Abstract

Abstract
A challenge is emerging in the design of scheduling support systems and facility layout planning, both for manufacturing environments where dynamic adaptation and optimization become increasingly important on the efficiency and productivity. Focusing on the interactions between these two problems, this work combines two paradigms in sequential manner, optimization techniques and multi-agent systems, to better reflect practical manufacturing scenarios. This approach, in addition to significantly improve the quality of the solutions, enables fast reaction to condition changes. In such stochastic and very volatile environments, the manufacturing industries, the fast rescheduling, or planning, are crucial to maintain the system in operation. The proposed architecture was codified in MatLab $$^{\tiny {\textregistered }}$$ and NetLogo and applied to a real-world job shop case study. The experimental results achieved optimized solutions, as well as in the responsiveness to achieve dynamic results for disruptions and simultaneously layout optimization. © 2020, Springer Nature Switzerland AG.

Abstract
The fourth industrial revolution, commonly known as Industry 4.0, germinated in Germany as an industrial program of the government to reinvigorate the manufacturing sector. Nowadays, this change in the industrial paradigm has reached a global scale and is proposing to transform the traditional factories into more competitive, efficient and productive industries. In order to accomplish this goal it is necessary to establish methodologies to migrate from the traditional systems to innovative systems, namely those applying the Cyber-Physical Production Systems (CPPS) concepts. This paper proposes a methodology based on the Petri nets formalism for the modelling, analysis, validation and simulation of the migration process during the design phase and the control and monitoring of such processes during the implementation phase. © 2018 IEEE.

Abstract
The introduction of Industry 4.0 to modernize the existing industrial manufacturing systems has created a wave of disruption all over the world, that changed not only the industry paradigm, but also the research directions in the intelligent manufacturing systems field. After five years since its presentation, similar initiatives have fostered in several countries worldwide to promote the adoption of Industry 4.0 principles. This paper aims to analyze and discuss the current state of adoption of this initiative and to verify the way it changed the research directions, particularly at the level of applying artificial intelligence and ICT technologies in the cyber-physical systems context. For this purpose, an analytical study of the scientific publications related to Industry 4.0 domain has been performed, considering a dataset of scientific publications retrieved from the IEEE Xplore database. This dataset considers two distinct time periods, separated by the introduction of Industry 4.0 as the digitalization threshold: before digital era (B.D.) and after digital era (A.D.). © 2018 IEEE.

Abstract
Home Health Care (HHC) services are growing worldwide and, usually, the home care visits are manually planned, being a time and effort consuming task that leads to a non optimized solution. The use of some optimization techniques can significantly improve the quality of the scheduling solutions, but lacks the achievement of solutions that face the fast reaction to condition changes. In such stochastic and very volatile environments, the fast re-scheduling is crucial to maintain the system in operation. Taking advantage of the inherent distributed and intelligent characteristics of Multi-agent Systems (MAS), this paper introduces a methodology that combines the optimization features provided by centralized scheduling algorithms, e.g. genetic algorithms, with the responsiveness features provided by MAS solutions. The proposed approach was codified in Matlab and NetLogo and applied to a real-world HHC case study. The experimental results showed a significant improvement in the quality of scheduling solutions, as well as in the responsiveness to achieve those solutions. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Enterprise Service Bus (ESB) is a middleware infrastructure that provides a way to integrate loosely-coupled heterogeneous software applications based on the services principles. The life-cycle management of services in such environments is a critical issue for the component's reuse, maintenance and operation. This paper introduces a service life-cycle management module that extends the traditional functionalities with advanced monitoring and data analytics to contribute for the robustness, reliability and self-organization of networks of clusters based on ESB platforms. The realization of this module was embedded in the JBoss ESB, considering a sniffer mechanism to collect the service messages crossing the bus and a Liferay portal to display relevant information related to the services' health. © 2014 IEEE.