331 research outputs found

    Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing

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    Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA

    An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things

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    Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule

    Business analytics in industry 4.0: a systematic review

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    Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a CiĂȘncia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

    Production Optimization Indexed to the Market Demand Through Neural Networks

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    Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent production management that facilitates communication between machines, people and processes and uses technology as the main driver. Many works in the literature treat maintenance and production management in separate approaches, but there is a link between these areas, with maintenance and its actions aimed at ensuring the smooth operation of equipment to avoid unnecessary downtime in production. With the advent of technology, companies are rushing to solve their problems by resorting to technologies in order to fit into the most advanced technological concepts, such as industries 4.0 and 5.0, which are based on the principle of process automation. This approach brings together database technologies, making it possible to monitor the operation of equipment and have the opportunity to study patterns of data behavior that can alert us to possible failures. The present thesis intends to forecast the pulp production indexed to the stock market value.The forecast will be made by means of the pulp production variables of the presses and the stock exchange variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective planning. To support the decision of efficient production management, in this thesis algorithms were developed and validated with from five pulp presses, as well as data from other sources, such as steel production and stock exchange, which were relevant to validate the robustness of the model. This thesis demonstrated the importance of data processing methods and that they have great relevance in the model input since they facilitate the process of training and testing the models. The chosen technologies demonstrated good efficiency and versatility in performing the prediction of the values of the variables of the equipment, also demonstrating robustness and optimization in computational processing. The thesis also presents proposals for future developments, namely in further exploration of these technologies, so that there are market variables that can calibrate production through forecasts supported on these same variables.Conectividade, mobilidade e anĂĄlise de dados em tempo real sĂŁo prĂ©-requisitos para um novo modelo de gestĂŁo inteligente da produção que facilita a comunicação entre mĂĄquinas, pessoas e processos, e usa a tecnologia como motor principal. Muitos trabalhos na literatura tratam a manutenção e a gestĂŁo da produção em abordagens separadas, mas existe uma correlação entre estas ĂĄreas, sendo que a manutenção e as suas polĂ­ticas tĂȘm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens desnecessĂĄrias na linha de produção. Com o advento da tecnologia hĂĄ uma corrida das empresas para solucionar os seus problemas recorrendo Ă s tecnologias, visando a sua inserção nos conceitos tecnolĂłgicos, mais avançados, tais como as indĂșstrias 4.0 e 5.0, as quais tĂȘm como princĂ­pio a automatização dos processos. Esta abordagem junta as tecnologias de sistema de informação, sendo possĂ­vel fazer o acompanhamento do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrĂ”es de comportamento dos dados que nos possam alertar para possĂ­veis falhas. A presente tese pretende prever a produção da pasta de papel indexada Ă s bolsas de valores. A previsĂŁo serĂĄ feita por via das variĂĄveis da produção da pasta de papel das prensas e das variĂĄveis da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo conseguir um planeamento eficaz. Para suportar a decisĂŁo de uma gestĂŁo da produção eficiente, na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores, os quais se mostraram relevantes para a validação da robustez dos modelos. A presente tese demonstrou a importĂąncia dos mĂ©todos de tratamento de dados e que os mesmos tĂȘm uma grande relevĂąncia na entrada do modelo, visto que facilita o processo de treino e testes dos modelos. As tecnologias escolhidas demonstraram uma boa eficiĂȘncia e versatilidade na realização da previsĂŁo dos valores das variĂĄveis dos equipamentos, demonstrando ainda robustez e otimização no processamento computacional. A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração mais aprofundada destas tecnologias, de modo a que haja variĂĄveis de mercado que possam calibrar a produção atravĂ©s de previsĂ”es suportadas nestas mesmas variĂĄveis

    Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

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    The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS

    Planning and Scheduling Optimization

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    Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

    Real-Time Sensor Networks and Systems for the Industrial IoT

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    The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

    Sense and Respond

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    Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes

    Discrete Event Simulations

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    Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

    Integrated Models and Tools for Design and Management of Global Supply Chain

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    In modern and global supply chain, the increasing trend toward product variety, level of service, short delivery delay and response time to consumers, highlight the importance to set and configure smooth and efficient logistic processes and operations. In order to comply such purposes the supply chain management (SCM) theory entails a wide set of models, algorithms, procedure, tools and best practices for the design, the management and control of articulated supply chain networks and logistics nodes. The purpose of this Ph.D. dissertation is going in detail on the principle aspects and concerns of supply chain network and warehousing systems, by proposing and illustrating useful methods, procedures and support-decision tools for the design and management of real instance applications, such those currently face by enterprises. In particular, after a comprehensive literature review of the principal warehousing issues and entities, the manuscript focuses on design top-down procedure for both less-than-unit-load OPS and unit-load storage systems. For both, decision-support software platforms are illustrated as useful tools to address the optimization of the warehousing performances and efficiency metrics. The development of such interfaces enables to test the effectiveness of the proposed hierarchical top-down procedure with huge real case studies, taken by industry applications. Whether the large part of the manuscript deals with micro concerns of warehousing nodes, also macro issues and aspects related to the planning, design, and management of the whole supply chain are enquired and discussed. The integration of macro criticalities, such as the design of the supply chain infrastructure and the placement of the logistic nodes, with micro concerns, such the design of warehousing nodes and the management of material handling, is addressed through the definition of integrated models and procedures, involving the overall supply chain and the whole product life cycle. A new integrated perspective should be applied in study and planning of global supply chains. Each aspect of the reality influences the others. Each product consumed by a customer tells a story, made by activities, transformations, handling, processes, traveling around the world. Each step of this story accounts costs, time, resources exploitation, labor, waste, pollution. The economical and environmental sustainability of the modern global supply chain is the challenge to face
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