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Dr. Adel Hatamimarbini
Department of Management and Entrepreneurship, Faculty of Business & Law, De Montfort University, Leicester LE1 9BH, UK

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Research Keywords & Expertise

0 sustainable supply chain management
0 Quantitative modelling in operations management
0 Logistics and transportation
0 Decision support systems (DEA, MCDM, …)
0 Performance evaluation of supply chain managemen

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Performance evaluation of supply chain managemen

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Journal article
Published: 18 March 2021 in European Journal of Operational Research
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Measuring economic and cost efficiency receives ever-increasing attention of the executives and managers of small-and medium-sized enterprises (SMEs) to minimise the total production costs. The conventional Farrell cost efficiency (CE) as a key determinant requires the precise information on inputs, outputs and input prices, while in praxis uncertainty is inherent and inevitable in data and its negligence conceivably results in a dire approximation for CE measures. This paper is concerned with Farrell CE in situations of both endogenous and exogenous uncertainty. The source of uncertainty allows us to define two different scenarios; (i) in situations of endogenous uncertainty in input and output data where the uncertainty is affected by the decision maker, and (ii) in situations of uncertain prices for inputs where the uncertainty is exogenously given. In the first scenario, the theory of robust optimisation is adopted to develop the robust data envelopment analysis (DEA) models with the aim of grappling uncertainties in input and output data when measuring technical and cost efficiencies. The second scenario aims to accommodate uncertainties on price information by developing a pair of robust DEA models based upon robust optimisation estimating the upper and lower bounds for CE measures. This unprecedented study helps us to provide a generalised framework for economic efficiency with uncertainties in which conventional properties of Farrell measures are fulfilled. In addition to comparing the developed approach in this paper with other existing approaches through a simple numerical example, the usefulness and applicability of the suggested framework are minutely studied in an empirical application in the context of allocation problems.

ACS Style

Adel Hatami-Marbini; Aliasghar Arabmaldar. Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application. European Journal of Operational Research 2021, 295, 604 -620.

AMA Style

Adel Hatami-Marbini, Aliasghar Arabmaldar. Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application. European Journal of Operational Research. 2021; 295 (2):604-620.

Chicago/Turabian Style

Adel Hatami-Marbini; Aliasghar Arabmaldar. 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application." European Journal of Operational Research 295, no. 2: 604-620.

Review article
Published: 20 February 2021 in Expert Systems with Applications
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A coalition as a group of agents aims to work jointly to earn much more gains as a result of their cooperation. Many existing studies assumed that members take advantage of joining one coalition at a time, albeit the importance of coalition formation problems. Therefore, more attention to overlapping coalitions needs to be paid to optimise resource management by forming in multiple overlapping coalitions simultaneously. Roughly speaking, the related literature includes two main streams; (i) theoretical foundations of coalition formation games and, (ii) the coalition structure generation problems. This paper first provides a review of coalition structure generation at large to develop a taxonomic framework and classify the existing literature, viz., macro analysis. The paper then reviews studies on overlapping coalitions thoroughly, viz., micro analysis. The micro analysis presents and discusses different models of overlapping coalition games and related solution concepts as well as surveying all problem-solving approaches for overlapping coalition structure generation. Finally, the outstanding challenges and opportunities for future research considerations are discussed and shared.

ACS Style

Hannan Amoozad Mahdiraji; Elham Razghandi; Adel Hatami-Marbini. Overlapping coalition formation in game theory: A state-of-the-art review. Expert Systems with Applications 2021, 174, 114752 .

AMA Style

Hannan Amoozad Mahdiraji, Elham Razghandi, Adel Hatami-Marbini. Overlapping coalition formation in game theory: A state-of-the-art review. Expert Systems with Applications. 2021; 174 ():114752.

Chicago/Turabian Style

Hannan Amoozad Mahdiraji; Elham Razghandi; Adel Hatami-Marbini. 2021. "Overlapping coalition formation in game theory: A state-of-the-art review." Expert Systems with Applications 174, no. : 114752.

Regular article
Published: 14 October 2020 in OR Spectrum
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Data envelopment analysis (DEA) is a data-driven and benchmarking tool for evaluating the relative efficiency of production units with multiple outputs and inputs. Conventional DEA models are based on a production system by converting inputs to outputs using input-transformation-output processes. However, in some situations, it is inescapable to think of some assessment factors, referred to as dual-role factors, which can play simultaneously input and output roles in DEA. The observed data are often assumed to be precise although it needs to consider uncertainty as an inherent part of most real-world applications. Dealing with imprecise data is a perpetual challenge in DEA that can be treated by presenting the interval data. This paper develops an imprecise DEA approach with dual-role factors based on revised production possibility sets. The resulting models are a pair of mixed binary linear programming problems that yield the possible relative efficiencies in the form of intervals. In addition, a procedure is presented to assign the optimal designation to a dual-role factor and specify whether the dual-role factor is a nondiscretionary input or output. Given the interval efficiencies, the production units are categorized into the efficient and inefficient sets. Beyond the dichotomized classification, a practical ranking approach is also adopted to achieve incremental discrimination through evaluation analysis. Finally, an application to third-party reverse logistics providers is studied to illustrate the efficacy and applicability of the proposed approach.

ACS Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. An interval efficiency analysis with dual-role factors. OR Spectrum 2020, 43, 255 -287.

AMA Style

Mehdi Toloo, Esmaeil Keshavarz, Adel Hatami-Marbini. An interval efficiency analysis with dual-role factors. OR Spectrum. 2020; 43 (1):255-287.

Chicago/Turabian Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. 2020. "An interval efficiency analysis with dual-role factors." OR Spectrum 43, no. 1: 255-287.

Journal article
Published: 20 June 2020 in Computers & Industrial Engineering
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The problem of controlling the production rates of failure prone manufacturing systems has stochastic features that make it more complex and challenging. In this study, we consider a network of manufacturing machines based on the hedging point policy where the final goods are perishable, and the demand rate is constant. Our objective in this paper is to control the production rates of multiple machines in failure prone manufacturing systems in the presence of perishable goods in order to minimise the expected cost consisting of holding, shortage, perished goods and repair costs over an infinite horizon. We develop a new framework by way of a simulation-optimisation approach to deal with complexity and uncertainty. To this end, we first formulate the analytical model subject to stochastic failures and corrective repairs. Then, we use a combination of simulated annealing metaheuristic, simulation and Taguchi experimental design to estimate the optimal control policy. In addition, a numerical example is presented to illustrate the applicability and efficacy of the proposed framework.

ACS Style

Adel Hatami-Marbini; Seyed Mojtaba Sajadi; Hiva Malekpour. Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Computers & Industrial Engineering 2020, 146, 106614 .

AMA Style

Adel Hatami-Marbini, Seyed Mojtaba Sajadi, Hiva Malekpour. Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Computers & Industrial Engineering. 2020; 146 ():106614.

Chicago/Turabian Style

Adel Hatami-Marbini; Seyed Mojtaba Sajadi; Hiva Malekpour. 2020. "Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods." Computers & Industrial Engineering 146, no. : 106614.

Journal article
Published: 19 March 2020 in Omega
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Data envelopment analysis (DEA) is a non-parametric data-driven approach for evaluating the efficiency of a set of homogeneous decision-making units (DMUs) with multiple inputs and multiple outputs. The number of performance factors (inputs and outputs) plays a crucial role when applying DEA to real-world applications. In other words, if the number of performance factors is significantly greater than the number of DMUs, it is highly possible to arrive at a large portion of efficient DMUs, which practically may become problematic due to the lack of ample discrimination among DMUs. The current research aims to develop an array of selecting DEA models to narrow down the performance factors based upon a rule of thumb. To this end, we show that the input- and output-oriented selecting DEA models may select different factors and then present the integrated models to identify a set of common factors for both orientations. In addition to efficiency evaluation at the individual level, we study structural efficiency with a single production unit at the industry level. Finally, a case study on the EU countries is presented to give insight into business innovation, social economy and growth with regard to the efficiency of the EU countries and entire EU.

ACS Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. Selecting data envelopment analysis models: A data-driven application to EU countries. Omega 2020, 101, 102248 .

AMA Style

Mehdi Toloo, Esmaeil Keshavarz, Adel Hatami-Marbini. Selecting data envelopment analysis models: A data-driven application to EU countries. Omega. 2020; 101 ():102248.

Chicago/Turabian Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. 2020. "Selecting data envelopment analysis models: A data-driven application to EU countries." Omega 101, no. : 102248.

Original paper
Published: 14 March 2020 in Operational Research
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Supplier selection is one of the key competencies in the sourcing function. Considering the important role of suppliers in the strategy framework of supply chains, it is surprising that the sourcing function has not been subject to more focused research on the development of adequate decision support tools. The relatively simplified ranking systems that often have been presented on an ad hoc basis offer only partial information on the decision. This research attempts to develop a unified and integrated structure for supplier selection practices across a supply chain on the basis of strategic planning. Our evaluation is conducted by means of a multi-attribute efficiency analysis models and a multivariate statistical method, a so-called principal component analysis-data envelopment analysis (PCA-DEA) approach, to support supplier relationship management under uncertainty. The main contribution of this paper is to address the gap in the supply chain management (SCM) literature by proposing a strategy-based method for supplier selection problems when data are interrelated and interdependent. The proposed method in this study is applied to a real-world case study in agri-food industry to demonstrate the advantages and applicability of the proposed framework.

ACS Style

Adel Hatami-Marbini; Siavash Hekmat; Per J. Agrell. A strategy-based framework for supplier selection: a grey PCA-DEA approach. Operational Research 2020, 1 -35.

AMA Style

Adel Hatami-Marbini, Siavash Hekmat, Per J. Agrell. A strategy-based framework for supplier selection: a grey PCA-DEA approach. Operational Research. 2020; ():1-35.

Chicago/Turabian Style

Adel Hatami-Marbini; Siavash Hekmat; Per J. Agrell. 2020. "A strategy-based framework for supplier selection: a grey PCA-DEA approach." Operational Research , no. : 1-35.

Original article
Published: 10 May 2019 in Neural Computing and Applications
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In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm’s efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.

ACS Style

Adel Hatami-Marbini; Saber Saati. Measuring performance with common weights: network DEA. Neural Computing and Applications 2019, 32, 3599 -3617.

AMA Style

Adel Hatami-Marbini, Saber Saati. Measuring performance with common weights: network DEA. Neural Computing and Applications. 2019; 32 (8):3599-3617.

Chicago/Turabian Style

Adel Hatami-Marbini; Saber Saati. 2019. "Measuring performance with common weights: network DEA." Neural Computing and Applications 32, no. 8: 3599-3617.

Journal article
Published: 24 April 2019 in Computers & Industrial Engineering
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The performance evaluation of for-profit and not-for-profit organisations is a unique tool to support the continuous improvement process. Data envelopment analysis (DEA) is literally known as an impeccable technique for efficiency measurement. However, the lack of the ability to attend to ratio measures is an ongoing challenge in DEA. The convexity axiom embedded in standard DEA models cannot be fully satisfied where the dataset includes ratio measures and the results obtained from such models may not be correct and reliable. There is a typical approach to deal with the problem of ratio measures in DEA, in particular when numerators and denominators of ratio data are available. In this paper, we show that the current solutions may also fail to preserve the principal properties of DEA as well as to instigate some other flaws. We also make modifications to explicitly overcome the flaws and measure the performance of a set of operating units for the input- and output orientations regardless of assumed technology. Finally, a case study in the education sector is presented to illustrate the strengths and limitations of the proposed approach.

ACS Style

Adel Hatami-Marbini; Mehdi Toloo. Data envelopment analysis models with ratio data: A revisit. Computers & Industrial Engineering 2019, 133, 331 -338.

AMA Style

Adel Hatami-Marbini, Mehdi Toloo. Data envelopment analysis models with ratio data: A revisit. Computers & Industrial Engineering. 2019; 133 ():331-338.

Chicago/Turabian Style

Adel Hatami-Marbini; Mehdi Toloo. 2019. "Data envelopment analysis models with ratio data: A revisit." Computers & Industrial Engineering 133, no. : 331-338.

Journal article
Published: 01 April 2019 in RAIRO - Operations Research
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Benchmarking is a powerful and thriving tool to enhance the performance and profitabilities of organizations in business engineering. Though performance benchmarking has been practically and theoretically developed in distinct fields such as banking, education, health, and so on, benchmarking of supply chains with multiple echelons that include certain characteristics such as intermediate measure differs from other practices. In spite of incremental benchmarking activities in practice, there is the dearth of a unified and effective guideline for benchmarking in organizations. Amongst the benchmarking tools, data envelopment analysis (DEA) as a non-parametric technique has been widely used to measure the relative efficiency of firms. However, the conventional DEA models that are bearing out precise input and output data turn out to be incapable of dealing with uncertainty, particularly when the gathered data encompasses natural language expressions and human judgements. In this paper, we present an imprecise network benchmarking for the purpose of reflecting the human judgments with the fuzzy values rather than precise numbers. In doing so, we propose the fuzzy network DEA models to compute the overall system scale and technical efficiency of those organizations whose internal structure is known. A classification scheme is presented based upon their fuzzy efficiencies with the aim of classifying the organizations. We finally provide a case study of the airport and travel sector to elucidate the details of the proposed method in this study.

ACS Style

Adel Hatami-Marbini. Benchmarking with network dea in a fuzzy environment. RAIRO - Operations Research 2019, 53, 687 -703.

AMA Style

Adel Hatami-Marbini. Benchmarking with network dea in a fuzzy environment. RAIRO - Operations Research. 2019; 53 (2):687-703.

Chicago/Turabian Style

Adel Hatami-Marbini. 2019. "Benchmarking with network dea in a fuzzy environment." RAIRO - Operations Research 53, no. 2: 687-703.

Journal article
Published: 02 August 2018 in Applied Soft Computing
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Data envelopment analysis (DEA) has been genuinely known as an impeccable technique for efficiency measurement. In practice, since many production systems such as broadcasting companies, banking and R&D activities include two processes connected in series, we have need of utilizing two-stage DEA models to identify the sources of inefficiency and explore in turn appropriate options for improving performance. The lack of the ability to generate the actual weights is not only an ongoing challenge in traditional DEA models, it can have serious repercussion for the contemporary DEA models (e.g., two-stage DEA). This paper presents a common-weights method for two-stage structures that allows us to consider equality of opportunity in a fuzzy environment when evaluating the system efficiency and the component process efficiencies. The proposed approach first seeks upper bounds on factor weights and then determines a set of common weights by a single linear programming problem. We illustrate the approach with a data set taken from the literature.

ACS Style

Adel Hatami-Marbini; Saber Saati. Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: A common-weights approach. Applied Soft Computing 2018, 72, 156 -165.

AMA Style

Adel Hatami-Marbini, Saber Saati. Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: A common-weights approach. Applied Soft Computing. 2018; 72 ():156-165.

Chicago/Turabian Style

Adel Hatami-Marbini; Saber Saati. 2018. "Efficiency evaluation in two-stage data envelopment analysis under a fuzzy environment: A common-weights approach." Applied Soft Computing 72, no. : 156-165.

Original paper
Published: 05 June 2018 in Central European Journal of Operations Research
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Two-stage data envelopment analysis (TsDEA) models evaluate the performance of a set of production systems in which each system includes two operational stages. Taking into account the internal structures is commonly found in many situations such as seller-buyer supply chain, health care provision and environmental management. Contrary to conventional DEA models as a black-box structure, TsDEA provides further insight into sources of inefficiencies and a more informative basis for performance evaluation. In addition, ignoring the qualitative and imprecise data leads to distorted evaluations, both for the subunits and the system efficiency. We present the fuzzy input and output-oriented TsDEA models to calculate the global and pure technical efficiencies of a system and sub-processes when some data are fuzzy. To this end, we propose a possibilistic programming problem and then convert it into a deterministic interval programming problem using the α-level based method. The proposed method preserves the link between two stages in the sense that the total efficiency of the system is equal to the product of the efficiencies derived from two stages. In addition to the study of technical efficiency, this research includes two further contributions to the ancillary literature; firstly, we minutely discuss the efficiency decompositions to indicate the sources of inefficiency and secondly, we present a method for ranking the efficient units in a fuzzy environment. An empirical illustration is also utilised to show the applicability of the proposed technique.

ACS Style

Adel Hatami-Marbini; Saber Saati; Seyed Mojtaba Sajadi. Efficiency analysis in two-stage structures using fuzzy data envelopment analysis. Central European Journal of Operations Research 2018, 26, 909 -932.

AMA Style

Adel Hatami-Marbini, Saber Saati, Seyed Mojtaba Sajadi. Efficiency analysis in two-stage structures using fuzzy data envelopment analysis. Central European Journal of Operations Research. 2018; 26 (4):909-932.

Chicago/Turabian Style

Adel Hatami-Marbini; Saber Saati; Seyed Mojtaba Sajadi. 2018. "Efficiency analysis in two-stage structures using fuzzy data envelopment analysis." Central European Journal of Operations Research 26, no. 4: 909-932.

Journal article
Published: 01 June 2018 in Omega
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The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Efficiency analyses are crucial to managerial competency for evaluating the degree to which resources are consumed in the production process of gaining desired services or products. Among the vast available literature on performance analysis, Data Envelopment Analysis (DEA) has become a popular and practical approach for assessing the relative efficiency of Decision-Making Units (DMUs) which employ multiple inputs to produce multiple outputs. However, in addition to inputs and outputs, some situations might include certain factors to simultaneously play the role of both inputs and outputs. Contrary to conventional DEA models which account for precise values for inputs, outputs and dual-role factors, we develop a methodology for quantitatively handling imprecision and uncertainty where a degree of imprecision is not trivial to be ignored in efficiency analysis. In this regard, we first construct a pair of interval DEA models based on the pessimistic and optimistic standpoints to measure the interval efficiencies where some or all observed inputs, outputs and dual-role factors are assumed to be characterized by interval measures. The optimal multipliers associated with the dual-role factors are then used to determine whether a factor is designated as an output, an input, or is in equilibrium even though the status of the dual-role factors may not be unique based upon the pessimistic and optimistic standpoints. To deal with the problem, we present a new model which integrates both pessimistic and optimistic models. The integrated model enables us to identify a unique status of each imprecise dual-role factor as well as to develop a structure for calculating an optimal reallocation model of each dual-role factor among the DMUs. As another method to investigate the role for dual-role factors, we introduce a fuzzy decision-making model which evaluates all DMUs simultaneously. We finally present an application to a data set of 20 banks to showcase the applicability and efficacy of the proposed procedures and algorithm

ACS Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. Dual-role factors for imprecise data envelopment analysis. Omega 2018, 77, 15 -31.

AMA Style

Mehdi Toloo, Esmaeil Keshavarz, Adel Hatami-Marbini. Dual-role factors for imprecise data envelopment analysis. Omega. 2018; 77 ():15-31.

Chicago/Turabian Style

Mehdi Toloo; Esmaeil Keshavarz; Adel Hatami-Marbini. 2018. "Dual-role factors for imprecise data envelopment analysis." Omega 77, no. : 15-31.

Journal article
Published: 01 June 2018 in Computers & Industrial Engineering
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The range directional model (RDM) relaxes the assumption of non-negativity of inputs and outputs in the conventional data envelopment analysis (DEA) with the aim of evaluating the efficiency of a decision-making unit (DMU) when some data are negative. Although the concept of super-efficiency in the RDM contributes to enhancing discriminatory power, the formulated model may lead to the infeasibility problem for some efficient DMUs. In this paper, we modify the super-efficiency RDM (SRDM) model to overcome the infeasibility problem occurring in such cases. Our method leads to a complete ranking of the DMUs with negative data for yielding valuable insights that aid decision makers to better understand the findings from a performance evaluation process. The contribution of this paper is fivefold: (1) we detect the source of infeasibility problems of SRDM in the presence of negative data, (2) the proposed model in this study yields the SRDM measures regardless of feasibility or infeasibility of the model, (3) when feasibility occurs, the modified SRDM model results in the scores that are the same as the original model, (4) we differentiate the efficient units to improve discriminatory power in SRDM, and (5) we provide two numerical examples to elucidate the details of the proposed method.

ACS Style

Adel Hatami-Marbini; Jafar Pourmahmoud; Elnaz Babazadeh. A modified super-efficiency in the range directional model. Computers & Industrial Engineering 2018, 120, 442 -449.

AMA Style

Adel Hatami-Marbini, Jafar Pourmahmoud, Elnaz Babazadeh. A modified super-efficiency in the range directional model. Computers & Industrial Engineering. 2018; 120 ():442-449.

Chicago/Turabian Style

Adel Hatami-Marbini; Jafar Pourmahmoud; Elnaz Babazadeh. 2018. "A modified super-efficiency in the range directional model." Computers & Industrial Engineering 120, no. : 442-449.

Journal article
Published: 01 March 2018 in Computers & Industrial Engineering
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The economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality observations gathered from production systems may be characterized by intervals. For instance, the heat losses of the combined production of heat and power (CHP) systems may be within a certain range, hinging on a wide variety of factors such as external temperature and real-time energy demand. Enriching the current literature independently tackling the two problems; interval data and RTS estimation; we develop an overarching evaluation process for estimating RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Besides, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models. The applicability and efficacy of the developed approach is finally studied through two numerical examples and a case study.

ACS Style

Adel Hatami-Marbini; Zahra Ghelej Beigi; Jens Leth Hougaard; Kobra Gholami. Measurement of returns-to-scale using interval data envelopment analysis models. Computers & Industrial Engineering 2018, 117, 94 -107.

AMA Style

Adel Hatami-Marbini, Zahra Ghelej Beigi, Jens Leth Hougaard, Kobra Gholami. Measurement of returns-to-scale using interval data envelopment analysis models. Computers & Industrial Engineering. 2018; 117 ():94-107.

Chicago/Turabian Style

Adel Hatami-Marbini; Zahra Ghelej Beigi; Jens Leth Hougaard; Kobra Gholami. 2018. "Measurement of returns-to-scale using interval data envelopment analysis models." Computers & Industrial Engineering 117, no. : 94-107.

Original paper
Published: 20 February 2018 in Operational Research
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Data envelopment analysis (DEA) is a well-known non-parametric technique primarily used to estimate radial efficiency under a set of mild assumptions regarding the production possibility set and the production function. The technical efficiency measure can be complemented with a consistent radial metrics for cost, revenue and profit efficiency in DEA, but only for the setting with known input and output prices. In many real applications of performance measurement, such as the evaluation of utilities, banks and supply chain operations, the input and/or output data are often stochastic and linked to exogenous random variables. It is known from standard results in stochastic programming that rankings of stochastic functions are biased if expected values are used for key parameters. In this paper, we propose economic efficiency measures for stochastic data with known input and output prices. We transform the stochastic economic efficiency models into a deterministic equivalent non-linear form that can be simplified to a deterministic programming with quadratic constraints. An application for a cost minimizing planning problem of a state government in the US is presented to illustrate the applicability of the proposed framework.

ACS Style

Rashed Khanjani Shiraz; Adel Hatami-Marbini; Ali Emrouznejad; Hirofumi Fukuyama. Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs. Operational Research 2018, 20, 1863 -1898.

AMA Style

Rashed Khanjani Shiraz, Adel Hatami-Marbini, Ali Emrouznejad, Hirofumi Fukuyama. Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs. Operational Research. 2018; 20 (3):1863-1898.

Chicago/Turabian Style

Rashed Khanjani Shiraz; Adel Hatami-Marbini; Ali Emrouznejad; Hirofumi Fukuyama. 2018. "Chance-constrained cost efficiency in data envelopment analysis model with random inputs and outputs." Operational Research 20, no. 3: 1863-1898.

Journal article
Published: 01 May 2017 in Expert Systems with Applications
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We extend weighted MCDEA model based on a lower bound for the weights.We improve discriminating power and weight dispersion simultaneously.The sensitivity analysis of weights and the simulation are implemented.A new DEA-based model is formulated to rank the efficient units. Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries.

ACS Style

Adel Hatami-Marbini; Mehdi Toloo. An extended multiple criteria data envelopment analysis model. Expert Systems with Applications 2017, 73, 201 -219.

AMA Style

Adel Hatami-Marbini, Mehdi Toloo. An extended multiple criteria data envelopment analysis model. Expert Systems with Applications. 2017; 73 ():201-219.

Chicago/Turabian Style

Adel Hatami-Marbini; Mehdi Toloo. 2017. "An extended multiple criteria data envelopment analysis model." Expert Systems with Applications 73, no. : 201-219.

Journal article
Published: 01 March 2017 in Computers & Industrial Engineering
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All the variables are considered fuzzy, including input and output data and efficiency scores.A lexicographic multi-objective linear programming optimization approach is proposed.Fuzzy input and output targets are computed.A super-efficiency fuzzy DEA model is also formulated to rank fuzzy efficient DMUs. There is an extensive literature in data envelopment analysis (DEA) aimed at evaluating the relative efficiency of a set of decision-making units (DMUs). Conventional DEA models use definite and precise data while real-life problems often consist of some ambiguous and vague information, such as linguistic terms. Fuzzy sets theory can be effectively used to handle data ambiguity and vagueness in DEA problems. This paper proposes a novel fully fuzzified DEA (FFDEA) approach where, in addition to input and output data, all the variables are considered fuzzy, including the resulting efficiency scores. A lexicographic multi-objective linear programming (MOLP) approach is suggested to solve the fuzzy models proposed in this study. The contribution of this paper is fivefold: (1) both fuzzy Constant and Variable Returns to Scale models are considered to measure fuzzy efficiencies; (2) a classification scheme for DMUs, based on their fuzzy efficiencies, is defined with three categories; (3) fuzzy input and output targets are computed for improving the inefficient DMUs; (4) a super-efficiency FFDEA model is also formulated to rank the fuzzy efficient DMUs; and (5) the proposed approach is illustrated, and compared with existing methods, using a dataset from the literature.

ACS Style

Adel Hatami-Marbini; Ali Ebrahimnejad; Sebastián Lozano. Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Computers & Industrial Engineering 2017, 105, 362 -376.

AMA Style

Adel Hatami-Marbini, Ali Ebrahimnejad, Sebastián Lozano. Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Computers & Industrial Engineering. 2017; 105 ():362-376.

Chicago/Turabian Style

Adel Hatami-Marbini; Ali Ebrahimnejad; Sebastián Lozano. 2017. "Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach." Computers & Industrial Engineering 105, no. : 362-376.

Journal article
Published: 01 March 2017 in Applied Soft Computing
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Display Omitted We propose three versions of fuzzy TOPSIS for solving group MADM problems.We apply fuzzy set theory to handle the imprecise information in the real-world problems.We take advantage of fuzzy-valued distance and fuzzy ranking method to provide a more rational decision-making process.We apply the proposed methods in the Tehran stock exchange. In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.

ACS Style

Adel Hatami-Marbini; Fatemeh Kangi. An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange. Applied Soft Computing 2017, 52, 1084 -1097.

AMA Style

Adel Hatami-Marbini, Fatemeh Kangi. An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange. Applied Soft Computing. 2017; 52 ():1084-1097.

Chicago/Turabian Style

Adel Hatami-Marbini; Fatemeh Kangi. 2017. "An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange." Applied Soft Computing 52, no. : 1084-1097.

Novel developments in dea
Published: 30 January 2017 in Annals of Operations Research
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The non-Archimedean epsilon \(\varepsilon \) is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of \(\varepsilon \) can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs when the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to re-shape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach.

ACS Style

Adel Hatami-Marbini; Per J. Agrell; Hirofumi Fukuyama; Kobra Gholami; Pegah Khoshnevis. The role of multiplier bounds in fuzzy data envelopment analysis. Annals of Operations Research 2017, 250, 249 -276.

AMA Style

Adel Hatami-Marbini, Per J. Agrell, Hirofumi Fukuyama, Kobra Gholami, Pegah Khoshnevis. The role of multiplier bounds in fuzzy data envelopment analysis. Annals of Operations Research. 2017; 250 (1):249-276.

Chicago/Turabian Style

Adel Hatami-Marbini; Per J. Agrell; Hirofumi Fukuyama; Kobra Gholami; Pegah Khoshnevis. 2017. "The role of multiplier bounds in fuzzy data envelopment analysis." Annals of Operations Research 250, no. 1: 249-276.

Journal article
Published: 01 January 2017 in Journal of Cleaner Production
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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

ACS Style

Adel Hatami-Marbini; Per J. Agrell; Madjid Tavana; Pegah Khoshnevis. A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. Journal of Cleaner Production 2017, 142, 2761 -2779.

AMA Style

Adel Hatami-Marbini, Per J. Agrell, Madjid Tavana, Pegah Khoshnevis. A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing. Journal of Cleaner Production. 2017; 142 ():2761-2779.

Chicago/Turabian Style

Adel Hatami-Marbini; Per J. Agrell; Madjid Tavana; Pegah Khoshnevis. 2017. "A flexible cross-efficiency fuzzy data envelopment analysis model for sustainable sourcing." Journal of Cleaner Production 142, no. : 2761-2779.