A fuzzy MCDM approach for stock selection

A fuzzy MCDM approach is applied to the stock selection problem, where the proposed approach can deal with qualitative information in addition to quantitative information. A hierarchy of major–sub criteria is then established to reduce the dependence between criteria. The ratings of alternatives versus qualitative sub-criteria and the weights of major- and sub-criteria are assessed in linguistic terms represented by fuzzy numbers. Each sub-criterion is in a benefit, cost, or balanced nature. New standardization methods for fuzzy numbers in the cost and balanced nature are presented. The algorithms of membership functions of the final aggregation are completely developed instead of approximation. The final aggregations in fuzzy numbers are then defuzzified to crisp values in order to rank the performance of alternatives. Moreover, the ratio of market price to performance (PP) is suggested to filter the over/under-pricing of alternatives. A set of buying/selling strategies are recommended according to the performance and PP. An empirical example then demonstrates the processing of the proposed approach.     

A QFD-based fuzzy MCDM approach for supplier selection

Supplier selection is a highly important multi-criteria group decision making problem, which requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. In this paper, a fuzzy multi-criteria group decision making approach that makes use of the quality function deployment (QFD) concept is developed for supplier selection process. The proposed methodology initially identifies the features that the purchased product should possess in order to satisfy the company’s needs, and then it seeks to establish the relevant supplier assessment criteria. Moreover, the proposed algorithm enables to consider the impacts of inner dependence among supplier assessment criteria. The upper and the lower bounds of the weights of supplier assessment criteria and ratings of suppliers are computed by using the fuzzy weighted average (FWA) method. The FWA method allows for the fusion of imprecise and subjective information expressed as linguistic variables or fuzzy numbers. The method produces less imprecise and more realistic overall desirability levels, and thus it rectifies the problem of loss of information. A fuzzy number ranking method that is based on area measurement is used to obtain the final ranking of suppliers. The computational procedure of the proposed framework is illustrated through a supplier selection problem reported in an earlier study.     

A naïve approach for solving MCDM problems: the GUESS method

We present a simple multiple criteria decision making solution technique called the GUESS method. This method has been used in MCDM experiments where different solution methods have been compared. The GUESS method is an interactive solution method designed to be used with continuous multiple criteria decision problems. It is based on a class of solution methods called reference point methods whereby the decision maker generates a sequence of solutions based on a sequence of guesses or aspiration vectors. In this paper we explain the basic concepts of the GUESS method and describe the algorithm of the solution method. An illustrative example is provided, along with a discussion of the method from a behavioural decision making perspective.     

The Runge–Kutta method for hybrid fuzzy differential equations

In this paper we study numerical methods for addressing hybrid fuzzy differential equations by an application of the Runge–Kutta method for fuzzy differential equations using the Seikkala derivative. We state a convergence result and give a numerical example to illustrate the theory.     

A hybrid evolutionary learning algorithm for TSK-type fuzzy model design

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the compact genetic algorithm (CGA) to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods.     

An instrument for measuring supply chain flexibility for the textile and clothing companies

Supply chain flexibility (SCF) represents the capability of firms to respond to unanticipated changes in customer needs and competitor actions. Given the growing research interest in flexibility strategies, the development of a valid and reliable instrument to measure organizational responses toward environmental uncertainties or risks is imperative. However, no systematic and scientific research has been conducted to develop such an instrument. The present study adopts a comprehensive and rigorous procedure to develop a multifaceted scale for SCF through an empirical investigation. The results of a confirmatory factor analysis suggest that SCF can be operationalized as a second-order factor model comprising four dimensions, namely: sourcing flexibility, operating system flexibility, distribution flexibility, and information system flexibility. A series of goodness-of-fit indices further demonstrates that this scale is internally consistent, reliable, and valid. The various findings suggested in the present study provide a more succinct picture of SCF, and the well-validated scale could be used as a basis for further research and theoretical groundwork in the field of supply chain management.     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

A hybrid method for sound-hard obstacle reconstruction

We are interested in solving the inverse problem of acoustic wave scattering to reconstruct the position and the shape of sound-hard obstacles from a given incident field and the corresponding far field pattern of the scattered field. The method we suggest is an extension of the hybrid method for the reconstruction of sound-soft cracks as presented in [R. Kress, P. Serranho, A hybrid method for two-dimensional crack reconstruction, Inverse Problems 21 (2005) 773–784] to the case of sound-hard obstacles. The designation of the method is justified by the fact that it can be interpreted as a hybrid between a regularized Newton method applied to a nonlinear operator equation with the operator that maps the unknown boundary onto the solution of the direct scattering problem and a decomposition method in the spirit of the potential method as described in [A. Kirsch, R. Kress, On an integral equation of the first kind in inverse acoustic scattering, in: Cannon, Hornung (Eds.), Inverse Problems, ISNM, vol. 77, 1986, pp. 93–102. Since the method does not require a forward solver for each Newton step its computational costs are reduced. By some numerical examples we illustrate the feasibility of the method.     

A hybrid metaheuristic method for the Maximum Diversity Problem

The Maximum Diversity Problem (MDP) consists in selecting a subset of m$m$ elements from a given set of n$n$ elements (n>m$n>m$) in such a way that the sum of the pairwise distances between the m$m$ chosen elements is maximized. We present a hybrid metaheuristic algorithm (denoted by MAMDP) for MDP. The algorithm uses a dedicated crossover operator to generate new solutions and a constrained neighborhood tabu search procedure for local optimization. MAMDP applies also a distance-and-quality based replacement strategy to maintain population diversity. Extensive evaluations on a large set of 120 benchmark instances show that the proposed approach competes very favorably with the current state-of-art methods for MDP. In particular, it consistently and easily attains all the best known lower bounds and yields improved lower bounds for 6 large MDP instances. The key components of MAMDP are analyzed to shed light on their influence on the performance of the algorithm.     

A hybrid method for computing Lyapunov exponents

In this paper we propose a numerical method for computing all Lyapunov coefficients of a discrete time dynamical system by spatial integration. The method extends an approach of Aston and Dellnitz (Comput Methods Appl Mech Eng 170:223–237, 1999) who use a box approximation of an underlying ergodic measure and compute the first Lyapunov exponent from a spatial average of the norms of the Jacobian for the iterated map. In the hybrid method proposed here, we combine this approach with classical QR-oriented methods by integrating suitable R-factors with respect to the invariant measure. In this way we obtain approximate values for all Lyapunov exponents. Assuming somewhat stronger conditions than those of Oseledec’ multiplicative theorem, these values satisfy an error expansion that allows to accelerate convergence through extrapolation. W.-J. Beyn and A. Lust was supported by CRC 701 ‘Spectral Analysis and Topological Methods in Mathematics’. The paper is mainly based on the PhD thesis [27] of A. Lust.     

A hybrid graph-genetic method for domain decomposition

A new method is developed for finite element (FE) domain decomposition. This method employs a hybrid graph-genetic algorithm for graph partitioning and correspondingly bisects finite element (FE) meshes.

A weighted incidence graph is first constructed for the FE mesh, denoted by G₀. A coarsening process is then performed using heavy-edge matching. A sequence of such operations is employed in “n” steps, which leads to the formation of G_n with a size suitable for genetic algorithm applications.

Hereafter, G_n is bisected using conventional genetic algorithm. The shortest route tree algorithm is used for the formation of the initial population in genetic algorithm. Then an uncoarsening process is performed and the results are transferred to the graph G_n−1. The initial population for genetic algorithm on G_n−1is constructed from the results of G_n. This process is repeated until G₀ is obtained in the uncoarsening operation. Employing the properties of G₁, the graph G₀ is bisected by the genetic algorithm.     

A hybrid method for quantum global optimization

This paper gives a quantum algorithm for global optimization. The heart of such approaches employ Grover’s database search (1996; Phys Rev Lett 79(23):4709–4712, 1997a; 79(2):325–328, 1997b). Chi and Kim (1998) show that when the phases of the generalized Grover database search operator are optimally chosen, it is capable of finding a solution by a single query. To apply this method to global optimization requires knowledge of the number of marked points m to calculate the optimal phases, but this value is seldom known. This paper focuses on overcoming this hurdle by showing that an estimate of the optimal phases can be found and used to replace the optimal phases while maintaining a high probability of finding a solution. Merging this finding with a recently discovered dynamic quantum global optimization algorithm (BBW2D) that reduces the problem to finding successively improving regions using Grover’s search, we present a hybrid method that improves the efficiency and reduces the variance of the search algorithm when empirically compared to other existing quantum search algorithms.     

Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems

The concept of globalization has been prosperous in the past decades while manufacturing as well as logistics have already become one of the most significant issues in the globalization era. However, while modern globalized firms are leveraging both global manufacturing resources as well as logistics systems for pursuing higher quality, lower cost as well as product differentiation, how to evaluate, selecting an appropriate global manufacturing strategy by considering issues from both aspects of global manufacturing as well as logistics has become one of the most critical and difficult issues. Moreover, how the chosen intertwined global manufacturing as well as logistics system is to be optimized so that the aspired level of the global manufacturing system can be achieved have few been addressed. Thus, this research aims to resolve the above mentioned global manufacturing and logistics strategy selection as well as system reconfiguration issue. A Decision Making Trial and Evaluation Laboratory (DEMATEL) technique based novel multiple criteria decision making (MCDM) method with Analytic Network Process (ANP), Grey Relational Analysis (GRA) as well as VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) will be proposed for selecting and re-configuring the aspired global manufacturing and logistics system. An empirical study based on the global manufacturing and logistics system design of a semiconductor company will be provided for verifying the effectiveness of this proposed methodology.     

A computational method for fuzzy Volterra-Fredholm integral equations

In this paper, we will study the application of homotopy perturbation method for solving fuzzy nonlinear Volterra-Fredholm integral equations of the second kind. Some examples are proposed to exhibit the efficiency of the method.     

A method for solving a fuzzy linear programming

In this paper a fuzzy linear programming problem is presented. Then using the concept of comparison of fuzzy numbers, by the aid of the Mellin transform, we introduce a method for solving this problem.     

A new parametric method for ranking fuzzy numbers

Ranking fuzzy numbers is important in decision-making, data analysis, artificial intelligence, economic systems and operations research. In this paper, to overcome the limitations of the existing studies and simplify the computational procedures an approach to ranking fuzzy numbers based on α$\alpha$-cuts is proposed. The approach is illustrated by numerical examples, showing that it overcomes several shortcomings such as the indiscriminative and counterintuitive behavior of existing fuzzy ranking approaches.     

A general method for calculating functions of fuzzy numbers

This paper discusses the calculation of functions of fuzzy numbers. Our general approach follows that of the fuzzy weighted algorithm (FWA) of Wong and Dong. However, by developing a classification scheme for functions and their arguments, we show that there are many cases for which the FWA, and all other published methods, will give wrong results. We suggest an alternative approach which will work in all cases. For certain restricted classes of functions, we develop methods which require less computation than the FWA.     

A simple method for computation of fuzzy linear regression

We propose a new method for computation of fuzzy regression that is simple and gives good solutions. We consider two cases: First, when only the dependent variable is fuzzy, our approach is given and is compared with those suggested in the literature. Secondly, when both dependent and independent variables are fuzzy, our approach is extended and compared with those given in the literature. In each case, a simple example is used to compare the competing approaches.