Ce travail de thèse s'inscrit dans le cadre d'une collaboration industrielle avec la société Volume Software pour le développement du module d'ordonnancement du logiciel "DirectPlanning". Dans ce travail, nous étudions le problème de job shop flexible multicritère et le problème de job shop multiressource multicritère. Notre objectif est de déterminer une approximation du front de Pareto. Nous avons proposé des algorithmes de résolution approchés et plus particulièrement des algorithmes de recherche Tabou et des algorithmes génétiques. Nous avons proposé différentes versions de nos méthodes pour les deux problèmes considérés. Des expérimentations ont été réalisées et montrent les bonnes performances de nos algorithmes, à la fois d'un point de vue qualité des résultats et d'un point de vue de la rapidité des méthodes.

O Job Shop Scheduling Problem é um problema NP-Difícil que chama a atenção de muitos pesquisadores devido seu desafio matemático e sua aplicabilidade em contextos reais. Geralmente, principalmente em cenários próximos aos de fábricas e indústrias, obter um escalonamento ótimo por meio de métodos computacionais exatos implica em um alto desprendimento de tempo. Em contrapartida, devido às exigências de um mercado cada vez mais competitivo, as decisões de onde, como, quando e com o que produzir devem ser tomadas rapidamente. O presente trabalho propõe o desenvolvimento de um método heurístico Beam Search para solucionar o Job Shop Scheduling Problem e o Flexible Job Shop Scheduling Problem. Para isso, inicialmente um algoritmo do tipo list scheduling é definido e então o método Beam Search é construído baseado neste algoritmo. Os métodos propostos foram avaliados em diferentes níveis de complexidade utilizando instâncias da literatura que retratam diferentes cenários de planejamento. Em linhas gerais, as soluções encontradas se mostraram bastante competitivas quando comparadas a outras soluções da literatura. / The Job Shop Scheduling Problem is a NP-Hard problem which draws the attention of researchers due to both its mathematical challenge and its applicability in real contexts. Usually, mainly in industry and factory environments, an optimal schedule got by the use of exact computational methods implies in a long spending time. On the other hand, due to a more and more competitive marketplace, the decisions on where, how, when and with which to produce must be taken quickly. The present work proposes the development of an heuristic Beam Search method to solve both the Job Shop Scheduling Problem and the Flexible Job Shop Scheduling Problem. To that end, at rst a list scheduling algorithm is dened and then the Beam Search method is built based on the list scheduling algorithm. The proposed methods were evaluated over dierent complexity levels using instances from the literature that report dierent planning environments. In general terms, the solutions implemented have been proved very competitive when compared against other solutions in the literature.

Nesta tese apresentamos duas estratégias para resolver o problema de job-shop flexível com o objetivo de minimizar o makespan. A primeira estratégia utiliza um algoritmo branch and cut (B&C) e a segunda abordagens matheuristics. O algoritmo B&C utiliza novas classes de inequações válidas, originalmente formulada para o problema de job-shop e estendida para o problema em questão. Para que as inequações válidas sejam eficientes, o modelo proposto por Birgin et al, (2014) (A milp model for an extended version of the fexible job shop problem. Optimization Letters, Springer, v. 8, n. 4, 1417-1431), é reformulado (MILP-2). A segunda estratégia utiliza as matheuristcs local branching e diversification, refining and tight-refining. Os experimentos computacionais mostraram que a inclusão dos planos de corte melhoram a relaxação do modelo MILP-2 e a qualidade das soluções. O algoritmo B&C reduziu o gap e o número de nós explorados para uma grande quantidade de instâncias. As abordagens matheuristics tiveram um excelente desempenho. Do total de 59 instâncias analisadas, somente em 3 problemas a resolução do modelo MILP-1 obteve melhores resultados do que as abordagens matheuristcs / This thesis proposes two approaches to solve the flexible job-shop scheduling problem to minimize the makespan. The first strategy uses a branch and cut algorithm (B&C) and the second approach is based on matheuristics. The B&C algorithm uses new classes of valid inequalities, originally formulated for job-shop scheduling problems and extended to the problem at hand. The second approach uses the matheuristics local branching and diversification, refining and tight-refining. For all valid inequalities to be effective, the precedence variable based model proposed by Birgin et al, (2014) (A milp model for an extended version of the fexible job shop problem. Optimization Letters, Springer, v. 8, n. 4, 1417-1431), is reformulated (MILP-2). The computational experiments showed that the inclusion of cutting planes tightened the linear programming relaxations and improved the quality of solutions. B&C algorithm reduced the gap value and the number of nodes explored in a large number of instances. The matheuristics approaches had an excellent performance. From 59 instances analized, MILP-1-Gurobi showed better results than matheuristics approaches in only 3 problems

This paper focuses on characterization of scheduling in systems with one or multiple machines. There are different types of tasks given, with which we can encounter in scheduling. At the beginning, introduce basic concepts of production scheduling. The second chapter is a flowshop problem with its history and projections for the flowshop problem. In the next chapter is a modification of flowshop problem called hybrid flowshop, which is divided according flexibility to hybrid flowshop with processing flexibility and hybrid flowshop with routing flexibility. Another chapter is open shop problem, which have some differences compare with the above mentioned types. The last chapter is a job shop, stating in a graphical solution for two machines and a brief description of the algorithm Shifting bottleneck.

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three sub-problems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

no / No / Scheduling systems based on traditional heuristic rules, which deal with the complexities of manufacturing systems, have been used by researchers for the past six decades. These heuristics rules prioritise all jobs that are waiting to be processed on a resource. In this paper, a novel Index Based Heuristic (IBH) solution for the Job Shop Scheduling Problem (JSSP) is presented with the objective of minimising the overall Makespan (Cmax). The JSSP is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. JSSP suits the challenges of current manufacturing environments. The proposed IBH calculates the indices of candidate jobs and assigns the job with the lower index value to the available machine. To minimise the gap between jobs, a swap technique is introduced. The swap technique takes candidate jobs for a machine and swaps them without violating the precedence constraint. Several benchmark problems are solved from the literature to test the validity and effectiveness of the proposed heuristic. The results show that the proposed IBH based algorithm outperforms the traditional heuristics and is a valid methodology for JSSP optimization.

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Made available in DSpace on 2015-03-05T13:56:58Z (GMT). No. of bitstreams: 0 Previous issue date: 20 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho tem como objetivo a geração de um modelo de escalonamento aplicado ao Jobshop Scheduling Problem num Sistema de Manufatura Flexível que considera o tempo total de produção (makespan), o tempo total de atraso, o tempo total parado e o tempo total ocioso.O modelo proposto é composto por: (a) uma função objetivo que reflete, através de suas variáveis de decisão e seus pesos respectivos, as estratégias de otimização, e de (b) uma arquitetura que está dividida em cinco fases. O modelo utilizou o algoritmo Busca Tabu que,através de duas estratégias de geração de vizinhanças, busca a otimização da função objetivo. A arquitetura do modelo baseia-se na extração da demanda de produção, na Tecnologia de Grupo, nas Regras de Despacho, no Algoritmo Busca Tabu e na gravação do plano de produção, para tratar os Problemas de Seleção de Partes (Famílias de Partes) e do Escalonamento. Foram realizados, através de um estudo de caso, diversos experimentos que possibilitaram a comparação de estratégias de otimiza / This paper has the aim of generating a scheduling model applied to Job-shop Scheduling Problem in Flexible Manufacturing System, which considers the makespan, total tardiness time, total stop time, total idle time. The model proposed is composed for: (a) an objective function that reflects, through its variables of decision and its weights, the optimization strategies, and (b) arquitecture that is divided in five phases. The model used the Tabu Search algorithm which, through two strategies neighborhoods generation, searching the objective function optimization. The model architecture is based on extraction of production demand, in the Group Technology, in the Dispatching Rules, in the Tabu Search algorithm and save production plan, to deal the Part Selections (Part Families) and Scheduling Problems.Through a study of case, it has been realized several experiments which makes it possible the comparison of optimization strategies and real scheduling, and which proves conflicts in decision variables. For mo