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.

O presente trabalho realiza o estudo e aplicação de um algoritmo de branch-and-price para a resolução de um problema de escalonamento de compressores. O problema é ligado à produção petrolífera, o qual consiste em definir um conjunto de compressores a serem ativados para fornecer gas de elevação a um conjunto de poços, atendendo toda demanda e minimizando os custos envolvidos. O problema é caracterizado por uma função objetivo não-convexa que é linearizada por partes de forma a ser formulada como um problema de programação inteira mista. A abordagem de geração de colunas é baseada na decomposição de Dantzig-Wolfe e apresenta melhores limitantes inferiores em relação à relaxação linear da formulação compacta. O branch-and-price é comparado ao solver CPLEX, sendo capaz de encontrar a solução ótima em menor tempo para um conjunto de instâncias, bem como melhores soluções factíveis para instâncias maiores em um período de tempo limitado. / This work presents the study and application of a branch-and-price algorithm for solving a compressor scheduling problem. The problem is related to oil production and consists of defining a set of compressors to be activated, supplying the gas-lift demand of a set of wells and minimizing the associated costs. The problem has a non-convex objective function, to which a piecewise-linear formulation has been proposed. This dissertation proposes a column generation approach based on the Dantzig-Wolfe decomposition, which achieves tighter lower bounds than the straightforward linear relaxation of the piecewise-linear formulation. The column generation was embedded in a branch-and-price algorithm and further compared with CPLEX, obtaining optimal solutions in lesser time for a set of instances. Further, the branch-and-price algorithm can find better feasible solutions for large instances under a limited processing time.

The main goal of a scheduling process is to decide when and how to execute each of the project's activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. Firstly, the developed algorithm is applied to a real-life software development project. Secondly, the performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

In deregulated electricity markets, generating companies submit energy bids which are derived from a self-schedule. In this thesis, we propose an improved semidefinite programming-based model for the self-scheduling problem. The model provides the profit-maximizing generation quantities of a single generator over a multi-period horizon and accounts for uncertainty in prices using robust optimization. Within this robust framework, the price information is represented analytically as an ellipsoid. The risk-adversity of the decision maker is taken into account by a scaling parameter. Hence, the focus of the model is directed toward the trade-off between profit and risk. The bounds obtained by the proposed approach are shown to be significantly better than those obtained by a mean-variance approach from the literature. We then apply the proposed model within a branch-and-bound algorithm to improve the quality of the solutions. The resulting solutions are also compared with the mean-variance approach, which is formulated as a mixed-integer quadratic programming problem. The results indicate that the proposed approach produces solutions which are closer to integrality and have lower relative error than the mean-variance approach.

In deregulated electricity markets, generating companies submit energy bids which are derived from a self-schedule. In this thesis, we propose an improved semidefinite programming-based model for the self-scheduling problem. The model provides the profit-maximizing generation quantities of a single generator over a multi-period horizon and accounts for uncertainty in prices using robust optimization. Within this robust framework, the price information is represented analytically as an ellipsoid. The risk-adversity of the decision maker is taken into account by a scaling parameter. Hence, the focus of the model is directed toward the trade-off between profit and risk. The bounds obtained by the proposed approach are shown to be significantly better than those obtained by a mean-variance approach from the literature. We then apply the proposed model within a branch-and-bound algorithm to improve the quality of the solutions. The resulting solutions are also compared with the mean-variance approach, which is formulated as a mixed-integer quadratic programming problem. The results indicate that the proposed approach produces solutions which are closer to integrality and have lower relative error than the mean-variance approach.

Cette thèse traite des lignes de traitement de surface qui sont des lignes dans lesquelles les pièces sont immergées dans une succession de cuves. Chaque cuve contient des bains qui affectent les propriétés mécaniques ou électriques des pièces. Ce type de ligne est utilisé, par exemple, pour la galvanoplastie. Les pièces sont montées sur des porteurs et transportées d'une cuve à l'autre par un robot. Le temps opératoire (ou temps pendant lequel la pièce reste dans la cuve) est borné. La borne inférieure est le temps minimum qui permet le traitement et la borne supérieure dépend du type de traitement (attaque acide, rinçage...).<br />Un objectif classique est de trouver les mouvements du robot qui maximisent la productivité, ce problème est communément appelé “hoist scheduling problem” (HSP). Lors de ce travail nous nous sommes attachés à une production cyclique. Nous avons proposé dans le cas d'une ligne à deux cuves une méthode permettant d'obtenir les cycles optimaux. Nous avons démontré, pour le cas d'une ligne équilibrée à trois cuves pour une production mono-produit, les caractéristiques des cycles optimaux ainsi qu'une méthode pour les obtenir. Ensuite, nous avons étudié le problème sur quatre machines dans le cas où les temps de trempe sont égaux et sans attente. Nous avons proposé les cycles optimaux dans le cas d'une production mono-produit. Enfin nous avons proposé une conjecture sur les cycles optimaux et en avons démontré certaines parties, dans le cas d'une ligne équilibrée avec un nombre de cuves quelconque et où les marges sur les temps de process sont nulles.

Session ID: TH-E1-4 / SCIS & ISIS 2010, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. December 8-12, 2010, Okayama Convention Center, Okayama, Japan

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.

O presente trabalho realiza o estudo e aplicação de um algoritmo de branch-and-price para a resolução de um problema de escalonamento de compressores. O problema é ligado à produção petrolífera, o qual consiste em definir um conjunto de compressores a serem ativados para fornecer gas de elevação a um conjunto de poços, atendendo toda demanda e minimizando os custos envolvidos. O problema é caracterizado por uma função objetivo não-convexa que é linearizada por partes de forma a ser formulada como um problema de programação inteira mista. A abordagem de geração de colunas é baseada na decomposição de Dantzig-Wolfe e apresenta melhores limitantes inferiores em relação à relaxação linear da formulação compacta. O branch-and-price é comparado ao solver CPLEX, sendo capaz de encontrar a solução ótima em menor tempo para um conjunto de instâncias, bem como melhores soluções factíveis para instâncias maiores em um período de tempo limitado. / This work presents the study and application of a branch-and-price algorithm for solving a compressor scheduling problem. The problem is related to oil production and consists of defining a set of compressors to be activated, supplying the gas-lift demand of a set of wells and minimizing the associated costs. The problem has a non-convex objective function, to which a piecewise-linear formulation has been proposed. This dissertation proposes a column generation approach based on the Dantzig-Wolfe decomposition, which achieves tighter lower bounds than the straightforward linear relaxation of the piecewise-linear formulation. The column generation was embedded in a branch-and-price algorithm and further compared with CPLEX, obtaining optimal solutions in lesser time for a set of instances. Further, the branch-and-price algorithm can find better feasible solutions for large instances under a limited processing time.

A logística de contêiner vem aumentando sua participação em volume de cargas transportadas, tornando-se a parcela mais significativa do tráfego de mercadorias. Com isso, o gerenciamento dos altos custos envolvidos com a aquisição, manutenção, manipulação e transporte desses contêineres tornam-se um problema relevante para as organizações. As alocações dos contêineres cheios e vazios são comumente vistos como dois sistemas distintos e estáticos e não de forma intregada e dinâmica. Há um número restrito de trabalhos na literatura desenvolvendo heurísticas integrando os sistemas, porém não foi encontrada uma formulação ótima para o problema. Logo, a questão para a dissertação é quão próximo estão os resultados das heurísticas encontradas na literatura, para o problema da alocação de contêineres, dos resultados ótimos. O presente trabalho apresenta uma formulação matemática para o problema de alocação dinâmica, e integrada, para contêineres cheios e vazios. A formulação foi testada com diversos cenários, objetivando saber o limite computacional das instâncias para a formulação. Como o problema é um problema NP-Hard, heurísticas são comumente apresentadas na literatura. Demonstra-se como podem ser realizadas comparações entre os resultados das heurísticas e os resultados ótimos e visam a constatação da importância de uma formulação ótima para comparações. / Containers' Logistics has increased their importance in the goods transportion and nowadays, has the most important share of them. With that in mind, the management of high costs of acquisition, maintenance, manipulation and transportation of them became a significant problem to organizations. The problem of empty container allocation and load container allocation are commonly treated as two distinct, and static, systems, which means without integration and not dynamically. Just a couple of examples could be found of the two systems dynamically integrated, and no optimal model was found. So, the question here is how close heuristics' results are from the optimal results. A mathematical formulation is presented to the problem concerned with the integration and the dynamics associated to it. The formulation was tested with several scenarios to determine the maximum size that could be tested with optimal results, in an acceptable computacional time. Since the problem is a NP-Hard problem, heuristics approach are commonly used. Here is demonstrated how could be compare optimal solutions of the formulation and solutions from heuristics, and aim to demonstrate the significance of the optimal formulation.