis of supply chain performance can be seen as an exercise in rational decision making under a set of temporal common constraints.Item Metadata only Frontier-based performance analysis models for supply chain management: State of the art and research directions(Elsevier, 2013-02-28) Agrell, P. J.; Hatami-Marbini, A.Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources. Acknowledging the lack of system’s view, the need to identify system-wide and individual effects as well as incorporating a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in network DEA modeling, in particular two-stage models, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating current work in this range using the unified notations and comparison of the properties of the presented models, the paper is closed with recommendations for future research in terms of both theory and application.Item Metadata only Frontier-based performance analysis models for supply chain management: State of the art and research directions(2011) Agrell, P. J.; Hatami-Marbini, A.Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources as well as potentially environmental exposure. Acknowledging the lack of system's view, the need to identify system-wide as well as individual effects, as well as the incorporation of a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in multi-stage or network DEA modeling, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating the current work in this range using a unified notation and by comparing the properties of the models presented, the paper is closed with recommendations for future research in terms of both theory and application.Item Metadata only A fuzzy data envelopment analysis for clustering operating units with imprecise data(World Scientific, 2013) Saati, S.; Hatami-Marbini, A.; Tavana, M.; Agrell, P. J.Data envelopment analysis (DEA) is a non-parametric method for measuring the efficiency of peer operating units that employ multiple inputs to produce multiple outputs. Several DEA methods have been proposed for clustering operating units. However, to the best of our knowledge, the existing methods in the literature do not simultaneously consider the priority between the clusters (classes) and the priority between the operating units in each cluster. Moreover, while crisp input and output data are indispensable in traditional DEA, real-world production processes may involve imprecise or ambiguous input and output data. Fuzzy set theory has been widely used to formalize and represent the impreciseness and ambiguity inherent in human decision-making. In this paper, we propose a new fuzzy DEA method for clustering operating units in a fuzzy environment by considering the priority between the clusters and the priority between the operating units in each cluster simultaneously. A numerical example and a case study for the Jet Ski purchasing decision by the Florida Border Patrol are presented to illustrate the efficacy and the applicability of the proposed method.Item Metadata only Fuzzy stochastic data envelopment analysis with application to Base Realignment and Closure (BRAC)(Elsevier, 2012-04-26) Tavana, M.; Shiraz, R. K.; Hatami-Marbini, A.; Agrell, P. J.; Paryab, K.Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) on the basis of multiple inputs and outputs. Conventional DEA models assume that inputs and outputs are measured by exact values on a ratio scale. However, the observed values of the input and output data in real-world problems are often vague or random. Indeed, decision makers (DMs) may encounter a hybrid uncertain environment where fuzziness and randomness coexist in a problem. Several researchers have proposed various fuzzy methods for dealing with the ambiguous and random data in DEA. In this paper, we propose three fuzzy DEA models with respect to probability-possibility, probability-necessity and probability-credibility constraints. In addition to addressing the possibility, necessity and credibility constraints in the DEA model we also consider the probability constraints. A case study for the base realignment and closure (BRAC) decision process at the U.S. Department of Defense (DoD) is presented to illustrate the features and the applicability of the proposed models.Item Metadata only Imprecise data envelopment analysis for the two-stage process(2013) Hatami-Marbini, A.; Agrell, P. J.; Aghayi, N.The aggregate black-box approach of conventional Data Envelopment Analysis (DEA) limits its usefulness in situations where the observation is the result of independent decision making in sub-units (sub-DMUs), sequentially linked through processes or semi-finished products. The situation is commonly found in e.g supply chain management, health care provision and environmental management (waste water treatment). Alternative approaches for sublevel evaluations include two-stage or multi-stage models, where intermediate outputs or inputs are identified to span local production possibility spaces. However, the reliance upon numeric values for such intermediate inputs or outputs adds an additional difficulty that may lower the value of the assessment. In this paper, we present an approach for two-stage evaluation with interval data to resolve this problem. The results show that ignoring the interval quality of the data leads to distorted evaluations, both for the subunit and the system efficiency. The proposed method obtains an efficiency interval consisting in an upper and a lower bound for the system efficiency and the sub-DMU efficiency. In order to link two stages, we consider the interval intermediate measures that are outputs and inputs for the first stage and the second stage, respectively. The derived interval metric, along with its mean, provides a more informative basis for multi-stage evaluation in the presence of imprecise data. The ranks of DMUs and sub-DMUs are obtained based on their interval efficiencies.Item Metadata only Interval data without sign restrictions in DEA(Elsevier, 2013-10-26) Hatami-Marbini, A.; Emrouznejad, A.; Agrell, P. J.Conventional DEA models assume deterministic, precise and non-negative data for input and output observations. However, real applications may be characterized by observations that are given in form of intervals and include negative numbers. For instance, the consumption of electricity in decentralized energy resources may be either negative or positive, depending on the heat consumption. Likewise, the heat losses in distribution networks may be within a certain range, depending on e.g. external temperature and real-time outtake. Complementing earlier work separately addressing the two problems; interval data and negative data; we propose a comprehensive evaluation process for measuring the relative efficiencies of a set of DMUs in DEA. In our general formulation, the intervals may contain upper or lower bounds with different signs. The proposed method determines upper and lower bounds for the technical efficiency through the limits of the intervals after decomposition. Based on the interval scores, DMUs are then classified into three classes, namely, the strictly efficient, weakly efficient and inefficient. An intuitive ranking approach is presented for the respective classes. The approach is demonstrated through an application to the evaluation of bank branches.Item Metadata only Positive and normative use of Fuzzy DEA-BCC models: A critical view on NATO enlargement(Wiley, 2013) Hatami-Marbini, A.; Tavana, M.; Saati, S.; Agrell, P. J.Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the input and output of a set of comparable decision-making units (DMUs) by evaluating their relative efficiency. The traditional DEA methods require accurate measurement of both the inputs and outputs. However, the real evaluation of the DMUs is often characterized by imprecision and uncertainty in data definitions and measurements. The development of fuzzy DEA (FDEA) with imprecise and ambiguous data has extended the scope of application for efficiency measurement. The purpose of this paper is to develop a fuzzy DEA framework with a BCC model for measuring crisp and interval efficiencies in fuzzy environments. We use an α-level approach to convert the fuzzy Banker, Charnes, and Cooper (BCC) (variable returns to scale) model into an interval programming model. Instead of comparing the equality (or inequality) of the two intervals, we define a variable in the interval to satisfy our constraints and maximize the efficiency value. We present a numerical example to show the similarities and differences between our solution and the solutions obtained from four fuzzy DEA methods in the literature. In addition, a case study for NATO enlargement is presented to illustrate the applicability of the proposed method.Item Metadata only Resource allocation and target setting in interval data envelopment analysis(World Scientific, 2012) Ghelej Beigi, Z.; Hosseinzadeh Lotfi, F.; Hatami-Marbini, A.; Agrell, P. J.; Gholami, K.Data envelopment analysis (DEA) is a commonly used non-parametric technique for performance measurement of decision making units (DMU) that can also be used in normatively in activity planning, resource allocation and target setting. Whereas earlier work in this line have considered completely defined data, prospective use of DEA in activity planning often involves uncertainty with respect to the feasible ranges for allocation of resources and target settings. In this paper, we present an imprecise DEA-based method with interval inputs and outputs as to address this shortcoming. The proposed model corresponds to reasonable managerial objectives concerning the technical efficiency of the subordinate units after implementing resource allocation and target setting.