Katarzyna Rudnik

Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system

Display Omitted A MCDM method as the extension of the fuzzy TOPSIS (FTOPSIS) is described.An approach uses ordered fuzzy numbers (OFNs).Orientations of fuzzy numbers distinguish the type of criteria (benefit, cost).Flow controller in a manufacturing system is presented as application of the method.A simulation of a manufacturing system model and comparison of other methods are presented. The aim of the paper is to present a practical solution to a particular real-life problem of discrete flow control in a manufacturing system. For this purpose a fuzzy Multi-Criteria Decision Making (MCDM) method, which is an extension of the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) approach using Ordered Fuzzy Numbers (OFNs), is used. The paper includes a case study of the application of this method as a flow controller for the transport trolley in a flexible manufacturing system. The immediate aim of control is to choose the transport trolleys destination (production line). A set of criteria (parameters) for the evaluation of the flows destination is suggested, related to time efficiency and equal workload of machines. The advantage of the proposed method is its ability to distinguish the type of criteria (benefit, cost) by using the orientation of ordered fuzzy numbers. An example of calculations using the presented method is shown and the results of the method, based on the simulation of a manufacturing system, are presented. The results thus obtained are compatible with flexible automation. In order to test the FTOPSIS method with OFNs the proposed flow control method is compared with the TOPSIS method and other simple control methods (dispatching rules). The proposed method is more efficient than the methods used for comparison and can be effective and efficient for diversified production and fast, automatic changeovers on production lines.

System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects

A probabilistic fuzzy system for innovative project risk assessment is proposed.A system as the extension of Mamdani probabilistic fuzzy system is described.A new method of system identification using parametric family of t-norms is proposed.The algorithm uses assumptions of fuzzy association rules.The approach uses analysis of probability of fuzzy events with technical risk factors. This paper presents the properties, identification issues and utilisation of a new concept of probabilistic fuzzy system for the innovative project risk assessment. This system constitutes the extension of Mamdani probabilistic fuzzy system. For this purpose, a group of risk factors, which influence risk variables, has been chosen. Linguistic risk variables are inputs to the innovation risk assessment system. The structure of fuzzy sets for linguistic values takes into account knowledge of a number of experts. Knowledge is presented as fuzzy IF-THEN rules together with probability measures of fuzzy events occurrence in the antecedent and conclusion of rules. The paper presents a new method of identification of the analysed system. The method uses parametric family of triangular t-norms, which facilitates inference parameters optimisation, enables flexible adjustment of a system to empirical data and makes the system more precise. The modified FP-Growth algorithm to create probabilistic fuzzy rule base is used. Using assumption of the minimal support of rules enables decreasing of knowledge base complexity while preserving the level of identification quality, comparable to the system with full marginal and conditional probability distributions. The results of the system inference have been compared with regression model and Mamdani fuzzy inference system. Finally, the numerical experiments show more precision of system inference than the compared method. The example of analytical use of created probabilistic fuzzy knowledge base in the context of technical innovation risk assessment is also presented.The constructed expert system has an identification character and it can be develop as a tool to help the assessment of applications for funding the implementation of innovative projects by the institutions established for this purpose.

Stochastic-Fuzzy Knowledge-Based Approach to Temporal Data Modeling

In the chapter an advanced fuzzy modeling method has been presented which can be useful in temporal data analysis. The method joints fuzzy and probabilistic approaches. The notions of the stochastic process with fuzzy states, and linguistic random variable have been defined to create a knowledge representation of the SISO and MISO dynamic systems. As the basic description of the stochastic process with fuzzy states observed at fixed moments, the joint probability distribution of n linguistic random variables has been assumed. The joint, conditional and marginal probability distributions of the stochastic process with fuzzy states valuate weights of particular rules of the knowledge rule base. Also, the probability distributions determine the probabilistic structure of the particular steps of the tested process. A mean fuzzy conclusion (prediction) can be calculated by the proposed inference procedure.

Probabilistic Fuzzy Approach to Assessment of Supplier Based on Delivery Process

In the article, a new tool for the assessment of suppliers’ performance is proposed. This tool uses a probabilistic fuzzy approach based on the assessment of a delivery process. The approach uses a probabilistic fuzzy system of MISO type, in which the knowledge base is described as fuzzy if-then rules with the probabilities of fuzzy events in the antecedents and consequents of rules at the same time. The system identification with limiting the number of elementary rules based on a measure of the minimal support of rules is presented. Various cases of suppliers assessments in a real-life company are illustrated by the analysis of the probabilistic fuzzy knowledge base.

Transport Trolley Control in a Manufacturing System Using Simulation with the FSAW, FWASPAS and FTOPSIS Methods

The paper is a case study of the application of fuzzy MCDM methods to transport trolley control in a manufacturing system. For this purpose, three methods (FSAW, FWASPAS and FTOPSIS) are applied and compared with their non-fuzzy versions using discrete simulation in Matlab Simulink with SimEvents. The aim of the control is the suboptimal selection of the transport trolley’s destination (the appropriate production line), which depends on time efficiency and equal workload of machines. The application of MCDM methods is compatible with flexible automation.