Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA a-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. In addition, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
This paper proposes a parametric programming approach to address the notion of the time value of delays in the presence of mixed (random and fuzzy) uncertainties that result from unreliable systems. To consider different types of delay time values, the system states are appropriately and carefully identified and defined, and a cost-based fuzzy decision model that incorporates several unreliability factors is constructed. Then, the proposed model is transformed into a pair of nonlinear programs parameterized by the possibility level alpha to identify the lower and upper bounds on the minimal total cost per unit time at alpha and thus construct the membership function. To provide analytical expressions, a special case with analytical results is also presented. In contrast to existing studies, the results derived from the proposed solution procedure conserve the fuzziness of the input information, representing a significant difference from the crisp results obtained using approaches based on probability theory. The results indicate that the proposed approach can provide more precise information to managers and improve decision-making in practical system design. (C) 2016 Elsevier B.V. All rights reserved.
This paper investigates uncertainties in complex supply chain situations and proposes a fuzzy-based decision support model for determining the chance of meeting on-time delivery in a complex supply chain environment. It integrates fuzzy logic principles and unitary structure-based supply chain model and enables addressing uncertainties associated with key inputs of on-time delivery performance for effective decision making process. The proposed pragmatic model deals with the fuzziness of the key inputs including, variations in demand forecasting, materials shortages and distribution lead time, and combines a fuzzy reasoning approach for monitoring on-time delivery of finished products. In systematically dealing with the uncertainties of complex supply chains, this model supports the minimizing of business losses that result from penalties and customer dissatisfaction, and the consequent reduced market share. Application of the proposed model is illustrated using a textile industry case study.
Many multiple attribute decision analysis problems include both quantitative and qualitative attributes with various kinds of uncertainties such as ignorance, fuzziness, interval data, and interval belief degrees. An evidential reasoning (ER) approach developed in the 1990s and in recent years can be used to model these problems. In this paper, the ER approach is extended to group consensus (GC) situations for multiple attributive group decision analysis problems. In order to construct and check the GC, a compatibility measure between two belief structures is developed first. Considering two experts' utilities, the compatibility between their assessments is naturally constructed using the compatibility measure. Based on the compatibility between two experts' assessments, the GC at a specific level that may be the attribute level, the alternative level, or the global level, can be constructed and reached after the group analysis and discussion within specified times. Under the condition of GC, we conduct a study on the forming of group assessments for alternatives, the achievement of the aggregated utilities of assessment grades, and the properties and procedure of the extended ER approach. An engineering project management software selection problem is solved by the extended ER approach to demonstrate its detailed implementation process, and its validity and applicability.
This paper presents a fuzzy lung allocation system (FLAS) in order to determine which potential recipients would receive a lung for transplantation when it becomes available in the USA. The developed system deals with the vagueness and fuzziness of the decision making of the medical experts in order to achieve accurate lung allocation processes in terms of transplant survival time and functional status after transplantation. The proposed approach is based on a real data set from the United Network for Organ Sharing (UNOS) to investigate how well it mimics the experience of transplant physicians in the field of lung allocation. The results are very promising in terms of both prediction accuracy (with an R-2 value of 83.2 percent and an overall accuracy of 82.1 percent) along with better interpretation capabilities and hence are superior to the existing techniques in literature. Furthermore, the proposed decision process provides a more effective (i.e. accurate), time-efficient, and systematic decision support tool for this problem with two criteria being considered i.e. graft survival time and functional status after transplantation. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
One task that catering services for high-speed railways (CSHRs) must accomplish is to identify and evaluate potential service stations before the design phase of the distribution system. Fuzzy quality function deployment (F-QFD) is one approach for processing the evaluation scheme by translating the basic requirements described in vague terms into actionable alternatives. However, fuzzy importance ratings obtained using F-QFD might be misleading because the approach does not consider the random fluctuations of the fuzzy importance ratings. This paper first proposes a two-phase robust F-QFD process that is integrated with a robust analysis to consider how the QFD process can interact with both the fuzziness and the randomness found in real-world management. Two indicators that measure absolute and relative robustness are proposed. Second, following the mean-end-chain concept, this paper considers the close relationship between the two phases by developing a set of robustness-oriented fuzzy goal programming (RFGP) models to determine the locations of potential service stations. Two robustness indicators are introduced into the two-phase RFGP models to mitigate the adverse effect of random fluctuations. To address the fuzzy and binary variables in the model of phase 2, a hybrid cross-entropy method (HCEA) is developed. The overall framework is termed two-phase robust F-QFD based on the mean-end-chain (MEC) concept (R2-F-QFD-MEC). A series of computational experiments demonstrate both the effectiveness of the framework and the benefits of the robustness-oriented F-QFD. A case study regarding 33 potential service stations along the Beijing-Shanghai high-speed corridor is used to demonstrate the applicability of the method. (C) 2017 Elsevier B.V. All rights reserved.