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

Minimización del makespan para el problema de máquinas paralelas no relacionadas con tiempos de setup dependientes de la secuencia mediante un algoritmo híbrido VNS/ACO*

This paper proposes a hybrid heuristic that combines Variable Neighborhood Search (VNS) with Ant Colony Optimization (ACO) to solve the scheduling problem of nonrelated parallel machines with sequence dependent setup times in order to minimize the makespan. The Variable Neighborhood Search is proposed to solve the scheduling problem with a descending scheme in a first phase, with an ACO algorithm, which successively reorder the jobs in the machine with the largest makespan in a second phase. An experimental study was performed using test problems from the literature showing that the second phase of the algorithm improves the solution obtained in the first phase. The results obtained are also compared with other methods in the literature proving to be a competitive method.Se propone una heurística híbrida combinando Variable Neighborhood Search (VNS) y Ant Colony Optimization (ACO) para resolver el problema de programación de máquinas paralelas no relacionadas con tiempos de preparación dependientes de la secuencia con el objetivo de minimizar el makespan. La búsqueda en entornos variables se propone con un esquema descendente resolviendo en una primera etapa el problema de programación de los trabajos a las máquinas, y luego, en una segunda etapa, un algoritmo ACO, reordena sucesivamente los trabajos en la máquina de mayor makespan. Se realizan pruebas experimentales sobre un conjunto de problemas de prueba de la literatura, mostrando que al aplicar la segunda etapa de la metaheurística propuesta se mejoran las soluciones obtenidas en la primera etapa del algoritmo y que al comparar los resultados obtenidos con otros métodos de la literatura resulta ser un método competitivo.&nbsp

Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Aproximações heurísticas para um problema de escalonamento do tipo flexible job-shop

Mestrado em Engenharia e Gestão IndustrialEste trabalho aborda um novo tipo de problema de escalonamento que pode ser encontrado em várias aplicações do mundo-real, principalmente na indústria transformadora. Em relação à configuração do shop floor, o problema pode ser classificado como flexible job-shop, onde os trabalhos podem ter diferentes rotas ao longo dos recursos e as suas operações têm um conjunto de recursos onde podem ser realizadas. Outras características de processamento abordadas são: datas possíveis de início, restrições de precedência (entre operações de um mesmo trabalho ou entre diferentes trabalhos), capacidade dos recursos (incluindo paragens, alterações na capacidade e capacidade infinita) e tempos de setup (que podem ser dependentes ou independentes da sequência). O objetivo é minimizar o número total de trabalhos atrasados. Para resolver o novo problema de escalonamento proposto um modelo de programação linear inteira mista é apresentado e novas abordagens heurísticas são propostas. Duas heurísticas construtivas, cinco heurísticas de melhoramento e duas metaheurísticas são propostas. As heurísticas construtivas são baseadas em regras de ordenação simples, onde as principais diferenças entre elas dizem respeito às regras de ordenação utilizadas e à forma de atribuir os recursos às operações. Os métodos são designados de job-by-job (JBJ), operation-by-operation (OBO) e resource-by-resource (RBR). Dentro das heurísticas de melhoramento, a reassign e a external exchange visam alterar a atribuição dos recursos, a internal exchange e a swap pretendem alterar a sequência de operações e a reinsert-reassign é focada em mudar, simultaneamente, ambas as partes. Algumas das heurísticas propostas são usadas em metaheurísticas, nomeadamente a greedy randomized adaptive search procedure (GRASP) e a iterated local search (ILS). Para avaliar estas abordagens, é proposto um novo conjunto de instâncias adaptadas de problemas de escalonamento gerais do tipo flexible job-shop. De todos os métodos, o que apresenta os melhores resultados é o ILS-OBO obtendo melhores valores médios de gaps em tempos médios inferiores a 3 minutos.This work addresses a new type of scheduling problem which can be found in several real-world applications, mostly in manufacturing. Regarding shop floor configuration, the problem can be classified as flexible job-shop, where jobs can have different routes passing through resources and their operations have a set of eligible resources in which they can be performed. The processing characteristics addressed are release dates, precedence constraints (either between operations of the same job or between different jobs), resources capacity (including downtimes, changes in capacity, and infinite capacity), and setup times, which can be sequence-dependent or sequence-independent. The objective is to minimise the total number of tardy jobs. To tackle the newly proposed flexible job-shop scheduling problem (FJSP), a mixed integer linear programming model (MILP) is presented and new heuristic approaches are put forward. Three constructive heuristics, five improvement heuristics, and two metaheuristics are proposed. The constructive heuristics are based on simple dispatching rules, where the main differences among them concern the used dispatching rules and the way resources are assigned. The methods are named job-by-job (JBJ), operation-by-operation (OBO) and resource-by-resource (RBR). Within improvement heuristics, reassign and external exchange aim to change the resources assignment, internal exchange and swap intend changing the operations sequence, and reinsert-reassign is focused in simultaneously changing both parts. Some of the proposed heuristics are used within metaheuristic frameworks, namely greedy randomized adaptive search procedure (GRASP) and iterative local search (ILS). In order to evaluate these approaches, a new set of benchmark instances adapted from the general FJSP is proposed. Out of all methods, the one which shows the best average results is ILS-OBO obtaining the best average gap values in average times lower than 3 minutes

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

Makespan Minimization in Re-entrant Permutation Flow Shops

Re-entrant permutation flow shop problems occur in practical applications such as wafer manufacturing, paint shops, mold and die processes and textile industry. A re-entrant material flow means that the production jobs need to visit at least one working station multiple times. A comprehensive review gives an overview of the literature on re-entrant scheduling. The influence of missing operations received just little attention so far and splitting the jobs into sublots was not examined in re-entrant permutation flow shops before. The computational complexity of makespan minimization in re-entrant permutation flow shop problems requires heuristic solution approaches for large problem sizes. The problem provides promising structural properties for the application of a variable neighborhood search because of the repeated processing of jobs on several machines. Furthermore the different characteristics of lot streaming and their impact on the makespan of a schedule are examined in this thesis and the heuristic solution methods are adjusted to manage the problem’s extension

Modelos e métodos para problemas de dimensionamento de lotes e escalonamento

Tese de doutoramento em Engenharia Industrial e de SistemasO trabalho que se apresenta nesta tese relaciona-se com o desenvolvimento de modelos e de métodos para a resolução de dois problemas de planeamento da produção de médio/curto prazo. A principal motivação consiste na exploração e comparação de diferentes abordagens, baseadas em programação inteira mista, em modelos/métodos de decomposição e em métodos heurísticos, para os problemas em estudo. O primeiro problema, é um problema clássico de dimensionamento de lotes, que está associado às decisões de planeamento da produção de médio-prazo. O problema consiste na determinação de um plano de produção para vários produtos finais ao longo de um determinado horizonte temporal, que minimize todos os custos envolvidos e respeite restrições de procura e de capacidade. Para este problema desenvolve-se um novo modelo exacto, que resulta da aplicação dos princípios da decomposição de Dantzig-Wolfe múltipla a uma formulação de programação inteira mista para o problema. Os princípios gerais de aplicação desta decomposição são também apresentados neste trabalho. A potencial mais valia deste modelo relaciona-se com a obtenção de limites inferiores de boa qualidade. O modelo que resulta da decomposição de Dantzig-Wolfe múltipla é comparado com dois modelos de decomposição alternativos, que se obtêm aplicando directamente os princípios da decomposição de Dantzig-Wolfe, e com o modelo de programação inteira mista, resolvido directamente através de um software de estado-da-arte. Para determinar a solução óptima inteira dos modelos de decomposição aplica-se o método de partição e geração de colunas (branchand- price). São apresentados resultados computacionais partindo de um conjunto de instâncias da literatura, para os vários modelos e métodos. O segundo problema estudado neste trabalho surge associado ao planeamento de curto-prazo e combina as decisões de dimensionamento de lotes, com as decisões de afectação e escalonamento desses lotes. Este estudo foi motivado por um problema real da indústria têxtil, no qual se pretende definir um plano de produção para uma secção de tricotagem, onde os principais componentes dos produtos finais são realizados num conjunto de máquinas paralelas idênticas. Para este problema propõe-se um novo modelo de programação inteira mista, que se resolve através de um software de estadoda- arte. Paralelamente, propõem-se vários métodos heurísticos. Duas das heurísticas propostas são: uma heurística de fluxos em rede e escalonamento e uma heurística de ordenação e escalonamento. Estas heurísticas visam a obtenção de soluções com alguma qualidade em pouco tempo. Propõem-se ainda quatro algoritmos de pesquisa local, que têm em consideração características específicas do problema e que tentam melhorar a qualidade das soluções das heurísticas anteriores. Atendendo ao desempenho dos algoritmos de pesquisa local, estes são combinados através de mudanças sistemáticas das vizinhanças, dando origem a duas meta-heurísticas: uma de descida em vizinhanças variáveis e outra de pesquisa em vizinhanças variáveis. Para avaliar as soluções do modelo de programação inteira mista e dos métodos heurísticos sugere-se uma função de avaliação inovadora, que minimiza os atrasos totais e os níveis em curso de fabrico entre duas etapas sucessivas do processo produtivo. É ainda sugerida uma nova função de avaliação nos métodos heurísticos, também baseada na minimização dos atrasos totais e na minimização dos níveis em curso de fabrico. A principal vantagem desta segunda medida de avaliação é contabilizar de um modo mais rigoroso os níveis em curso de fabrico. Para avaliar o desempenho e a qualidade das soluções do modelo de programação inteira mista e dos métodos heurísticos, desenvolveu-se um gerador de instâncias, que gera instâncias semelhantes às do problema real.This work is associated with the development of models and methods for two medium/short term production planning problems. Our main motivation is the exploration and comparison of different approaches, based on mixed integer programming, on decomposition models and methods and on heuristics, for those two problems. The first one is a classical lot sizing problem associated with the medium-term production planning decisions. The problem consists of finding a production plan for several final items over a given planning horizon that minimizes the overall costs involved, while respecting demand and capacity constraints. An exact model based on a multiple Dantzig-Wolfe decomposition is developed. The general principles of this decomposition are presented in this work too. The potential benefit of this decomposition is the achievement of good quality lower bounds, although our purpose is to obtain integer optimal solutions. The resulting model of multiple Dantzig-Wolfe decomposition is compared with two alternative decomposition models that are obtained when applying directly the Dantzig-Wolfe decomposition principles, and is also compared with an integer programming formulation solved by a state-of-art software. The integer optimal solutions of all the decomposition models are obtained through branch-and-price algorithms. We present computational results for a set of instances from the literature. The second problem studied in this work is a short-term production planning problem that integrates lot sizing, assignment and scheduling decisions. This study was motivated by a real problem from a textile industry. The aim is to define a production plan for a knitting section where the main components of the final items are processed on a set of identical parallel machines. A new mixed integer programming model is proposed for this problem, as well as several heuristics. Two of those heuristics are: a network flow and scheduling heuristic and an ordering and scheduling heuristic. The purpose of these heuristics is to find good quality solutions quickly. Four local search based algorithms that consider specific characteristics of the problem are developed too, in order to try to improve the solutions of the previous heuristics. Taking into account the performance of the four local search heuristics, we combine them through systematic changes of neighborhoods, testing two metaheuristics: variable neighborhood descent and variable neighborhood search. To evaluate the mixed integer programming model solutions and the solutions of all the heuristics, an innovative evaluation function that minimizes a weighted sum of total tardiness and work-in-process levels between two successive production processes is suggested. We study another new evaluation function for the heuristic methods, which is related to the previous one. The main advantage of the second evaluation function over the first one is that it calculates in a more precise way the levels of workin- process inventory. The performance and quality of solutions of all the above presented methods for the second problem are evaluated using a set of instances that are similar to the real ones. Those instances were generated by an instance generator developed by us.Fundação para a Ciência e a Tecnologia (FCT) - SFRH/BD/38582/200