Adapting a Heuristic Oriented Methodology for Achieving Minimum Number of Late Jobs with Identical Processing Machines

Abstract: This study deals with an identical parallel machines scheduling problem where the objective is to minimize the number of jobs be late. The decision on this problem is known as a NP-Hard case. Hence, in this paper, a novel heuristic evolutionary technique which is based on a simple principle, easy to implement, with excellent evolutionary performance, is designed to achieve the optimal/near optimal solution for the considered issue. A sequence of solutions are generated by iterating over a greedy construction heuristic in terms of destruction and construction phases and then an improving local search is conducted to more improve the search performance. In order to assess the effectiveness of the heuristic, some simulation experiments are carried out which reveal out performance of the proposed heuristic as opposed to the traditional evolutionary framework

Balancing mixed-model assembly line to reduce work overload in a multi-level production system

Generating the optimal production schedule for an assembly line, which will balance the workload at all the production stages, is a difficult task considering a variety of practical constraints. Varying customer demand is an important factor to be considered when designing an assembly line. In order to respond to varying customer demand, many companies are attempting to make their production system more flexible/agile or adaptable to change. Due to the volatile nature of market, companies cannot afford to manufacture same type of product for long period of time and neither can maintain high inventory level; to tackle this problem we propose a new approach of balancing mixed-model assembly line in a multi-level production system. The emphasis is on incorporating the effect of set-up times of lower production levels on the final assembly schedule. This will facilitate stabilized workload among and across the stations and effectively balance the production schedule at all production stages. As a result, the proposed model assures that workloads are balanced and setup times are reduced to such an extent that WIP and overall inventories are kept to a low level

Aplicação do Particle Swarm Optimization a um problema de escalonamento de máquinas paralelas não relacionadas com tempos de setup dependentes da sequência

Dissertação de mestrado em Engenharia de SistemasEsta dissertação aborda um problema de escalonamento de máquinas paralelas não relacionadas com tempos de setup dependentes da sequência e o objetivo é minimizar o makespan de um conjunto de trabalhos. Para tal, é implementado o algoritmo Particle Swarm Optimization, que é usado para resolver um problema da literatura, dividido em pequenos e grandes problemas, consoante o número de trabalhos que são utilizados. O desempenho deste algoritmo foi avaliado através de uma análise comparativa das suas soluções com as soluções obtidas usando o Ant Colony Optimization, o Simulated Annealing e o Genetic Algorithm. Na implementação do algoritmo em estudo foi utilizado a toolbox particleswarm do software MATLAB, que tenta otimizar utilizando o algoritmo Particle Swarm Optimization. Os resultados da implementação mostram que para pequenos problemas o Particle Swarm consegue superar o Genetic Algorithm em algumas instâncias, sendo que os outros três algoritmos apresentam valores de makespan inferiores. Para grandes problemas, é clara a superioridade do Particle Swarm em relação ao Genetic Algorithm, no entanto, relativamente aos restantes algoritmos o mesmo não acontece. Existe também a tendência crescente da variação percentual entre os algoritmos à medida que o número de máquinas aumenta para o mesmo número de trabalhos.This dissertation addresses the unrelated parallel machine scheduling problem with sequence-dependent setup times and the objective is to minimize the makespan of a set of jobs. It is implemented the Particle Swarm Optimization, used to solve a problem from the literature, divided into small and large problems, depending on the number of jobs that are used. Particle Swarm performance is evaluated through a comparative analysis between its solutions and the solutions obtained using Ant Colony Optimization, Simulated Annealing and Genetic Algorithm. For implementing the algorithm under study, the particle swarm toolbox from the MATLAB software was used, which tries to optimize using the Particle Swarm Optimization. The results of the implementation show that for small problems the Particle Swarm can overcome the Genetic Algorithm in some instances, with the other three algorithms having lower makespan values. For large problems, the Particle Swarm superiority over Genetic Algorithm is clear, however, in relation to the other algorithms the same does not happen. There is also as increasing trend in the percentage variation between the algorithms as the number of machines increases for the same number of jobs

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Diseño de una metodología de programación de producción para la reducción de costos en un flow shop híbrido flexible mediante el uso de algoritmos genéticos. Aplicación a la industria textil

La industria textil posee configuración productiva flow shop híbrido flexible, además de una serie de particularidades que hacen que los modelos estándares de programación de producción no sean aplicables. Se ha demostrado la naturaleza N-P completo del problema, por lo que el uso de meta heurísticas está bien justificado. Considerando la importancia de la reducción de los costos de fabricación en la industria textil colombiana, se propone una nueva metodología de programación de producción basada en algoritmos genéticos, que tiene presente algunas de las complejidades de la industria textil (tiempos de montaje dependientes de la secuencia, máquinas paralelas no relacionadas, cumplimiento de fechas de entrega) y permite la reducción de sus costos de producción. Al aplicarla a un problema basado en la industria textil colombiana se obtuvo una mejora promedio del 22,39% y 22,36% con respecto al método SPT y a un método aleatorio, respectivamente. Asimismo se reduce casi en un 100% el incumplimiento de fechas de entrega. Se concluye que la metodología es efectiva y que puede extenderse su aplicación a otros sectores industriales con configuración flow shop híbrido flexible. Futuros trabajos podrían considerar otras complejidades como los lotes de transferencia variables, la entrada dinámica y la maleabilidad, o aplicar la metodología a otro tipo de industrias con esta configuración productivaAbstract : Textile industry can be described by the productive configuration denominated Hybrid Flow Shop, and has a number of characteristics that make the standard scheduling models not applicable. It has been proved the NP-complete nature of the problem, so that the use of meta-heuristics is well justified. Considering the importance of reducing manufacturing costs in Colombian textile industry, a new production scheduling methodology based on genetic algorithms is proposed, which take into account some of the complexities presented in the textile industry (sequence dependent setup times, unrelated parallel machines, compliance with due dates) and allows the reduction of production costs. When the methodology was applied to a Colombian textile industry-based problem, an average improvement of 22.39% and 22.36% in comparison with the SPT method and random method, respectively, were obtained. It was also reduced almost in 100% the failure to due dates. It is concluded that the methodology is effective and can extend its application to other industries with a hybrid flow shop configuration. Future work could consider other complexities such as variable transfer batches, dynamic input and malleability, or apply the methodology to other industries in this productive configurationMaestrí

GRASP and Tabu Search applied to scheduling problems in parallel machines

Orientador: Vinicius Amaral ArmentanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho é dedicado à programação de tarefas em máquinas paralelas. Dois ambientes são considerados. No primeiro, as máquinas são idênticas e o objetivo é a minimização da soma ponderada de custos de atraso. Todas as tarefas estão disponíveis para processamento no início do horizonte de programação e a cada uma são associadas uma data de entrega e uma penalização por atraso específicas. No segundo, as máquinas são não relacionadas e o objetivo é a minimização da soma ponderada de custos de avanço e de atraso. Instantes de liberação, datas de entrega, penalizações por avanço e por atraso são específicos para cada tarefa. Em ambos, as transições entre tarefas requerem tempos de preparação dependentes da seqüência de processamento. Os problemas são resolvidos por meio de GRASP e Busca Tabu. Memória de longo prazo é empregada para melhorar o desempenho das duas metaheurísticas. No GRASP, soluções de elite influenciam a fase construtiva. Na Busca Tabu, estratégias de diversificação e de intensificação fazem uso direto das soluções de elite e também de freqüências de residência. Como pós-otimização, nas duas metaheurísticas, realizam-se religações de caminhos entre as soluções de eliteAbstract: This work is dedicated to the scheduling of a set of jobs in parallel machines. Two scenarios are considered. In the first one, the machines are identical and the objective is the minimization of the weighted sum of tardiness costs. All jobs are ready for processing at the beginning of the scheduling horizon and to each one is associated a due date and a tardiness penalty. In the second scenario, the machines are non-related and the objective is the minimization of the weighted sum of earliness and tardiness costs. Ready times, due dates, earliness and tardiness penalties are specifics to each job. In both problems, the transitions between jobs require sequence dependent setup times. The problems are solved using GRASP and Tabu Search. Long term memory is applied to improve the performance of the metaheuristics. A set of elite solutions are used to influence the constructive phase in GRASP. In Tabu Search, diversification and intensification strategies make direct use of the elite solutions, as well of residence frequences. Path relinking between the elite solutions is used as a post-optimization approachDoutoradoAutomaçãoDoutor em Engenharia Elétric

The dynamic, resource-constrained shortest path problem on an acyclic graph with application in column generation and literature review on sequence-dependent scheduling

This dissertation discusses two independent topics: a resource-constrained shortest-path problem (RCSP) and a literature review on scheduling problems involving sequence-dependent setup (SDS) times (costs). RCSP is often used as a subproblem in column generation because it can be used to solve many practical problems. This dissertation studies RCSP with multiple resource constraints on an acyclic graph, because many applications involve this configuration, especially in column genetation formulations. In particular, this research focuses on a dynamic RCSP since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-time âÂÂpreliminaryâ phase to transform RCSP into an unconstrained shortest path problem (SPP) and then solves the resulting SPP after new values of dual variables are used to update objective function coefficients (i.e., reduced costs) at each iteration. Network reduction techniques are considered to remove some nodes and/or arcs permanently in the preliminary phase. Specified techniques are explored to reoptimize when only several coefficients change and for dealing with forbidden and prescribed arcs in the context of a column generation/branch-and-bound approach. As a benchmark method, a label-setting algorithm is also proposed. Computational tests are designed to show the effectiveness of the proposed algorithms and procedures. This dissertation also gives a literature review related to the class of scheduling problems that involve SDS times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations - single machine, parallel machine, flow shop, and job shop - reviewing both optimizing and heuristic solution methods in each category. Since lot-sizing is so intimately related to scheduling, this dissertation reviews work that integrates these issues in relationship to each configuration. This dissertation provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems. since, as a subproblem in column generation, objective function coefficients are updated using new values of dual variables at each iteration. This dissertation proposes a pseudo-polynomial solution method for solving the dynamic RCSP by exploiting the special structure of an acyclic graph with the goal of effectively reoptimizing RCSP in the context of column generation. This method uses a one-tim

Mejora de tiempos de entrega en un flow shop híbrido flexible usando técnicas inteligentes. Aplicación en la industria de tejidos técnicos

Se busca aportar herramientas útiles para la programación de producción en la industria de tejidos técnicos. Se parte de las condiciones actuales de la programación de producción en este tipo de industria y de los antecedentes en la literatura científica sobre modelos aplicables a estos entornos. Se propone un modelo de solución por técnicas inteligentes a la problemática de la secuenciación y asignación de tareas en los entornos flow shop híbrido flexible considerando situaciones como: paralelismo entre máquinas no relacionadas, tiempos de montaje dependientes de la secuencia, entrada dinámica de trabajos, restricción de elegibilidad, maleabilidad y lotes de transferencia variables entre etapas. De allí se construye la propuesta de solución que involucra simultáneamente todas las condiciones de entorno real mencionadas y aplica un algoritmo genético modificado de acuerdo a las características del problema. Se concluye que el modelado considerando condiciones realistas es posible, que los algoritmos genéticos son una opción práctica para entornos reales y que las empresas pueden obtener mejoras en su capacidad de respuesta con este tipo de solucionesAbstract : It seeks to provide useful tools for production scheduling in the technical textiles industry. It begins in the current conditions of production scheduling in this type of industry and the background in scientific literature, applicable to these environments models. The mathematical model to solve the problem of sequencing and assigning jobs in Flexible hybrid flow shop environments is developed considering: unrelated parallel machines, sequence dependent setup time, dynamic entry of jobs, availability constrain, malleability and variable transfer batches between stages. The solution proposal is build including all actual environment features considered together and applying a modified genetic algorithm modeled according to the problem. It is concluded that the model of scheduling problems considering realistic conditions is possible, that genetic algorithms are a practical option for real environments, and that companies can achieve improvements in their responsiveness with this kind of solutionsDoctorad