Machine Learning Algorithms in Cloud Manufacturing - A Review

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

With the development of Industry 4.0 and intelligent manufacturing, aiming at the incompatibility of heterogeneous manufacturing resource interfaces and the low efficiency of collaborative scheduling of manufacturing resources among enterprises，we proposed the resource integration and scheduling strategy among enterprises based on Mixed Set Programming [1]. By using the metadata and ontology modeling methods, we were able to realize a standardized model description of manufacturing resources. At last, an enterprise application case was discussed to verify the resources integration and scheduling strategy based on Mixed Set Programming is effective to optimize and improve the efficiency of the collaborative scheduling of resources among enterprises. The resources integration and scheduling strategy based on Mixed Set Programming could be applied to promote the optimal allocation of manufacturing resources

Scheduling in cloud manufacturing systems: Recent systematic literature review

Cloud Manufacturing (CMFg) is a novel production paradigm that benefits from Cloud Computing in order to develop manufacturing systems linked by the cloud. These systems, based on virtual platforms, allow direct linkage between customers and suppliers of manufacturing services, regardless of geographical distance. In this way, CMfg can expand both markets for producers, and suppliers for customers. However, these linkages imply a new challenge for production planning and decision-making process, especially in Scheduling. In this paper, a systematic literature review of articles addressing scheduling in Cloud Manufacturing environments is carried out. The review takes as its starting point a seminal study published in 2019, in which all problem features are described in detail. We pay special attention to the optimization methods and problem-solving strategies that have been suggested in CMfg scheduling. From the review carried out, we can assert that CMfg is a topic of growing interest within the scientific community. We also conclude that the methods based on bio-inspired metaheuristics are by far the most widely used (they represent more than 50% of the articles found). On the other hand, we suggest some lines for future research to further consolidate this field. In particular, we want to highlight the multi-objective approach, since due to the nature of the problem and the production paradigm, the optimization objectives involved are generally in conflict. In addition, decentralized approaches such as those based on game theory are promising lines for future research.Fil: Halty, Agustín. Universidad de la República; UruguayFil: Sánchez, Rodrigo. Universidad de la República; UruguayFil: Vázquez, Valentín. Universidad de la República; UruguayFil: Viana, Víctor. Universidad de la República; UruguayFil: Piñeyro, Pedro. Universidad de la República; UruguayFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentin

Provendo robustez a escalonadores de workflows sensíveis às incertezas da largura de banda disponível

Orientadores: Edmundo Roberto Mauro Madeira, Luiz Fernando BittencourtTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Para que escalonadores de aplicações científicas modeladas como workflows derivem escalonamentos eficientes em nuvens híbridas, é necessário que se forneçam, além da descrição da demanda computacional desses aplicativos, as informações sobre o poder de computação dos recursos disponíveis, especialmente aqueles dados relacionados com a largura de banda disponível. Entretanto, a imprecisão das ferramentas de medição fazem com que as informações da largura de banda disponível fornecida aos escalonadores difiram dos valores reais que deveriam ser considerados para se obter escalonamentos quase ótimos. Escalonadores especialmente projetados para nuvens híbridas simplesmente ignoram a existência de tais imprecisões e terminam produzindo escalonamentos enganosos e de baixo desempenho, o que os tornam sensíveis às informações incertas. A presente Tese introduz um procedimento pró-ativo para fornecer um certo nível de robustez a escalonamentos derivados de escalonadores não projetados para serem robustos frente às incertezas decorrentes do uso de informações imprecisas dadas por ferramentas de medições de rede. Para tornar os escalonamentos sensíveis às incertezas em escalonamentos robustos às essas imprecisões, o procedimento propõe um refinamento (uma deflação) das estimativas da largura de banda antes de serem utilizadas pelo escalonador não robusto. Ao propor o uso de estimativas refinadas da largura de banda disponível, escalonadores inicialmente sensíveis às incertezas passaram a produzir escalonamentos com um certo nível de robustez às essas imprecisões. A eficácia e a eficiência do procedimento proposto são avaliadas através de simulação. Comparam-se, portanto, os escalonamentos gerados por escalonadores que passaram a usar o procedimento proposto com aqueles produzidos pelos mesmos escalonadores mas sem aplicar esse procedimento. Os resultados das simulações mostram que o procedimento proposto é capaz de prover robustez às incertezas da informação da largura de banda a escalonamentos derivados de escalonardes não robustos às tais incertezas. Adicionalmente, esta Tese também propõe um escalonador de aplicações científicas especialmente compostas por um conjunto de workflows. A novidade desse escalonador é que ele é flexível, ou seja, permite o uso de diferentes categorias de funções objetivos. Embora a flexibilidade proposta seja uma novidade no estado da arte, esse escalonador também é sensível às imprecisões da largura de banda. Entretanto, o procedimento mostrou-se capaz de provê-lo de robustez frente às tais incertezas. É mostrado nesta Tese que o procedimento proposto aumentou a eficácia e a eficiência de escalonadores de workflows não robustos projetados para nuvens híbridas, já que eles passaram a produzir escalonamentos com um certo nível de robustez na presença de estimativas incertas da largura de banda disponível. Dessa forma, o procedimento proposto nesta Tese é uma importante ferramenta para aprimorar os escalonadores sensíveis às estimativas incertas da banda disponível especialmente projetados para um ambiente computacional onde esses valores são imprecisos por natureza. Portanto, esta Tese propõe um procedimento que promove melhorias nas execuções de aplicações científicas em nuvens híbridasAbstract: To derive efficient schedules for the tasks of scientific applications modelled as workflows, schedulers need information on the application demands as well as on the resource availability, especially those regarding the available bandwidth. However, the lack of precision of bandwidth estimates provided by monitoring/measurement tools should be considered by the scheduler to achieve near-optimal schedules. Uncertainties of available bandwidth can be a result of imprecise measurement and monitoring network tools and/or their incapacity of estimating in advance the real value of the available bandwidth expected for the application during the scheduling step of the application. Schedulers specially designed for hybrid clouds simply ignore the inaccuracies of the given estimates and end up producing non-robust, low-performance schedules, which makes them sensitive to the uncertainties stemming from using these networking tools. This thesis introduces a proactive procedure to provide a certain level of robustness for schedules derived from schedulers that were not designed to be robust in the face of uncertainties of bandwidth estimates stemming from using unreliable networking tools. To make non-robust schedulers into robust schedulers, the procedure applies a deflation on imprecise bandwidth estimates before being used as input to non-robust schedulers. By proposing the use of refined (deflated) estimates of the available bandwidth, non-robust schedulers initially sensitive to these uncertainties started to produce robust schedules that are insensitive to these inaccuracies. The effectiveness and efficiency of the procedure in providing robustness to non-robust schedulers are evaluated through simulation. Schedules generated by induced-robustness schedulers through the use of the procedure is compared to that of produced by sensitive schedulers. In addition, this thesis also introduces a flexible scheduler for a special case of scientific applications modelled as a set of workflows grouped into ensembles. Although the novelty of this scheduler is the replacement of objective functions according to the user's needs, it is still a non-robust scheduler. However, the procedure was able to provide the necessary robustness for this flexible scheduler be able to produce robust schedules under uncertain bandwidth estimates. It is shown in this thesis that the proposed procedure enhanced the robustness of workflow schedulers designed especially for hybrid clouds as they started to produce robust schedules in the presence of uncertainties stemming from using networking tools. The proposed procedure is an important tool to furnish robustness to non-robust schedulers that are originally designed to work in a computational environment where bandwidth estimates are very likely to vary and cannot be estimated precisely in advance, bringing, therefore, improvements to the executions of scientific applications in hybrid cloudsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2012/02778-6FAPES

Sustainability of systems interoperability in dynamic business networks

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresCollaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property to support businesses development. When achieved seamlessly, efficiency is increased in the entire product life cycle support. However, due to the different sources of knowledge, models and semantics, enterprise organisations are experiencing difficulties exchanging critical information, even when they operate in the same business environments. To solve this issue, most of them try to attain interoperability by establishing peer-to-peer mappings with different business partners, or use neutral data and product standards as the core for information sharing, in optimized networks. In current industrial practice, the model mappings that regulate enterprise communications are only defined once, and most of them are hardcoded in the information systems. This solution has been effective and sufficient for static environments, where enterprise and product models are valid for decades. However, more and more enterprise systems are becoming dynamic, adapting and looking forward to meet further requirements; a trend that is causing new interoperability disturbances and efficiency reduction on existing partnerships. Enterprise Interoperability (EI) is a well established area of applied research, studying these problems, and proposing novel approaches and solutions. This PhD work contributes to that research considering enterprises as complex and adaptive systems, swayed to factors that are making interoperability difficult to sustain over time. The analysis of complexity as a neighbouring scientific domain, in which features of interoperability can be identified and evaluated as a benchmark for developing a new foundation of EI, is here proposed. This approach envisages at drawing concepts from complexity science to analyse dynamic enterprise networks and proposes a framework for sustaining systems interoperability, enabling different organisations to evolve at their own pace, answering the upcoming requirements but minimizing the negative impact these changes can have on their business environment

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

A Service-Oriented Approach for Sensing in the Internet of Things: Intelligent Transportation Systems and Privacy Use Cases

This paper presents a Sensing-as-a-Service run-time Service Oriented Architecture (SOA), called 3SOA, for the development of Internet of Things (IoT) applications. 3SOA aims to allow interoperability among various IoT platforms and support service-oriented modelling at high levels of abstraction where fundamental SOA theories and techniques are fully integrated into a practical software engineering approach. 3SOA abstracts the dependencies of the middleware programming model from the application logic. This abstraction allows the development efforts to focus on writing the application logic independently from hardware platforms, middleware, and languages in which applications are programmed. To achieve this result, IoT objects are treated as independent entities that may interact with each other using a well-defined message exchange sequence. Each object is defined by the services it provides and the coordination protocol it supports. Objects are then able to coordinate their resources to address the global objectives of the system. To practically validate our proposals, we demonstrate an intelligent transportation system and data privacy functional prototypes as proof of concepts. The use cases show that 3SOA and the presented abstraction language allow the amalgamation of macroprogramming and node-centric programming to develop real-time and efficient applications over IoT

A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)