A Novel Dual Factor Fuzzy Time Series Forecasting based on new Fuzzy sets and Interval Definition by Evolution Strategies

This paper proposes a new dual factor time-invariant fuzzy time series method that is capable of forecasting stock marketPrice Index. The proposed approach uses a new fuzzy logic relationship definition. According to the utilized membershipdegrees used to define the fuzzy relationships, each datum may belong to two distinct intervals rather than only one interval.This assumption, which has not been considered in the other studies, contributes to better forecasting results. In addition, anappropriate meta-heuristic algorithm for continuous solution schemes, namely evolution strategies (ES), is utilized to identifythe appropriate interval lengths. The proposed approach has been tested on TAIFEX index. The computational results showedthat the proposed approach outperforms the former studies

A Digital Twin-based scheduling framework including Equipment Health Index and Genetic Algorithms

The advent of Industry 4.0 technologies and in particular the Cyber-Physical Systems, Digital Twins and pervasive connected sensors is transforming many industries, among which smart scheduling is one of the most relevant. This paper contributes to the research on scheduling by proposing a framework to include equipment health predictions into the scheduling activity and embedding a field-synchronized Equipment Health Indicator module into the DT simulation. The metaheuristic approach to scheduling optimization is performed by a genetic algorithm, that is connected with the DT simulator and provides various generations of scheduling alternatives that are assessed through the simulator itself. The paper also proposes a practical Proof-of-Concept of the innovative framework, by developing an architecture to identify how the various framework modules are implemented and by applying the framework to a real application case, set in a laboratory assembly line environment. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved