The code of Occupational Health and Safety (OHS) is an important regulation to improve the on-the-job safety of employees. Several factors affect the planning and implementation of OHS management systems (OHSMS). The evaluation of OHS practice is the most important component when building a safety environment policy for employees and administration. Begin aware of subjective nature of factors affecting OHS and the use of statistical method, it becomes controversial as to a way of handling this type of survey data. This research presents a combination of possibilistic regression analysis with a convex hull approach to analyze the fitting factors that impact good practices of OHS. In addition, selected samples of data could be represented as fuzzy sets. This study offers an alternative platform to evaluate influential factors being used towards a successful implementation of the OHS policy.
The use of incomplete probabilistic linguistic term sets (InPLTSs) can enrich the flexibility of qualitative decision-making information expression, especially in decision-making situations with high time pressure and insufficient knowledge. In this article, we develop a method for group decision-making (GDM) with incomplete probabilistic linguistic preference relations (InPLPRs), considering consistency and consensus simultaneously. First, to fully explore the ability of InPLTSs to express uncertain information, InPLTSs are specifically classified. Then, an expected multiplicative consistency of InPLPRs is introduced, which is conducive to estimating the missing information more accurately and effectively. Subsequently, considering the consensus of GDM problems, a consensus index, which considers the principle of majority and minority, is developed to measure the agreement degree among multiple individuals. Because individual InPLPRs may not all meet acceptable consistency after reaching consensus, a consistency- and consensus-improving mathematical programming model considering information distortion is presented. Then, to aggregate all individual preference relations into a collective one, a reliability-induced ordered weighted geometric operator is introduced, whose induced variable reliability is determined by the confidence degree and consistency index of individual preference relations. Furthermore, a multiphase algorithm with InPLPRs is developed to solve GDM problems. Finally, a numerical example of fire emergency decisions is presented to illustrate the applicability of the proposed method, and a detailed validity test and comparative analysis are conducted to highlight the advantages of the proposed method.
Proposed is a certain generalization of nonmonotonic fuzzy set operators introduced originally by R. R. Yager. By introducing a modulating function one can effectively model situations in which available information interacts (overlaps) with a given default fuzzy set. Discussed is a complete learning environment in which the default values (sets) can be derived based upon some experimental data. The role of the nonmonotonic operations is also revealed in the setting of reasoning carried out in the presence of fuzzy data. ©1997 John Wiley & Sons, Inc.
In time series forecasting, a challenging and important task is to realize long-term forecasting that is both accurate and transparent. In this study, we propose a long-term prediction approach by transforming the original numerical data into some meaningful and interpretable entities following the principle of justifiable granularity. The obtained sequences exhibiting sound semantics may have different lengths, which bring some difficulties when carrying out predictions. To equalize these temporal sequences, we propose to adjust their lengths by involving the dynamic time warping (DTW) distance. Two theorems are included to ensure the correctness of the proposed equalization approach. Finally, we exploit hidden Markov models (HMM) to derive the relations existing in the granular time series. A series of experiments using publicly available data are conducted to assess the performance of the proposed prediction method. The comparative analysis demonstrates the performance of the prediction delivered by the proposed model.
Granulation involves decomposition of whole object into a collection of parts called granules. The granules are formed, based on the notions of indistinguishability, similarity, proximity or functionality. Building information granules, especially for highly dimensional data is a demanding task. In this study, we propose a genetic-based development of information granules. The approach is concerned with structural and parametric aspects of the information granulation that involves the number of information granules and their parameters. It is shown how information granulation supports a descriptive data analysis, namely a comprehensive process of revealing essential structures in data sets.
Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.
