102 research outputs found

    Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

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    At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

    Energy aware hybrid flow shop scheduling

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    Only if humanity acts quickly and resolutely can we limit global warming' conclude more than 25,000 academics with the statement of SCIENTISTS FOR FUTURE. The concern about global warming and the extinction of species has steadily increased in recent years

    Note on a Single-Machine Scheduling Problem with Sum of Processing Times Based Learning and Ready Times

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    In the recent 20 years, scheduling with learning effect has received considerable attention. However, considering the learning effect along with release time is limited. In light of these observations, in this paper, we investigate a single-machine problem with sum of processing times based learning and ready times where the objective is to minimize the makespan. For solving this problem, we build a branch-and-bound algorithm and a heuristic algorithm for the optimal solution and near-optimal solution, respectively. The computational experiments indicate that the branch-and-bound algorithm can perform well the problem instances up to 24 jobs in terms of CPU time and node numbers, and the average error percentage of the proposed heuristic algorithm is less than 0.5%

    Theoretical and Computational Research in Various Scheduling Models

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    Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

    A review of scheduling problems in radiotherapy

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    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

    Sequencing in Mixed Model Non-Permutation Flowshop Production Lines using Constrained Buffers

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    En una línea de producción clásica, solamente se producían productos con las mismas opciones. Para la fabricación de variaciones del mismo producto básico se utilizaba una línea diferente o eran necesarias modificaciones importantes de la maquinaria. En los últimos años se ha visto acrecentada la necesidad de considerar métodos que permitan más flexibilidad ofreciendo una mayor variedad de productos al cliente. En general estos métodos consisten en producir diferentes tipos de productos en una misma línea de producción. Además, con la filosofía de Just-In-Time, los stocks y sus costes derivados, especialmente el stock de productos acabados, se reducen considerablemente y consecuentemente una producción con lotes ya no es favorable. Con este panorama la producción de distintos productos o modelos en la misma línea de forma simultánea, sin lotes, adquiere un gran auge y con ello la complejidad de gestión de la línea aumenta. La toma de decisiones en las fases de secuenciación y programación se convierte en esencial.Existen varios diseños de líneas que pueden permitir la resecuenciación, como son:utilizar grandes almacenes (Automatic-Storage-and-Retrieval-System), desacoplar una parte del proceso del resto de la línea; disponer de almacenes con plazas limitadas fuera de la línea; existencia de líneas híbridas o flexibles; posibilitar la división y unión de líneas;o cambiar los atributos de las piezas en vez de cambiar la posición en la secuencia. La resecuenciación de piezas dentro de la línea llega ser más efectiva cuando se presenta un tiempo o coste adicional, conocido como setup-time y setup-cost, necesario en muchos casos, cuando en una estación, la siguiente pieza es de otro modelo.Esta tesis considera el caso de una línea de flujo con la posibilidad de resecuenciar piezas entre estaciones consecutivas. Los almacenes están ubicados fuera de la línea y en un primer paso accesible desde una sola estación (caso del almacén intermedio). A continuación se utilizará un solo almacén, centralizado, accesible desde varias estaciones. En ambos casos se considera que una pieza, debido a su tamaño, quizás no pueda ocupar ciertas plazas del almacén ya sea intermedio o centralizado. Como resultado del estudio y análisis del Estado del Arte, que permitió delimitar el caso a estudiar, se propone una Novedosa Clasificación de líneas de flujo no permutación. Esta clasificación era indispensable, debido a que en la literatura actual no se ha clasificado con profundidad este tipo de producción, hasta hoy las clasificaciones existentes no consideran las múltiples opciones que se presentan al incluir la posibilidad de resecuenciar piezas en la línea. La presente tesis presenta distintas formulaciones: un método exacto, utilizando un modelo de programación por restricciones (CLP), varios métodos híbridos, basados en CLP, y un método heurístico, utilizando un Algoritmo Genético (GA).Durante el curso de este trabajo, los estudios que se han realizado muestran la efectividad de resecuenciar. Los resultados de los experimentos simulados muestran los beneficios que sumergen con un almacén centralizado, comparado con los almacenes intermedios.El problema considerado es relevante para una variedad de aplicaciones de líneas de flujo como es el caso de la industria química, donde los pedidos de los clientes tienen diferentes volúmenes y en la misma línea existen tanques de diferentes volúmenes para resecuenciar. También, en líneas en las cuales se utilizan lotes divididos (split-lot) con el fin de investigar variaciones en los procesos, así como en la industria de semiconductores, o en la producción de casas prefabricadas, donde fabrican paredesgrandes y pequeñas que pasan por estaciones consecutivas y en las que se instalan circuitos eléctricos, tuberías, puertas, ventanas y aislamientos.In the classical production line, only products with the same options were processed at once. Products of different models, providing distinct options, were either processed on a different line or major equipment modifications were necessary. For today's production lines approaches, considering more flexibility, are required which result more and more in the necessity of manufacturing a variety of different models on the same line, motivated by offering a larger variety of products to the client. Furthermore, with the Just-In-Time philosophy, the stock and with that the expenses derived from it, especially for finished products, are considerably reduced and lead to the case in which a production with batches is no longer favourable.Taking into account this panorama, the simultaneous production of distinct products ormodels in the same line, without batches, lead to an increased importance and at the same time the logistic complexity enlarges. The decision-making in sequencing and scheduling become essential.Various designs of production lines exist which permit resequencing of jobs within the production line: using large buffers (Automatic-Storage-and-Retrieval-System) which decouple one part of the line from the rest of the line; buffers which are located offline; hybrid or flexible lines; and more seldom, the interchange of job attributes instead of physically changing the position of a job within the sequence. Resequencing of jobs within the line is even more relevant with the existence of an additional cost or time, occurring when at a station the succeeding job is of another model, known as setup cost and setup time.The present thesis considers a flowshop with the possibility to resequence jobs between consecutive stations. The buffers are located offline either accessible from a single station (intermediate case) or from various stations (centralized case). In both cases, it is considered that a job may not be able to be stored in a buffer place, due to its extended physical size.Following the extensive State-of-the-Art, which led to the problem under study, a Novel Classification of Non-permutation Flowshops is proposed. This classification was indispensable, due to the lack of an adequate classification for flowshop production lines that would consider the diversity of arrangements which permit resequencing of jobs within the production line. Furthermore, distinct formulations are presented: an exact approach, utilizing Constrained Logic Programming (CLP), various hybrid approaches, based on CLP, and a heuristic approach, utilizing a Genetic Algorithm (GA).During the course of this work, the realized studies of performance demonstrate the effectiveness of resequencing. The results of the simulation experiments reveal the benefits that come with a centralized buffer location, compared to the intermediate buffer location.The considered problem is relevant to various flowshop applications such as chemical productions dealing with client orders of different volumes and different sized resequencing tanks. Also in productions where split-lots are used for engineering purpose, such as the semiconductor industry. Even in the production of prefabricated houses with, e.g., large and small walls passing through consecutive stations where electrical circuits, sewerage, doors, windows and isolation are applied

    A survey of AI in operations management from 2005 to 2009

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    Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

    Unveiling Hidden Values of Optimization Models with Metaheuristic Approach

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    Considering that the decision making process for constrained optimization problem is based on modeling, there is always room for alternative solutions because there is usually a gap between the model and the real problem it depicts. This study looks into the problem of finding such alternative solutions, the non-optimal solutions of interest for constrained optimization models, the SoI problem. SoI problems subsume finding feasible solutions of interest (FoIs) and infeasible solutions of interest (IoIs). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making come into play and for this purpose the SoIs can be very valuable. An evolutionary computation approach (in particular, a population-based metaheuristic) is proposed for solving the SoI problem and a systematic approach with a feasible-infeasible- two-population genetic algorithm is demonstrated. In this study, the effectiveness of the proposed approach on finding SoIs is demonstrated with generalized assignment problems and generalized quadratic assignment problems. Also, the applications of the proposed approach on the multi-objective optimization and robust-optimization issues are examined and illustrated with two-sided matching problems and flowshop scheduling problems respectively

    Study on application possibilities of Case-Based Reasoning on the domain of scheduling problems

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    Ces travaux concernent la mise en place d'un système d'aide à la décision, s'appuyant sur le raisonnement à partir de cas, pour la modélisation et la résolution des problèmes d'ordonnancement en génie des procédés. Une analyse de co-citation a été exécutée afin d'extraire de la littérature la connaissance nécessaire à la construction de la stratégie d'aide à la décision et d'obtenir une image de la situation, de l'évolution et de l'intensité de la recherche du domaine des problèmes d'ordonnancement. Un système de classification a été proposée, et la nomenclature proposée par Blazewicz et al. (2007) a été étendue de manière à pouvoir caractériser de manière complète les problèmes d'ordonnancement et leur mode de résolution. Les difficultés d'adaptation du modèle ont été discutées, et l'efficacité des quatre modèles de littérature a été comparée sur trois exemples de flow-shop. Une stratégie de résolution est proposée en fonction des caractéristiques du problème mathématique. ABSTRACT : The purpose of this study is to work out the foundations of a decision-support system in order to advise efficient resolution strategies for scheduling problems in process engineering. This decision-support system is based on Case-Based Reasoning. A bibliographic study based on co-citation analysis has been performed in order to extract knowledge from the literature and obtain a landscape about scheduling research, its intensity and evolution. An open classification scheme has been proposed to scheduling problems, mathematical models and solving methods. A notation scheme corresponding to the classification has been elaborated based on the nomenclature proposed by Blazewicz et al. (2007). The difficulties arising during the adaptation of a mathematical model to different problems is discussed, and the performances of four literature mathematical models have been compared on three flow-shop examples. A resolution strategy is proposed based on the characteristics of the scheduling problem
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