This thesis aims to review and analyze the scheduling problem in general and Job Shop Scheduling Problem (JSSP) in particular and the solution techniques applied to these problems. The JSSP is the most general and popular hard combinational optimization problem in manufacturing systems. For the past sixty years, an enormous amount of research has been carried out to solve these problems. The literature review showed the inherent shortcomings of solutions to scheduling problems. This has directed researchers to develop hybrid approaches, as no single technique for scheduling has yet been successful in providing optimal solutions to these difficult problems, with much potential for improvements in the existing techniques. The hybrid approach complements and compensates for the limitations of each individual solution technique for better performance and improves results in solving both static and dynamic production scheduling environments. Over the past years, hybrid approaches have generally outperformed simple Genetic Algorithms (GAs). Therefore, two novel priority heuristic rules are developed: Index Based Heuristic and Hybrid Heuristic. These rules are applied to benchmark JSSP and compared with popular traditional rules. The results show that these new heuristic rules have outperformed the traditional heuristic rules over a wide range of benchmark JSSPs. Furthermore, a hybrid GA is developed as an alternate scheduling approach. The hybrid GA uses the novel heuristic rules in its key steps. The hybrid GA is applied to benchmark JSSPs. The hybrid GA is also tested on benchmark flow shop scheduling problems and industrial case studies. The hybrid GA successfully found solutions to JSSPs and is not problem dependent. The hybrid GA performance across the case studies has proved that the developed scheduling model can be applied to any real-world scheduling problem for achieving optimal or near-optimal solutions. This shows the effectiveness of the hybrid GA in real-world scheduling problems. In conclusion, all the research objectives are achieved. Finaly, the future work for the developed heuristic rules and the hybrid GA are discussed and recommendations are made on the basis of the results.

Production scheduling is one of the most important issues in the planning and operation of manufacturing systems. Customers increasingly expect to receive the right product at the right price at the right time. Various problems experienced in manufacturing, for example low machine utilization and excessive work-in-process, can be attributed directly to inadequate scheduling. In this dissertation a production scheduling algorithm is developed for Optimatix, a South African-based company specializing in supply chain optimization. To address the complex requirements of the customer, the problem was modeled as a flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and production down time. The algorithm development process focused on investigating the application of both particle swarm optimization (PSO) and differential evolution (DE) to production scheduling environments characterized by multiple machines and multiple objectives. Alternative problem representations, algorithm variations and multi-objective optimization strategies were evaluated to obtain an algorithm which performs well against both existing rule-based algorithms and an existing complex flexible job shop scheduling solution strategy. Finally, the generality of the priority-based algorithm was evaluated by applying it to the scheduling of production and maintenance activities at Centurion Ice Cream and Sweets. The production environment was modeled as a multi-objective uniform parallel machine shop problem with sequence-dependent set-up times and unavailability intervals. A self-adaptive modified vector evaluated DE algorithm was developed and compared to classical PSO and DE vector evaluated algorithms. Promising results were obtained with respect to the suitability of the algorithms for solving a range of multi-objective multiple machine scheduling problems. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Industrial and Systems Engineering / unrestricted

No / The Job Shop Scheduling Problem (JSSP) is a hard combinatorial optimisation problem. This paper presents a heuristic-based Genetic Algorithm (GA) or Hybrid Genetic Algorithm (HGA) with the aim of overcoming the GA deficiency of fine tuning of solution around the optimum, and to achieve optimal or near optimal solutions for benchmark JSSP. The paper also presents a detail GA parameter analysis (also called sensitivity analysis) for a wide range of benchmark problems from JSSP. The findings from the sensitivity analysis or best possible parameter combination are then used in the proposed HGA for optimal or near optimal solutions. The experimental results of the HGA for several benchmark problems are encouraging and show that HGA has achieved optimal solutions for more than 90% of the benchmark problems considered in this paper. The presented results will provide a reference for selection of GA parameters for heuristic-based GAs for JSSP.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.

Especialmente nas últimas quatro décadas, muitos estudos se voltaram às variáveis determinantes para a implementação efetiva de sistemas flexíveis de manufatura, tais como seu design, sequenciamento e controle. Neste ínterim, o manejo apropriado do conjunto de ferramentas necessárias para a fabricação de um respectivo lote de produtos foi destacado como fator crucial no desempenho do sistema de produção como um todo. Neste trabalho, abordamos a otimização do número de inserções e remoções de ferramentas no magazine de uma ou mais máquinas numericamente controladas, admitindo-se que uma parcela significativa do tempo de produção é dispensada com estas trocas de ferramentas. De forma mais precisa, a minimização do número de trocas de ferramentas consiste em determinar a ordem de processamento de um conjunto de tarefas, bem como o carregamento ótimo do(s) compartimento(s) de ferramentas da(s) máquina(s), a fim de que o número de trocas seja minimizado. Como demostrado na literatura, mesmo o caso restrito à existência de apenas uma máquina de manufatura (MTSP, do inglês Minimization of Tool Switches Problem) é um problema NP-difícil, o que pode justificar o fato observado de que a maioria dos métodos de solução existentes o abordam de maneira heurística. Consequentemente, concluímos que a extensão ao contexto de múltiplas máquinas é também um problema NP-difícil, intrinsecamente complicado de se resolver. Nosso objetivo consiste em estudar formas eficientes de otimizar o número de trocas de ferramentas em ambientes equipados com máquinas flexíveis de manufatura. Para tanto, abordamos o problema básico, MTSP, e duas de suas variantes, em níveis crescentes de abrangência, que consideram o sequenciamento de tarefas em um conjunto de: (i) máquinas paralelas e idênticas (IPMTC, do inglês Identical Parallel Machines problem with Tooling Constraints); e (ii) máquinas paralelas e idênticas inseridas em um ambiente do tipo job shop (JSSPTC, do inglês Job Shop Scheduling Problem with Tooling Constraints). Classificamos as principais contribuições desta tese com respeito a três aspectos. Primeiramente, empurramos as fronteiras da literatura do MTSP propondo formulações matemáticas para os problemas IPMTC e JSSPTC. Desenvolvemos, também, algoritmos baseados em diferentes técnicas de resolução, como redução de domínio, Path relinking, Adaptive large neighborhood search e a elaboração de regras de despacho. Por último, com o intuito de bem avaliar a eficiência e o alcance de nossos métodos, propomos três novos conjuntos de instâncias teste. Acreditamos, assim, que este trabalho contribui positivamente com pesquisas futuras em um cenário abrangente dentro da minimização das trocas de ferramentas em um sistema flexível de manufatura. / Several studies, especially in the last four decades, have focused on decisive elements for the effective implementation of flexible manufacturing systems, such as their design, scheduling and control. In the meantime, the appropriate management of the set of tools needed to manufacture a certain lot of products has been highlighted as a crucial factor in the performance of the production system as a whole. This work deals with the optimization of the number of insertions and removals from the magazine of one or more numerical controlled machines, assuming that a significant part of the production time is wasted with such tool switches. More precisely, the minimization of tool switches problem (MTSP) consists on determining the processing order of a set of jobs, as well as the optimal loading of the magazine(s) of the machine(s), so that the total number of switches is minimized. As formally demonstrated in the literature, the MTSP is a NP-hard problem even when considering the existence of only one manufacturing machine, which could justify the fact that most of the solution methods tackles it heuristically. We thus conclude that its extension to the case of multiples machines is also NP-hard and, therefore, a problem intrinsically difficult to solve. Our goal consists in studying efficient ways to optimize the number of tool switches in environments equipped with flexible manufacturing machines. For that, we address the basic problem, MTSP, and two MTSP variants, in increasing levels of reach, that consider the job sequencing in a set of: (i) identical parallel machines (Identical Parallel Machines problem with Tooling Constraints, IPMTC); and (ii) identical parallel machines inserted in a job shop environment (Job Shop Scheduling Problem with Tooling Constraints, JSSPTC). The main contributions of this thesis are classified according three aspects. First, we pushed the frontier of the MTSP literature by proposing mathematical formulations for IPMTC and JSSPTC. We also developed algorithms based on different solution techniques, such as domain reduction, Path Relinking, Adaptive Large Neighborhood Search and dispatching rules. Finally, to fully evaluate the effectiveness and limits of our methods, three new sets of benchmark instances were generated. We believe that this work contributes positively to the future of research in a broad scenario inside the minimization of tool switches in flexible manufacturing systems.

In this thesis, the Job Shop Scheduling Problem (JSSP) is the problem of interest. The classical JSSP is well-known as an NP-hard problem. Although with current computational capabilities, the small problems are solvable using deterministic methods, it is out of reach when they are larger in size. The complexity of JSSP is further increased when process interruptions, such as machine breakdown and/or machine unavailability, are introduced. Over the last few decades, several stochastic algorithms have been proposed to solve JSSPs. However, none of them are suitable for all kinds of problems. Genetic and Memetic algorithms have proved their effectiveness in these regards, because of their diverse searching behavior. In this thesis, we have developed one genetic algorithm and three different Memetic Algorithms (MAs) for solving JSSPs. Three priority rules are designed, namely partial re-ordering, gap reduction and restricted swapping, and these have been used as local search techniques in designing our MAs. We have solved 40 well-known benchmark problems and compared the results obtained with some of the established algorithms available in the literature. Our algorithm clearly outperforms those established algorithms. For better justification of the superiority of MAs over GA, we have performed statistical significance testing (Student's t-test). The experimental results show that MA, as compared to GA, not only significantly improves the quality of solutions, but also reduces the overall computation. We have extended our work by proposing an improved local search technique, shifted gap-reduction (SGR), which improves the performance of MAs when tested with the relatively difficult test problems. We have also modified the new algorithm to accommodate JSSPs with machine unavailability and also developed a new reactive scheduling technique to re-optimize the schedule after machine breakdowns. We have considered two scenarios of machine unavailability. Firstly, where the unavailability information is available in advance (predictive), and secondly, where the information is known after a real breakdown (reactive). We show that the revised schedule is mostly able to recover if the interruptions occur during the early stages of the schedules. We also confirm that the effect of a single continuous breakdown has more impact compared to short multiple breakdowns, even if the total durations of the breakdowns are the same. Finally, for convenience of implementation, we have developed a decision support system (DSS). In the DSS, we have built a graphical user interface (GUI) for user friendly data inputs, model choices, and output generation. This DSS tool will help users in solving JSSPs without understanding the complexity of the problem and solution approaches, as well as will contribute in reducing the computational and operational costs.

Made available in DSpace on 2015-03-05T13:58:26Z (GMT). No. of bitstreams: 0 Previous issue date: 1 / Universidade do Vale do Rio dos Sinos / Neste trabalho é abordado o Problema de Escalonamento em um job shop, considerando restrições de datas de entrega, turnos de produção e tempo de setup entre operações. Considera-se um ambiente de Sistema de Manufatura flexível, que dado ao alto nível de automação, permite a previsibilidade dos processos de carregamento dos recursos à área de processamento. O problema foi modelado através de uma Função Objetivo fn composta de três variáveis de decisão. A importância da contribuição de cada variável para o valor de fn é gerida pela atribuição de valores aos pesos associados às variáveis. Na abordagem proposta, são utilizadas técnicas de Tecnologia de Grupo e Busca Tabu. O modelo implementado é uma modificação da técnica i TSAB, proposta por Nowicki e Smutnicki, a qual apresenta bons resultados no tratamento do Problema de Escalonamento em um job shop PEJS clássico. A consideração das restrições adicionais ao PEJS aumenta a complexidade do modelo implementado, porém, deixa o problema mais próximo da realidade. / In this work the Job Shop Scheduling Problem is studied, considering due dates, production turns and tooling constraints. This problem is applied in a Flexible Manufacturing System, which possesses high degree of automation, allowing previsibility in the processes of loading and unloading jobs on the machines. The problem is modeled through a objective function fn composed by three weighted decision variables. The importance of each variable in the fn final value is managed through assignment of values to the weights of these variables. In the proposed approach, it was used Group Technology and Tabu Search techniques. The implemented model is a modification of the i TSAB technique, proposed by Nowicki and Smutniki. The consideration of adicional constraints in the Job Shop Scheduling Problem increases the complexity of the implementation, otherwise, makes the problem closer to the industrial reality. The model was validated using benchmark instances, in which the data from the addional constraints were added.

We meet with solving of optimization problems every day, when we try to do our tasks in the best way. An Ant Colony Optimization is an algorithm inspired by behavior of ants seeking a source of food. The Ant Colony Optimization is successfuly using on optimization tasks, on which is not possible to use a classical optimization methods. A Genetic Algorithm is inspired by transmision of a genetic information during crossover. The Genetic Algorithm is used for solving optimization tasks like the ACO algorithm. The result of my master's thesis is created simulator for solving choosen optimization tasks by the ACO algorithm and the Genetic Algorithm and a comparison of gained results on implemented tasks.

[pt] GRASP (Greedy Randomized Adaptive Search Procedure)é uma metaeurística de partidas múltiplas usada para obter soluções para problemas de otimização combinatória. Nesse trabalho. A metaheurística GRASP tem sido usada para obter soluções de qualidade para muitos problemas de otimização combinatória. Nesse trabalho é proposta uma metodologia para análise do comportamento da metaheurística GRASP. Também são propostas estratégias de hibridização com o religamento de caminhos. Essas estratégias foram desenvolvidas para o problema de atribuição de três índices (AP3) e para o problema de escalonamento de tarefas conhecido na literatura como job-shop schedulling problem (JSP) e são analisadas de acordo com a metodologia proposta. A metodologia para análise do comportamento do método GRASP pode ser usada para prever a partir da versão seqüencial do algoritmo, como a qualidade da solução do algoritmo implementado em paralelo irá variar. Os algoritmos GRASPs desenvolvidos para AP3 e para JSP foram paralelizados e os resultados são comparados aos resultados obtidos usando a metodologia proposta. / [en] GRASP (Greedy Randomized Adaptive Search Procedure) is a multi-start metaheuristic for combinatorial optimization problems. GRASP has been used to find quality solutions of several combinatorial optimization problems. In this work we describe a methodology for analysis of GRASP. Hybrid strategies of GRASP with path relinking are also proposed. These strategies are studied for the 3-index assignment problem (AP3) and for the job-shop schedulling problem (JSP) and are analyzed according to the methodology proposed. The methodology for analysis of GRASP is used to predict qualitatively how the quality of the solution varies in a parallel independent GRASP, using the data of the GRASP sequential version as input. The GRASPs for the AP3 and for the JSP are parallelized and the computational results are compared to the results obtained using the methodology proposed.