A biased-randomized simheuristic for a hybrid flow shop with stochastic processing times in the semiconductor industry

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksCompared to other industries, production systems in semiconductor manufacturing have an above-average level of complexity. Developments in recent decades document increasing product diversity, smaller batch sizes, and a rapidly changing product range. At the same time, the interconnections between equipment groups increase due to rising automation, thus making production planning and control more difficult. This paper discusses a hybrid flow shop problem with realistic constraints, such as stochastic processing times and priority constraints. The primary goal of this paper is to find a solution set (permutation of jobs) that minimizes the production makespan. The proposed algorithm extends our previous work by combining biased-randomization techniques with a discrete-event simulation heuristic. This simulation-optimization approach allows us to efficiently model dependencies caused by batching and by the existence of different flow paths. As shown in a series of numerical experiments, our methodology can achieve promising results even when stochastic processing times are considered.Peer ReviewedPostprint (author's final draft

A biased-randomized discrete-event algorithm for the hybrid flow shop problem with time dependencies and priority constraints

Based on a real-world application in the semiconductor industry, this article models and discusses a hybrid flow shop problem with time dependencies and priority constraints. The analyzed problem considers a production where a large number of heterogeneous jobs are processed by a number of machines. The route that each job has to follow depends upon its type, and, in addition, some machines require that a number of jobs are combined in batches before starting their processing. The hybrid flow model is also subject to a global priority rule and a “same setup” rule. The primary goal of this study was to find a solution set (permutation of jobs) that minimizes the production makespan. While simulation models are frequently employed to model these time-dependent flow shop systems, an optimization component is needed in order to generate high-quality solution sets. In this study, a novel algorithm is proposed to deal with the complexity of the underlying system. Our algorithm combines biased-randomization techniques with a discrete-event heuristic, which allows us to model dependencies caused by batching and different paths of jobs efficiently in a near-natural way. As shown in a series of numerical experiments, the proposed simulation-optimization algorithm can find solutions that significantly outperform those provided by employing state-of-the-art simulation software.Peer ReviewedPostprint (published version

An Iterated Greedy Algorithm for Flexible Flow Lines with Sequence Dependent Setup Times to Minimize Total Weighted Completion Time

This paper explores the flexile flow lines where setup times are sequence- dependent. The optimization criterion is the minimization of total weighted completion time. We propose an iterated greedy algorithm (IGA) to tackle the problem. An experimental evaluation is conducted to evaluate the proposed algorithm and, then, the obtained results of IGA are compared against those of some other existing algorithms. The effectiveness of IGA is demonstrated through comparison

An Hybrid Genetic Algorithm to Optimization of Flow Shop Scheduling Problems under Real Environments Constraints

This paper aims to analyzing the effect of the inclusion of several constraints that have negative influence in the real manufacturing productions. For the solution of the scheduling problem treated in this paper, known as Flow Shop Scheduling, an efficient Genetic Algorithm is introduced combined with the Variable Neighborhood Search for problems of n tasks and m machines minimizing the total completion time or makespan. Release date, dependent setup-times and transport times are entered. These are common restrictions that can be found in multiple manufacturing environments where there are machines, tools, and a set of jobs must be processed in these, following the same flow pattern. The computational experiments carried out on a set of instances of problems of different sizes of complexity show that the proposed hybrid metaheuristic achieves high quality solutions comparable to the optimum ones reported

Un problema de programación de la producción en células de fabricación que incluye almacenes

En este trabajo se presenta un problema específico de programación de la producción flow-shop de interés práctico. El sistema de fabricación está configurado como una célula de fabricación y en el planteamiento del problema se consideran los almacenes de materias primas y de productos terminados. El desempeño de la programación se evalúa de una manera multiobjetivo, considerando el tiempo total de producción (makespan) y la tardanza total (tardiness). Se propone una formulación matemática para el problema. Además, se presenta una estrategia meta-heurística para resolver eficientemente dicho problema y obtener soluciones de buena calidad en un tiempo computacional razonable. El procedimiento aplicado se basa en una adaptación de la metaheurística de recocido simulado. Se generaron conjuntos de problemas para evaluar el método propuesto, obteniendo soluciones óptimas o casi óptimas en tiempos significativamente menores que los requeridos por el enfoque de optimización resuelto mediante CPLEX. Además, el algoritmo fue probado con problemas de mayor tamaño, para evaluar su comportamiento en espacios de búsqueda más extensos.Sociedad Argentina de Informática e Investigación Operativ

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.The translation of this paper was funded by Universidad Politecnica de Valencia, Spain.Gómez Gasquet, P.; Andrés Romano, C.; Lario Esteban, FC. (2012). An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan. Expert Systems with Applications. 39(9):8095-8107. https://doi.org/10.1016/j.eswa.2012.01.158S8095810739

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

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