Pub. online:5 Aug 2022Type:Research ArticleOpen Access
Journal:Informatica
Volume 16, Issue 2 (2005), pp. 213–240
Abstract
The article describes a hierarchical decision making framework for the evaluation and improvement/redesign of composite systems. The framework is based on Hierarchical Morphological Multicriteria Design (HMMD) and corresponding morphological clique problem which realize “partitioning/synthesis macroheuristic”. The system evaluation process consists in hierarchical integration of expert judgment (as ordinal estimates): a method of integration tables or the above-mentioned morphological approach. As a result, ordinal multi-state classification is realized. The system improvement/redesign process is examined as the selection and planning of redesign operations while taking into account operations attributes (e.g., required resources, effectiveness) and binary relations (equivalence, complementarity, precedence) on the operation sets. For modeling the system improvement process several combinatorial optimization models are used (knapsack problem, multiple choice problem, etc.) including HMMD.
The suggested approach is illustrated by realistic numerical example for two-floor building. This applied problem is examined from the viewpoint of earthquake engineering.
Pub. online:1 Jan 2019Type:Research ArticleOpen Access
Journal:Informatica
Volume 30, Issue 2 (2019), pp. 269–292
Abstract
The 3D extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS), and neutrosophic sets (NS) aim to describe experts’ judgments more informatively and explicitly. In this paper, generalized three dimensional spherical fuzzy sets are presented with their arithmetic, aggregation, and defuzzification operations. Weighted Aggregated Sum Product ASsessment (WASPAS) is a combination of two well-known multi-criteria decision-making (MCDM) methods, which are weighted sum model (WSM) and weighted product model (WPM). The aim of this paper is to extend traditional WASPAS method to spherical fuzzy WASPAS (SF-WASPAS) method and to show its application with an industrial robot selection problem. Additionally, we present comparative and sensitivity analyses to show the validity and robustness of the given decisions.