Informatica

A new method is proposed to solve the interactive group decision making problem in which the preference information takes the form of intuitionistic fuzzy preference relations. Firstly, we aggregate all individual intuitionistic fuzzy preference relations into a collective one. Then, a method to determine the experts’ weights by utilizing the compatibility measures of the individual intuitionistic fuzzy preference relations and the collective one is proposed. Furthermore, a practical interactive procedure is developed, in which the intuitionistic fuzzy association coefficient is used to rank the given alternatives. Finally, this study presents a numerical example to illustrate the availability of the developed approach and compare it to another method.

Statistical modelling plays a central role for any prediction problem of interest. However, predictive models may give misleading results when the data contain outliers. In many real-world applications, it is important to identify and treat the outliers without direct elimination. To handle such issues, a hybrid computational method based on conic quadratic programming is introduced and employed on earthquake ground motion dataset. This method aims to minimize the impact of the outliers on regression estimators as well as handling the nonlinearity in the dataset. Results are compared against widely used parametric and nonparametric ground motion prediction models.

This paper investigates a kind of hybrid multiple attribute decision making (MADM) problems with incomplete attribute weight information and develops a hesitant fuzzy programming method based on the linear programming technique for multidimensional analysis of preference (LINMAP). In this method, decision maker (DM) gives preferences over alternatives by the pair-wise comparison with hesitant fuzzy truth degrees and the evaluation values are expressed as crisp numbers, intervals, intuitionistic fuzzy sets (IFSs), linguistic variables and hesitant fuzzy sets (HFSs). First, by calculating the relative projections of alternatives on the positive ideal solution (PIS) and negative ideal solution (NIS), the overall relative closeness degrees of alternatives associated with attribute weights are derived. Then, the hesitant fuzzy consistency and inconsistency measures are defined. Through minimizing the inconsistency measure and maximizing the consistency measure simultaneously, a new bi-objective hesitant fuzzy programming model is constructed and a novel solution method is developed. Thereby, the weights of attributes are determined objectively. Subsequently, the ranking order of alternatives is generated based on the overall relative closeness degrees of alternatives. Finally, a supplier selection example is provided to show the validity and applicability of the proposed method.

This paper deals with the problem of selecting a suitable design pattern when necessary. The number of design patterns has been rapidly rising, but management and searching facilities appear to be lagging behind. In this paper we will present a platform, which is used to search for suitable design patterns and for design patterns knowledge exchange. We are introducing a novel design pattern proposing approach: the developer no longer searches for an appropriate design pattern, but rather the intelligent component asks the developer questions. We do not want to invest extra effort in terms of maintaining a special expert system. Guided dialogues consist of independent questions from different sources and authors that are automatically combined. The enabling algorithm and formulas are discussed in detail. This paper also presents our comparison with human-created expert systems via a decision tree. Experiments were executed in order to verify our approach performance. The control group used a human-created expert system, while others were given a proposing component to find appropriate design patterns.

We present a new root cause analysis algorithm for discovering the most likely causes of differences found in testing results of two versions of the same software. Problematic points in test and environment attribute hierarchies are presented to a user in a compact way which in turn allows saving time on test result processing. We have proven that for clearly separated problem causes our algorithm gives an exact solution. Practical application of described method is discussed.

We present an algorithm to solve multistage stochastic convex problems, whose objective function and constraints are nonlinear. It is based on the twin-node-family concept involved in the Branch-and-Fix Coordination method. These problems have 0–1 mixed-integer and continuous variables in all the stages. The non-anticipativity constraints are satisfied by means of the twin-node-family strategy.

The 2-tuple linguistic computational model is an important tool to deal with linguistic information. To extend the application of hesitant fuzzy linguistic term sets and avoid information loss, this paper introduces hesitant fuzzy 2-tuple linguistic term sets that are expressed by using several symbolic numbers in $[0\mathrm{,}1]$. Considering the order relationship between hesitant fuzzy 2-tuple linguistic term sets, measures of expected value and variance are defined. Meanwhile, several induced generalized hesitant fuzzy 2-tuple linguistic aggregation operators are defined, by which the comprehensive attribute values of alternatives can be obtained. Then, models for the optimal weight vector on a decision maker set, on an attribute set and on their ordered sets are constructed, respectively. Furthermore, an approach to multi-granularity group decision making with hesitant fuzzy linguistic information is developed. Finally, an example is selected to illustrate the feasibility and practicality of the proposed procedure.

This paper presents minimum mean square error (MMSE) estimators for mean life and failure rate of Exponential distribution model based on failure censored step-stress accelerated life-testing (SSALT) data. The MMSE estimators are drived by revising the corresponding unbiased estimators in terms of mean square error (MSE). Two theorems prove mathematically the fact that MSE of the resulting MMSE estimators are smaller than that of the corresponding unbiased estimators. The results show that the MMSE estimators are more efficient than the unbiased estimators and maximum likelihood estimators (MLEs) in small and moderate sample size.

In the fierce global competition, cost, quality and customer satisfaction appears to be utmost significant. Flexible manufacturing systems (FMS) have a great potential in manufacturing both cost effective and customer based products. These systems bring us flexibility, but this flexibility accompanies cost and time. Thus, selecting suitable FMS necessitates excessive attention. The problem of FMS selection and evaluation becomes more difficult when facing multi FMSs selection problem. In this paper, we propose an integrated approach to find a suitable combination of FMSs in a multi FMSs decision making problem. Each FMS has several alternatives. Therefore, there are many possible solutions for this problem. We first identify the objective and subjective attributes. Second, Grey system theory is applied to deal with the incomplete and uncertain information of subjective data, and the objective data are extracted from simulation modelling. A goal-programming model is then utilized to formulate the problem and to assign priorities to the objectives. Finally, a genetic algorithm (FA) based model is applied to solve the combination problem, as the formulated problem is difficult to be solved. The model proposed in this paper determines the most appropriate FMSs combination and facilitates decision making of such a hard problem.