Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

The aim of this paper has twofold: i) to explore the fundamental concepts and methods of neighborhood-based cluster analysis with its roots in statistics and decision theory, ii) to provide a compact tool for researchers. Since DBSCAN is the first method which uses the concept of neighborhood and it has many successors, we started our discussion by exploring it. Then we compared some of the successors of DBSCAN algorithm and other crisp and fuzzy methods on the basis of neighborhood strategy.

It is difficult to make efficient decisions in manufacturing environments due to complexity and uncertainty which one can experience during most of the production phases. Although handling uncertainty is a difficult issue, it must be considered during modeling in order to obtain more realistic solutions for complex problems. One of the flow oriented production systems which is used in manufacturing is parallel assembly lines. They are preferred due to their flexible and productive nature. In this paper, parallel assembly line balancing problem with fuzzy parameters is studied in order to provide more realistic solutions in which the problem data is imprecise. A multi-colony ant algorithm for solving parallel assembly line balancing problems with fuzzy cycle and task times is proposed. Considering task times fuzzy is necessary especially in manual assembly operations. The fuzziness of the cycle time is related to task time variability. The proposed approach is tested on benchmark problems and solutions are presented.

Sampling strategies which have very significant role on examining data characteristics (i.e. imbalanced, small, exhaustive) have been discussed in the literature for the last couple decades. In this study, the sampling problem encountered on small and continuous data sets is examined. Sampling with measured data by employing k-fold cross validation, and sampling with synthetic data generated by fuzzy c-means clustering are applied, and then the performances of genetic programming (GP) and adaptive neuro fuzzy inference system (ANFIS) on these data sets are discussed. Concluding remarks are that when the experimental results are considered, fuzzy c-means based synthetic sampling is more successful than k-fold cross validation while modeling small and continous data sets with ANFIS and GP, so it can be proposed for these type of data sets. Additionally, ANFIS shows slightly better performance than GP when sytnthetic data is employed, but GP is less sensitive to data set and produces ouputs that are narrower range than ANFIS's outputs while k-fold cross validation is employed.

Prediction of daily streamflow in mountainous karst basins by means of deterministic models requires vast amount of data which is often not available. Such predictions are critical for the optimal operation of hydropower plants utilizing streamflow. The powerful prediction tool adaptive neuro-fuzzy interference system (ANFIS) was used for prediction of downstream flow in the Zamanti River Basin. Model input parameters are upstream flow, precipitation and retrospective downstream flow. Four different models constructed and the model results were assessed by using the determination coefficient and the root mean square error. Applied models produced reliable prediction values. Model results reveal an acceptable deviation from observations at stream flows exceeding the average. The prediction success of the applied ANFIS model appears to be promising for streamflow prediction in similar hydrogeological settings.

Clinical practice guidelines are expected to promote more consistent, effective, and efficient medical practices and improve health outcomes, especially if provided in the form of clinical decision support. However, most clinical guidelines, especially when expressed in the form of condition-action recommendations, embody different kinds of structural errors that compromise their practical value. With this respect, this paper presents a novel method for verifying the reliability of condition-action clinical recommendations encoded in the form of fuzzy rules, with the final aim of determining inconsistency, redundancy and incompleteness anomalies in a very simple and understandable fashion. The method is based on general definitions of inconsistency, redundancy and incompleteness for fuzzy clinical rules in terms of similarity between antecedents and consequents, bringing them near the imprecise character of fuzzy decision support systems. A key issue relies on the formalization of fuzzy degrees for these anomalies that can be simply interpreted by the final users as measurements suggesting the modifications to be performed to the clinical rules in order to eliminate or mitigate the existing undesired effects. The method has been profitably assessed on two sample sets of clinical rules: the first one identified from the relevant clinical literature and the second one extracted automatically by machine learning techniques from a widely known clinical database. The achieved results prove simplicity and usability of our method in detecting structural anomalies and in adjusting a rule base by exploiting information carried out during the verification phase.

Technology commercialization is a hot topic for governments, entrepreneurs, marketers and researchers due to the fact that its measurement is quite important for decision makers to know their possible growth potential in a dynamic and competitive environment. Having a high technology commercialization potential for invested technology is the engine of national development and growth as well. However, the measuring technology commercialization of any investment project is difficult because of the fact that there are some difficult questions, which should be answered exactly. For example, “how will the technology be marketed in own sector?” “Which factors are important for considered technology and affect technology commercialization?” “Which scientific methods should be used for predication of technology commercialization potential properly?” and so on. The purpose of this study is to develop an evaluation index system for predicting the technology commercialization of the investment project. At the beginning of this study, a review of the technology commercialization literature is performed to address the factors which affect technology commercialization. Then, the evaluation index system proposed in this study can be used to prioritize investment projects in terms of their technology commercialization potential. It uses fuzzy multi-criteria decision making (FMCDM) methods and beta distribution to estimate the mean of each factor. Next, a solution approach to find an index value which shows technology commercialization potential is proposed. The proposed approach firstly finds interrelationships among the factors under fuzziness by using the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. Subsequently, it makes use of the fuzzy Analytic Network Process (Fuzzy ANP) method to determine the weights of factors. The values of all factors are then predicted by experts and assessed by beta distribution to estimate the mean of each factor with respect to the experts' opinion. Finally, a case study is presented to demonstrate the usefulness of the proposed approach.

Nowadays, there has been a growing interest in reverse logistics, recycling, remanufacturing and reusing due to the environmental, economical issues and legal obligations. Due to this fact, companies should take into account the remanufacturing option while preparing the medium term planning (MTP) activities instead of using traditional production planning models. There are lots of studies in the literature related to the reverse logistics (RL) and closed-loop supply chain (CLSC) network design problem which takes place in strategic planning level but a few of them handles the medium-term planning activities. Thus, a fuzzy mixed integer programming model for medium-term planning in a CLSC related to a conceptual product with remanufacturing option is developed in this paper. In the proposed model, both forward and reverse flows are included and two production alternatives are considered: either “production of new products directly in manufacturing plants” or “bringing the returned products back to ‘as new condition’ in the remanufacturing facilities”. However, real world closed-loop supply chains are surrounded with uncertainty. Thus, storage capacities, retailers' and wholesalers' demands, return rates, acceptance ratios, weekly available production/remanufacturing times, transportation upper bounds and objective function value are considered as fuzzy in the proposed model. The proposed fuzzy mathematical programming model is converted into a crisp equivalent model by utilizing several fuzzy aggregation procedures from the literature. The proposed model is applied to an illustrative case and scenario analysis is also carried out for evaluating the effects of some parameters related to the concerned collection-recovery system. Solution of the proposed model is achieved by using ILOG OPL Studio 6.3.