473 research outputs found

    Analysis of metaheuristic optimisation techniques for simulated matrix production systems

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    A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

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    The concept of sustainability is defined as composed of three pillars: social, environmental, and economic. Social sustainability implies a commitment to equity in terms of several “interrelated and mutually supportive” principles of a “sustainable society”; this concept includes attitude change, the Earth’s vitality and diversity conservation, and a global alliance to achieve sustainability. The social and environmental aspects of sustainability are related in the way sustainability indicators are related to “quality of life” and “ecological sustainability”. The increasing interest in green and sustainable products and production has influenced research interests regarding sustainable scheduling problems in manufacturing systems. This study is aimed both at reducing pollutant emissions and increasing production efficiency: this topic is known as Green Scheduling. Existing literature research reviews on Green Scheduling Problems have pointed out both theoretical and practical aspects of this topic. The proposed work is a critical review of the scientific literature with a three-pronged approach based on keywords, taxonomy analysis, and research mapping. Specific research questions have been proposed to highlight the benefits and related objectives of this review: to discover the most widely used methodologies for solving SPGs in manufacturing and identify interesting development models, as well as the least studied domains and algorithms. The literature was analysed in order to define a map of the main research fields on SPG, highlight mainstream SPG research, propose an efficient view of emerging research areas, propose a taxonomy of SPG by collecting multiple keywords into semantic clusters, and analyse the literature according to a semantic knowledge approach. At the same time, GSP researchers are provided with an efficient view of emerging research areas, allowing them to avoid missing key research areas and focus on emerging ones

    From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

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    Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

    Scheduling Algorithms: Challenges Towards Smart Manufacturing

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    Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

    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

    Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

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    Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.Fil: Rossit, Daniel Alejandro. 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; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; ArgentinaFil: Rossit, Diego Gabriel. 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; ArgentinaFil: Nesmachnow, Sergio. Facultad de Ingeniería; Urugua

    Simulation-Optimization model for a hybrid flow shop. Case study, chemical industry

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    Actualmente, las técnicas de investigación de operaciones han demostrado tener un impacto significativo en los sistemas de fabricación modernos, ya que proporcionan una productividad y rendimiento mejorados en mercados altamente competitivos. El problema del Flow Shop de flujo híbrido, también conocido como problema de Flow Shop flexible, es un problema de programación relacionado con un grupo de máquinas paralelas por etapa, frecuentemente asociado con la minimización del tiempo en un entorno de producción. Este problema se considera un problema NP-hard debido a las decisiones combinatorias, los recursos informáticos exigentes y el tiempo de ejecución. Esta investigación se centra en un caso de estudio de la empresa Fuller Pinto, una empresa internacional con sede en Colombia de la industria química que presenta un entorno de tienda de flujo híbrido. Incluso hoy en día, la compañía todavía tiene problemas asociados con la entrega tardía de productos debido a la mala programación, lo que hace que la producción planificada no se lleve a cabo en su totalidad. Además, estos productos sin terminar se convierten en pedidos pendientes con mayor importancia que deben suministrarse de manera obligatoria. Por esta razón, esta investigación propone un sistema de apoyo a la decisión basado en un modelo de simulación-optimizacion para la programación de Fuller Pinto, con el objetivo de minimizar la tardanza total ponderada. El modelo propuesto presenta capacidades mejoradas que simulan el comportamiento del entorno de fabricación para soportar las decisiones de Fuller Pinto. Para validar este modelo, se han probado diferentes escenarios o instancias relacionadas con el comportamiento de producción de productos químicos. Se presentan las comparaciones entre el modelo de simulación-optimización propuesto, la regla de despacho de SPT y los datos históricos de la empresa. Como resultado, el modelo proporciona una programación de la producción óptima de trabajos para cada campaña de Fuller Pinto.Currently, opeiations research techniques have proved to make a significant impact in modem manufacturing systeins as it provides an enhanced productivity and performance on highly competitive markets. Hybrid Flow Shop Problem, also known as Flexible Flow Shop problem, is a scheduling problem related to a group ofparallel machines per stage, frequently associated with time minimization in a manufacturing environment. This problem is considered a NP-hard problem due to the combinatoria! decisions and the demanding computing resources and execution time in its resolution. This research is focused on a case study of Fuller Pinto which is an intemational Colombian-based company from the Chemical industry that presents a Hybrid Flow Shop environment on its shop-floor. Even today, the company still have issues associated to late deliverof products due to poor scheduling, causing the planned production no to be completed. Moreover, these unfínished products become backorders with higher importance that must be supplied mandatory. For this reason, this research proposes a decisión support system based on a simulation-optimization model íor Fuller Pinto scheduling, aiming the minimization of the total weighted tardiness. The proposed model features enhanced capabilities simulating the behavior of the complex manufacturing environment and hybridize iteratively an optimization algorithm to support the Fuller Pinto decisions. To valídate this model, different scenarios or instances related to the production behavior of Chemical products have been tested. Comparisons among the proposed simulation-optimization model, the SPT dispatching rule and historical data of the company are presented. As results, the model provides an optimal schedule of jobs for each campaign of Fuller Pinto.Ingeniero (a) IndustrialPregrad
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