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Abstract: The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project. [Copyright &y& Elsevier]

Abstract: The paper tackles the problem of robust fault detection using Takagi–Sugeno neuro-fuzzy (N-F) models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such an approach is corrupted by model uncertainty due to the fact that in real applications there exists a model–reality mismatch. In order to ensure reliable fault detection, the adaptive threshold technique is used to deal with the problem. The paper focuses also on the N-F model design procedure. The bounded-error approach is applied to generate rules for the model using available data. The proposed algorithms are applied to fault detection in a valve that is a part of the technical installation at the Lublin sugar factory in Poland. Experimental results are presented in the final part of the paper to confirm the effectiveness of the method. [Copyright &y& Elsevier]

In recent decades, technological advances coupled with research efforts have made possible to develop very complex Decision Support Systems (DSSs) able to exhibit highly sophisticated reasoning capabilities in order to improve clinical decision-making, and, thus, promote more efficient care practices. One of the most significant factors influencing, and in particular limiting, the adoption of clinical DSSs is represented by the modality of representation and computerization of clinical guidelines in form of patient-specific recommendations. Until now, many knowledge representation formalisms have been developed, mainly focused on time-oriented guidelines. However, they can generate an unrealistic over-simplification of reality, since they are not able to completely handle uncertainty and imprecision typically affecting clinical guidelines. In this respect, this paper proposes a novel fuzzy framework expressly thought for building guideline-based DSSs, by efficiently modelling and handling the peculiarities of clinical knowledge affected by uncertainty and imprecision and encoded in the form of guidelines. This framework has been devised with the aim of: (i) offering a set of patterns for easily inserting and editing clinical recommendations belonging to a guideline as a group of one or more fuzzy rules expressing positive evidence and one fuzzy ELSE rule including negative evidence; (ii) defining a set of Fuzzy Guideline Systems (FGSs), one for each guideline encoded, characterized by ad-hoc configurations for the mathematical operators necessary to evaluate rules and generate the outcome expected; (iii) implementing a multi-level inference scheme able to treat different FGSs as a whole and efficiently enable their interconnection, i.e. the chaining among the groups of fuzzy rules belonging to each FGS; (iv) exposing a set of graphical facilities for guiding the definition of fuzzy rules to be embedded into a clinical DSS and enabling their automatic encoding and execution by using an XML-based machine executable language. A usability evaluation has been performed, showing a good satisfaction of medical users with respect to the framework implemented, and, thus, proving both its feasibility and usefulness. [ABSTRACT FROM AUTHOR]

Interval-valued belief structures, as an extension of belief structures in classical evidence theory, are developed for better exploitation of uncertain and imprecise information. From the point of view of information theory, uncertainty measure of an interval-valued belief structure is critically important for information processing. However, it is still an open issue to measure its uncertainty. Besides discord and non-specificity, which hide in a precise belief structure, we claim that fuzziness is also associated with an interval-valued belief structure. In this paper, axiomatic requirements for uncertainty measure of interval-valued belief structure are defined. Then an uncertainty measure is proposed to measure the information conveyed by interval-valued belief structures. Its properties are mathematically proved. Finally, numerical experiments are employed to illustrate the performance of the proposed uncertainty measure. It is illustrated that the proposed uncertainty measure is sensitive to the change of belief structures, which might have beneficial effects on decision making. [ABSTRACT FROM AUTHOR]

Abstract: Fuzzy AHP is a hybrid method that combines Fuzzy Set Theory and AHP. It has been developed to take into account uncertainty and imprecision in the evaluations. Fuzzy Set Theory requires the definition of a membership function. At present, there are no indications of how these membership functions can be constructed. In this paper, a way to calibrate the membership functions with comparisons given by the decision-maker on alternatives with known measures is proposed. This new technique is illustrated in a study measuring the most important factors in selecting a student current account. [Copyright &y& Elsevier]

Abstract: We consider the issue of warehouse evaluation towards successful logistic and supply chain management. Suppose a company has managed a chain of owned warehouses, and now this company is in need of acquiring some new and profitable warehouse adding to its operation chain. A key business decisions here is how to choose the most profitable warehouses from a number of potential warehouses. In reality, the challenge is that the future profitability is unpredictable. Therefore, it is infeasible to rank potential warehouses directly for choice. To address such a problem, this paper proposes a new rule-based decision model. This model includes the following characteristics: (i) decision information is provided via interval-valued intuitionistic fuzzy values; (ii) multiple experts as a group of decision makers are involved; (iii) both subjective evaluations from experts and objective data of historical profitability are employed; (iv) both certain and uncertain information are exploited. The core decision mechanism is, making use of uncertain information of owned warehouses, to induce a collection of “if…then…”rules, and subsequently to exploit these rules for prediction of preference orders of all potential warehouses. Therein, we develop and integrate multiple techniques for the purposes of (a) aggregation of uncertain information; (b) construction of pairwise comparison; (c) induction of certain and uncertain rules; and (d) decision rules exploitation. We finally elaborate our discussion with a numerical example illustrating the application of the proposed decision mechanism to supply-chain domain problems. [Copyright &y& Elsevier]

Abstract: In order to handle uncertain information, this paper proposes evidential cognitive maps (ECMs), similar to the fuzzy cognitive maps (FCMs). ECMs are uncertain-graph structures for representing causal reasoning through the combination of cognitive maps and Dempster Shafer evidence theory. The framework of ECMs is developed in detail and an application to socio-economic model is used to illustrate the application of the proposed methodology. [Copyright &y& Elsevier]

Abstract: This paper presents an intelligent decision support system for evaluating and selecting specific ships under uncertainty. A task-oriented procedure is developed for determining the relative importance of the evaluation and selection criteria with respect to a specific shipping task. A fuzzy multicriteria analysis algorithm is developed for determining the overall performance of each ship across all the selection criteria and their associated sub-criteria. An intelligent decision support system capable of integrating the developments above is proposed for facilitating the ship evaluation and selection process. An example is presented to demonstrate the effectiveness of the proposed intelligent decision support system. [Copyright &y& Elsevier]

Abstract: Material selection is a complex process, since the process includes many criteria, determination of criteria weight and the most important factor is that the selection of appropriate criterion. The last factor indicates that the criterion must be selected in a manner, such that the selection based upon the known material parameters and the requirements of the application. Therefore the material selection can be done using MCDM (Multi Criterion Decision Making) methods. Since the inputs provided by the decision maker in linguistic manner, there is a possible chance of getting incomplete problems. So in order to overcome the problem, the inputs could be provided as fuzzy numbers. Since fuzzy set represents the uncertainty in human perceptions. In this paper, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian, means Multicriteria Optimisation and Compromise Solution) has been used a MCDM tool for the selection of alternate material for instrument panel used in electric car and in order to evaluate this selection process in fuzzy environment, fuzzy based VIKOR is used. In addition to the fuzzy VIKOR method, the environmental impacts are also considered and compared for the four materials. The results achieved in both the assessment, showed that Polypropylene could be an alternate material for the instrument panel. The objective of this study is to develop a rational method to select the best material for an application based upon known material parameters and the requirements of the application. [Copyright &y& Elsevier]

Abstract: Data envelopment analysis (DEA) as introduced by is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input–output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on α-cut. One drawback of the α-cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of “local α-level” to develop a multi-objective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method. [Copyright &y& Elsevier]

Abstract: Sustainable transportation systems are the need of modern times. There has been an unexpected growth in the number of transportation activities over years and the trend is expected to continue in the coming years. This has obviously associated environmental costs like air pollution, noise, etc. which is degrading the quality of life in modern cities. To cope us this crisis, municipal administrations are investing in sustainable transportation systems that are not only efficient, robust and economical but also friendly towards environment. The challenge before the transportation decision makers is how to evaluate and select such sustainable transportation systems. In this paper, we present a multicriteria decision making approach for selecting sustainability transportation systems under partial or incomplete information (uncertainty). The proposed approach comprises of three steps. In step 1, we identify the criteria for sustainability assessment of transportation. In step 2, experts provide linguistic ratings to the potential alternatives against the selected criteria. Fuzzy TOPSIS is used to generate aggregate scores for sustainability assessment and selection of best alternative. In step 3, sensitivity analysis is performed to determine the influence of criteria weights on the decision making process. A numerical illustration is provided to demonstrate the applicability of the approach. The strength of the proposed work is its practical applicability and the ability to generate good quality solutions under uncertainty. [Copyright &y& Elsevier]

Abstract: This paper emphasizes the use of fuzzy sets for incorporating objective and subjective uncertainties to address coevolutionary alignment of a suite of water resources redistribution alternatives in a transboundary channel–reservoir system. The highlighted decision making complexity arises from the interactions between two neighboring water systems (i.e., the Tseng-Wen and Kao-Ping River Basins, South Taiwan) where a pending diversion plan has been under intensive debate for over a decade. While the local stakeholders make uncertain science linked with uncertain politics resulting in endless delay of the diversion plan, the environmental advocacy groups stress the increasing concern of loss of biological integrity due to changes of land use when sharing water resources across the boundary. Consequently, there is a need to generate a novel integration that enables us to consider a vast number of internal weirs, water intakes, reservoirs, drainage ditches, and transfer pipelines within the basin and bring out the connectivity via diversion between these two neighboring river basins under uncertainty. To explore the managerial implications with varying risk perception and risk attitude, four types of fuzzy operators tailored for the fuzzy multi-objective decision analysis depict greater flexibility in representing the complexity of possible trade-offs among those alternatives. These trade-offs in the multi-objective evaluation context are constrained by physical, chemical, socioeconomic, managerial, and technical factors reflecting the needs for adaptive water resources management. Findings indicates that the use of fuzzy operators is instructive, which could provide unique guidance for enlightening the potential barriers in sustainable water resources management at the regional scale. [Copyright &y& Elsevier]