Narrow Results

This paper presents a subjective safety and cost based modeling approach for evaluating safety requirements specifications in the study of safety-critical software. In the approach fuzzy set modeling and evidential reasoning are combined to assess both the safety associated with and the cost incurred in each option of safety requirements specifications. Both safety and cost estimates are combined to obtain the preference degree associated with each option of safety requirements specifications for selecting the best one. An example is presented to demonstrate the proposed approach for safety based on decision-making in safety requirements analysis of safety critical software development.

A fuzzy software reliability model is proposed where the time intervals between the software failures are taken as the fuzzy variables governed by a membership function. The model takes into account the following assumptions: new faults may be introduced into the software during debugging processes, the number of faults removed after a failure may be greater than one, and there is a growth of human experience during debugging. The model can be considered as an extension of the model developed by Cai, Wen and Zhang. An efficient algorithm is presented for estimating parameters of the model. The numerical examples validate the proposed model.

Manufacture of a product in a desired shape and size with the desired characteristics and properties depends not only on the design of the product but also on the selection of an appropriate manufacturing process(es), which requires knowledge about the various alternatives available. This paper describes the process selection methodology for unconventional or advanced machining processes (AMPs), along with a preliminary selection strategy for basic type of manufacturing process. These two tasks along with parametric optimization form the core of an integrated and automated process planning system for an advanced machining environment. The process selection methodology for the AMPs is based on elimination and ranking strategy. To facilitate the process selection, AMPs have been reclassified or regrouped according to their material application capabilities, shape or manufacturing feature generating capabilities, operational capabilities, economic aspects, and environmental aspects. The described process selection methodologies for basic manufacturing process and AMPs, have been implemented in a software named as APSPOAMPS (Automated Process Selection and Parametric Optimization of AMPs). This paper also describes the proposed reclassifications of AMPs, implementation details of the developed software along with the two test examples.

The application of additive manufacturing (AM) has increased exponentially in recent years. Industries are keen to explore this innovative technology but are apprehensive about the high processing cost of the process. Hence, it is crucial to carry out a cost analysis of the process. This paper presents an approach to compare the costs of an AM process (selective laser sintering (SLS)) and a traditional manufacturing process (injection molding (IM)) in the presence of uncertainties. Initially, the deterministic cost models comprising necessary cost variables for SLS and IM are described. The deterministic models are converted to fuzzy set-based models for tackling uncertainties. For this purpose, important uncertain variables are treated as fuzzy members and fuzzy arithmetic is employed. Only linear triangular fuzzy numbers are used in this work. Fuzzy cost estimates produce three values (low, most likely and high estimates) of cost corresponding to membership grades. A methodology to compare two fuzzy costs of the processes is proposed for a variable demand scenario. Concept of fuzzy reliability is suitably utilized and variability in demand is tackled from probability theory. Variable demand is assumed to follow uniform as well as normal probability distributions. The methodology is illustrated with the help of two examples.

We show here some of our results on intuitionistic fuzzy topological spaces. In 1983, K.T. Atanassov proposed a generalization of the notion of fuzzy set: the concept of intuitionistic fuzzy set. D. Çoker constructed the fundamental theory on intuitionistic fuzzy topological spaces, and D. Çoker and other mathematicians studied compactness, connectedness, continuity, separation, convergence and paracompactness in intuitionistic fuzzy topological spaces. Finally, G.-J Wang and Y.Y. He showed that every intuitionistic fuzzy set may be regarded as an L-fuzzy set for some appropriate lattice L. Nevertheless, the results obtained by above authors are not redundant with other for ordinary fuzzy sense. Recently, Smarandache defined and studied neutrosophic sets (NSs) which generalize IFSs. This author defined also the notion of neutrosophic topology. We proved that neutrosophic topology does not generalize the concept of intuitionistic fuzzy topology.

We propose a new method for ranking alternatives represented by Atanassov's intuitionistic fuzzy sets (A-IFSs) which takes into account the amount of information related to an alternative (expressed by a distance from the ideal positive alternative) and the reliability of information (how sure the information is).

Type-1 OWA operator provides us with a new technique for directly aggregating linguistic variables expressing human experts' opinions or preferences by fuzzy sets via OWA mechanism in soft decision making. However, the existing Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, a fast approach, called α-level Approach, is suggested to implement the type-1 OWA operator. Experimental results have shown that the α-level Approach can achieve much higher computing efficiency in performing type-1 OWA operation than the Direct Approach.

Bilevel decision techniques are developed for decentralized decision problems, which may be defined by fuzzy coefficients. Based on a fuzzy linear bilevel (FLBL) model and two FLBL algorithms, this research develops a FLBL decision support system (FLBLDSS). It first introduces a satisfactory-degree-adjustable FLBL model. Then, the system structure and function modules of this FLBLDSS are presented. Finally the key algorithms are illustrated.

Fabric-hand evaluation is one of the key features and measures in textile material selection for fashion design. Fabric-hand evaluation requires considering multiple criteria with in a group of evaluators. The evaluation process often involves fuzziness in the weights of criteria and the judgments of evaluators. This study first develops a multi-level textile material fabric-hand evaluation model. It then proposes a fuzzy multi-criteria group decision-making (FMCGDM) method for the evaluation. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in textile material fabric-hand evaluation.