Le contrôle hétérarchique des systèmes de production flexibles (FMS) préconise un contrôle peu complexe et hautement réactif supporté par des entités décisionnelles locales (DEs). En dépit d'avancées prometteuses, ces architectures présentent un comportement myope car les DEs ont une visibilité informationnelle limitée sue les autres DEs, ce qui rend difficile la garantie d'une performance globale minimum. Cette thèse se concentre sur les approches permettant de réduire cette myopie. D'abord, une définition et une typologie de cette myopie dans les FMS sont proposées. Ensuite, nous proposons de traiter explicitement le comportement myope avec une architecture semi-hétérarchique. Dans celle-ci, une entité décisionnelle globale (GDE) traite différents types de décisions myopes à l'aide des différentes techniques d'optimisation basée sur la simulation (SbO). De plus, les SbO peuvent adopter plusieurs rôles, permettant de réduire le comportement myope de plusieurs façons. Il est également possible d'avoir plusieurs niveaux d'autonomie en appliquant différents modes d'interaction. Ainsi, notre approche accepte des configurations dans lesquelles certains comportements myopes sont réduits et d'autres sont acceptés. Notre approche a été instanciée pour contrôler la cellule flexible AIP- PRIMECA de l'Université de Valenciennes. Les résultats des simulations ont montré que l'architecture proposée peut réduire les comportements myopes en établissant un équilibre entre la réactivité et la performance globale. Des expérimentations réelles ont été réalisées sur la cellule AIP-PRIMECA pour des scenarios dynamiques et des résultats prometteurs ont été obtenus. / Heterarchical-based control for flexible manufacturing systems (FMS) localizes control capabilities in decisional entities (DE), resulting in highly reactive and low complex control architectures. However, these architectures present myopic behavior since DEs have limited visibility of other DEs and their behavior, making difficult to ensure certain global performance. This dissertation focuses on reducing myopic behavior. At first, a definition and a typology of myopic behavior in FMS is proposed. In this thesis, myopic behavior is dealt explicitly so global performance can be improved. Thus, we propose a semi-heterarchical architecture in which a global decisional entity (GDE) deals with different kinds of myopic decisions using simulation-based optimization (SbOs). Different optimization techniques can be used so myopic decisions can be dealt individually, favoring GDE modularity. Then, the SbOs can adopt different roles, being possible to reduce myopic behavior in different ways. More, it is also possible to grant local decisional entities with different autonomy levels by applying different interaction modes. In order to balance reactivity and global performance, our approach accepts configurations in which some myopic behaviors are reduced and others are accepted. Our approach was instantiated to control the assembly cell at Valenciennes AIPPRIMECA center. Simulation results showed that the proposed architecture reduces myopic behavior whereby it strikes a balance between reactivity and global performance. The real implementation on the assembly cell verified the effectiveness of our approach under realistic dynamic scenarios, and promising results were obtained.

Sistemas de Bicicletas Compartilhadas (SBCs) têm sido implantados e aprimorados nos últimos anos nas principais cidades do mundo. Neste tipo de sistema, usuários podem retirar e devolver bicicletas em qualquer estação da rede, desde que haja bicicleta e vaga disponível, respectivamente. Porém, devido às características de ocupação do solo em grandes centros urbanos, existe uma tendência natural de desbalanceamento nos fluxos dos usuários, fazendo com que em determinados horários certas estações fiquem lotadas de bicicletas enquanto outras estações estão vazias. Para mitigar este problema, gestores de SBCs utilizam veículos de carga para rebalancear o sistema (reposicionar as bicicletas entre as estações). Entretanto, usualmente, esse processo na prática não é realizado com auxílio de ferramentas quantitativas que tornem o processo racional ou maximizem sua eficácia. Nesse sentido, no presente trabalho é proposto um modelo híbrido de simulação com otimização, aplicado ao rebalanceamento de um SBC brasileiro e com potencial para utilização em sistemas reais com o objetivo de melhorar seus níveis de serviço. Além disso, apresenta-se uma análise de dados e a caracterização de uso deste SBC, um histórico de evolução de SBCs ao redor do mundo e sua bibliografia pertinente, a fim de registrá-los na literatura e de se obter maior compreensão deste tipo de sistema. / Bike Sharing Systems (BSSs) have been implemented and enhanced in several major cities around the world, during the past few years. In such systems, users can take off a bike and return it at any network\'s station, provided that there is a bike and a dock available, respectively. However, these systems face an operational problem, caused by the fact that users\' flows are not balanced, bringing on that, at some point in time, some stations will be completely full while others will be empty. To tackle this issue, cargo vehicles are used by BSS\'s operators to rebalance the system (relocate bicycles through the stations). However, in most cases this process is not supported by quantitative tools that make the process rational or maximize its effectiveness. In this sense, this work proposes a hybrid model of simulation with optimization, applied to the rebalance of a Brazilian BSS and with potential for use in real systems with the aim of improving their service levels. In addition, is presented a data analysis and a usage study of this specific BSS, a BSSs evolutionary study and its relevant literature with the purpose of registering them in the literature and achieving a superior understanding of the problem.

Simulation-based optimization methodologies are widely applied in real world optimization problems. In developing these methodologies, beside simulation models, algorithms play a critical role. One example is an evolutionary multi objective optimization algorithm known as Reference point-based Non-dominated Sorting Genetic Algorithm-II (R-NSGA-II), which has shown to have some promising results in this regard. Its successor, R-NSGA-II-adaptive diversity control (hereafter Adaptive Epsilon-R-NSGA-II (AE-R-NSGA-II) algorithm) is one of the latest proposed extensions of the R-NSGA-II algorithm and in the early stages of its development. So far, little research exists on its applicability and usefulness, especially in real world optimization problems. This thesis evaluates behavior and performance of AE-R-NSGA-II, and to the best of our knowledge is one of its kind. To this aim, we have investigated the algorithm in two experiments, using two benchmark functions, 10 performance measures, and a behavioral characteristics analysis method. The experiments are designed to (i) assess behavior and performance of AE-R-NSGA-II, (ii) and facilitate efficient use of the algorithm in real world optimization problems. This is achieved through the algorithm parameter configuration (parametric study) according to the problem characteristics. The behavior and performance of the algorithm in terms of diversity of the solutions obtained, and their convergence to the optimal Pareto front is studied in the first experiment through manipulating a parameter of the algorithm referred to as Adaptive epsilon coefficient value (C), and in the second experiment through manipulating the Reference point (R) according to the distance between the reference point and the global Pareto front. Therefore, as one contribution of this study two new diversity performance measures (called Modified spread, and Population diversity), and the behavioral characteristics analysis method called R-NSGA-II adaptive epsilon value have been introduced and applied. They can be modified and applied for the evaluation of any reference point based algorithm such as the AE-R-NSGA-II. Additionally, this project contributed to improving the Benchmark software, for instance by identifying new features that can facilitate future research in this area. Some of the findings of the study are as follows: (i) systematic changes of C and R parameters influence the diversity and convergence of the obtained solutions (to the optimal Pareto front and to the reference point), (ii) there is a tradeoff between the diversity and convergence speed, according to the systematic changes in the settings, (iii) the proposed diversity measures and the method are applicable and useful in combination with other performance measures. Moreover, we realized that because of the unexpected abnormal behaviors of the algorithm, in some cases the results are conflicting, therefore, impossible to interpret. This shows that still further research is required to verify the applicability and usefulness of AE-R-NSGA-II in practice. The knowledge gained in this study helps improving the algorithm.

In today’s world, simulation and optimization are playing a vital role in reducing the time, cost and preserving resources. In manufacturing industries, there are ample amount of problems that go on with the expansion of the industry. In such cases, to tackle these problems simulation can be helpful to check whether any change in the current situation makes any effect on the current efficiency of the overall plant. In the presented case study, a solution to the problem of internal and external logistics has been designed by using simulation and optimization to improve part of a material flow of an organization. Basically, the organization whose major production is established in the south of Sweden deals with the manufacturing and assembly of equipment. Before the dispatch, all of them go to the painting section which is the expansion of the present shop floor. However, the design and analysis of the material handling system to feed the new painting line which is going to be established by the organization is the aim of this case study. While achieving this aim the literature regarding the discrete event simulation, Lean and Simulation-Based optimization related to the material handling system has been done. Furthermore, the appropriate material handling systems along with the different scenarios were suggested to reduce the cost and the lead times between the production line and the new painting line. To support this process a methodology combining simulation, optimization and lean production has been implemented under the framework of the design and creation research strategy. In the Kaizen workshop organized at a company with managers and stakeholders, the designed scenarios were presented and after some discussion one of them was chosen and the selected scenario was designed and optimized. Moreover, the Simulation-Based multi-objective optimization has been helpful for the optimization of the designed model proposed as a final solution.

Sistemas de Bicicletas Compartilhadas (SBCs) têm sido implantados e aprimorados nos últimos anos nas principais cidades do mundo. Neste tipo de sistema, usuários podem retirar e devolver bicicletas em qualquer estação da rede, desde que haja bicicleta e vaga disponível, respectivamente. Porém, devido às características de ocupação do solo em grandes centros urbanos, existe uma tendência natural de desbalanceamento nos fluxos dos usuários, fazendo com que em determinados horários certas estações fiquem lotadas de bicicletas enquanto outras estações estão vazias. Para mitigar este problema, gestores de SBCs utilizam veículos de carga para rebalancear o sistema (reposicionar as bicicletas entre as estações). Entretanto, usualmente, esse processo na prática não é realizado com auxílio de ferramentas quantitativas que tornem o processo racional ou maximizem sua eficácia. Nesse sentido, no presente trabalho é proposto um modelo híbrido de simulação com otimização, aplicado ao rebalanceamento de um SBC brasileiro e com potencial para utilização em sistemas reais com o objetivo de melhorar seus níveis de serviço. Além disso, apresenta-se uma análise de dados e a caracterização de uso deste SBC, um histórico de evolução de SBCs ao redor do mundo e sua bibliografia pertinente, a fim de registrá-los na literatura e de se obter maior compreensão deste tipo de sistema. / Bike Sharing Systems (BSSs) have been implemented and enhanced in several major cities around the world, during the past few years. In such systems, users can take off a bike and return it at any network\'s station, provided that there is a bike and a dock available, respectively. However, these systems face an operational problem, caused by the fact that users\' flows are not balanced, bringing on that, at some point in time, some stations will be completely full while others will be empty. To tackle this issue, cargo vehicles are used by BSS\'s operators to rebalance the system (relocate bicycles through the stations). However, in most cases this process is not supported by quantitative tools that make the process rational or maximize its effectiveness. In this sense, this work proposes a hybrid model of simulation with optimization, applied to the rebalance of a Brazilian BSS and with potential for use in real systems with the aim of improving their service levels. In addition, is presented a data analysis and a usage study of this specific BSS, a BSSs evolutionary study and its relevant literature with the purpose of registering them in the literature and achieving a superior understanding of the problem.

This doctoral thesis investigates the application of simulation-based optimization (SBO) as an alternative to conventional optimization techniques when the inherent uncertainty and complex features of real manufacturing systems need to be considered. Inspired by a real-world production planning setting, we provide a general formulation of the situation as an extended knapsack problem. We proceed by proposing a solution approach based on single and multi-objective SBO models, which use simulation to capture the uncertainty and complexity of the manufacturing system and employ meta-heuristic optimizers to search for near-optimal solutions. Moreover, we consider the design of matheuristic approaches that combine the advantages of population-based meta-heuristics with mathematical programming techniques. More specifically, we consider the integration of mathematical programming techniques during the initialization stage of the single and multi-objective approaches as well as during the actual search process. Using data collected from a manufacturing company, we provide evidence for the advantages of our approaches over conventional methods (integer linear programming and chance-constrained programming) and highlight the synergies resulting from the combination of simulation, meta-heuristics and mathematical programming methods. In the context of the same real-world problem, we also analyse different single and multi-objective SBO models for robust optimization. We demonstrate that the choice of robustness measure and the sample size used during fitness evaluation are crucial considerations in designing an effective multi-objective model.

Diploma thesis describes using discrete simulation using with help Witness simulation software as a tool to support decision-making process and the company ECKELMANN l.l.c. This work briefly introduces business processes modeling and simulation problems. Creating a model of real production process and its subsequent optimization will help us map out the processes that take place in the company, enabling discovery of strengths and weaknesses.

This diploma thesis describes optimization of assembly working place of cylinder locks brand FAB company ASSA ABLOY Rychnov, s.r.o. Theoretical part deals with universal process for designing manufacturing systems and for description of selected analytical and engineered methods and ergonomics .The practical part shows evaluation of current situation and create new time standards. in accordance with are designed 3 variants of the solution for lay out working place. For each variant is created simulation model, which is explore especially working load and automatic filling machine. The various options are compared by weight valuation and the most optimal is determined by maximal work in process and optimal warehouse supply of input components.

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In arid countries both water scarcity and salinity represent the key factors which drastically limit crop yield in irrigated agriculture. In addition, relatively poor management practices with pretty low water productivity (WP) seriously aggravate the situation. In order to get “more crop per drop', i.e., to substantially improve water use efficiency, this thesis proposes the novel strategy NEMO (Nested Experimental, Modeling, and Optimization Strategy) for reliably evaluating an optimal irrigation schedule. The proposed methodology relies upon a close interaction between in-depth field investigations and physically based process modeling. It is tailored specifically to fit the requirements in resource-restricted regions. Comprehensive field experiments, on site measurements as well as various laboratory analyses provide a representative database for characterizing the relevant environmental parameters as e.g. the soil properties at the considered location and the prevailing climate. A substantial part of the data obtained from the field experiments provided the input for the internationally recognized SVAT software DAISY1 or APSIM2, both physically based irrigation models which have already been successfully applied in arid regions. APSIM - which is used in the advanced parts of the study - includes not only a process based model for soil moisture transport but also a plant physiological model which describes the plant behavior under specific irrigation scenarios for a selected crop throughout a growing season. The adaption of the irrigation model to local conditions and its preliminary parameterization firstly follows available guidelines and data for areas with similar climate and soil conditions. Reference data and deterministic weather data served to build up DAISY’s basic model files. DAISY is then used within the framework of the custom made and problem oriented optimization software GET-OPTIS for evaluating the corresponding optimal irrigation schedule for a first preliminary series of experiments (IrrEx1). A second series of field experiments (IrrEx2) was accompanied by transient soil moisture measurements, which served for evaluating the soil hydraulic parameters, while the obtained yield was used for calibrating the plant physiological model of APSIM. Taking still into account the stochastic nature of weather phenomena, a stochastic optimization with GET-OPTIS was then applied not only for the traditional full irrigation but also for the most important deficit irrigation and the irrigation with saline water. The obtained optimal irrigation schedules are subsequently used for a final series of rigorous irrigation experiments (IrrEx3) which specifically focused on: (1) full irrigation for high yields with most economic water application, (2) deficit irrigation aiming at a maximum yield with only a limited amount of irrigation water, and (3) full irrigation with saline irrigation water for maximum yield. At the harvesting time, the observed crop yield and the water productivity were compared - together with other plant characteristics - with the corresponding calculated values. The agreement between calculated and measured crop data was excellent. All the field experiments have been performed following a parallel use of the common traditional FAO class A-Pan method and the novel NEMO technology. Based on the outcome of the field experiments, the NEMO applications demonstrated a striking superiority throughout all scenarios as compared to the FAO method as regards economic efficiency and sustainable use of irrigation water in both aspects water quantity and salt accumulation. Contrary to common practice, the optimal NEMO irrigation schedule - which relies on stochastic weather data - has an extended validity. Together with the use of physical data and adequate process models, the developed methodology features a highly promising potential for generalizing the experimental findings for other, environmentally similar, regions. NEMO thus opens wide possibilities for a cost effective and sustainable long-term application to other arid or semi-arid areas.