Equivalent and efficient optimization models for an industrial discrete event system with alternative structural configurations

Discrete event systems in applications, such as industry and supply chain,may show a very complex behavior. For this reason, their design and operation may be carried out by the application of optimization techniques for decision making in order to obtain their highest performance. In a general approach, it is possible to implement these optimization techniques by means of the simulation of a Petri net model, which may require an intensive use of computational resources. One key factor in the computational cost of simulation-based optimization is the size of the model of the system; hence, it may be useful to apply techniques to reduce it. This paper analyzes the relationship between two Petri net formalisms, currently used in the design of discrete event systems, where it is usual to count on a set of alternative structural configurations.These formalisms are a particular type of parametric Petri nets, called compound Petri nets, and a set of alternative Petri nets. The development of equivalent models under these formalisms and the formal proof of this equivalence are the main topics of the paper.The basis for this formal approach is the graph of reachable markings, a powerful tool able to represent the behavior of a discrete event system and, hence, to show the equivalence between two different Petri net models. One immediate application of this equivalence is the substitution of a large model of a system by a more compact one, whose simulation may be less demanding in the use of computational resources

Simulation Modelling Practice and Theory

The influx of data in the world today needs analysis that no one method can handle. Some reports estimated the influx of data would reach 163 zitabytes by 2025, hence the need for simulation and modeling theory and practice. Simulation and modeling tools and techniques are most important in this day and age. While simulation carries the needed work, tools for visualizing the results help in the decision-making process. Simulation ranges from a simple queue to molecular dynamics, including seismic reliability analysis, structural integrity assessment, games, reliability engineering, and system safety. This book will introduce practitioners, researchers, and novice users to simulation and modeling, and to the world of imagination

Probabilistic Fail-Safe Size Optimization of Aerospace Structures Under Several Sources of Uncertainty

Programa Oficial de Doutoramento en Enxeñaría Civil . 5011V01[Abstract] This work presents a research on the probabilistic fail-safe size optimization of aerospace structures. The goal is to design minimum weight structures taking into account possible damage scenarios, as well as several sources of uncertainty. The first type of uncertainty refers to the one present in structural parameters, which can be characterized as aleatory, epistemic or hybrid uncertainty. The second type of uncertainty pertains to the ignorance of what partial collapse will occur in an accidental failure event. The last type of uncertainty is related to debris characterization in the event of an engine failure, due to the randomness in the parameters defining the debris, such as the number of impacts or the location and size of holes in the fuselage. Several methodologies have been developed to deal with the first type of uncertainty: fail-safe Reliability-Based Design Optimization (fail-safe RBDO) using the Sequential Optimization and Reliability Assessment method (SORA), fail-safe Evidence-Based Design Optimization (fail-safe EBDO) using the decoupled EBDO approach, and fail-safe Hybrid Reliability-Based Design Optimization (fail-safe HRBDO) using a fast-convergence decoupled strategy that was developed by the author to deal with random and evidence variables simultaneously. Concerning the second type of uncertainty, two methodologies are proposed in this research to address the probability of occurrence of each damage scenario: the Probability-Damage approach for Fail-Safe Design Optimization (PDFSO) and the Reliability-Index based strategy for the Probability-Damage Approach in Fail-Safe Design Optimization ( -PDFSO) where the latter also considers aleatory uncertainty in random structural parameters. Several application examples have been carried out, including a curved stiffened panel of an aircraft fuselage and the rear section of an aircraft fuselage. The last contribution of this research is the development of a framework (DamageCreator) to automatically generate a large enough set of possible damage scenarios from an aircraft mesh, due to an uncontained engine or propeller blade failure event. The debris parameters, such as number of impacts, impact area, spread angles, hole location, debris orientation, size, and velocity, can be considered as random or deterministic. The tool is applied to a cylindrical barrel structure and to a fuselagewing assembly corresponding to a narrow-body aircraft. The programming codes of the proposed methodologies were fully implemented by the author using Matlab and Python environments, as well as Abaqus and Nastran as finite element solvers.[Resumen] Este trabajo presenta una investigación sobre la optimización de tamaño a prueba de fallos de estructuras aeronáuticas en régimen probabilista. El objetivo es diseñar estructuras de peso mínimo teniendo en cuenta los posibles escenarios de daño, así como varias fuentes de incertidumbre. El primer tipo de incertidumbre se refiere a la presente en parámetros estructurales, que puede caracterizarse como incertidumbre aleatoria, epistémica o híbrida. El segundo tipo de incertidumbre se refiere al desconocimiento de qué colapso parcial se producirá en un evento de fallo accidental. El último tipo de incertidumbre está relacionado con la caracterización de los escombros en caso de fallo del motor, debido a la aleatoriedad en los parámetros que definen los escombros, como el número de impactos o la ubicación y el tamaño de los agujeros en el fuselaje. Se han desarrollado varias metodologías para tratar el primer tipo de incertidumbre: la optimización de diseño basada en la fiabilidad a prueba de fallos (RBDO a prueba de fallos) utilizando el método de optimización secuencial y evaluación de la fiabilidad (SORA), la optimización de diseño basada en la evidencia a prueba de fallos (EBDO a prueba de fallos) utilizando el enfoque EBDO desacoplado, y la optimización de diseño basada en la fiabilidad híbrida a prueba de fallos (HRBDO a prueba de fallos) utilizando una estrategia desacoplada de convergencia rápida que fue desarrollada por la autora para tratar las variables aleatorias y de evidencia simultáneamente. En cuanto al segundo tipo de incertidumbre, en esta investigación se proponen dos metodologías para tratar la probabilidad de ocurrencia de cada escenario de daño: el enfoque de la probabilidaddaño para la optimización de diseño a prueba de fallos (PDFSO) y la estrategia basada en el índice de fiabilidad para el enfoque de la probabilidad-daño en la optimización de diseño a prueba de fallos ( -PDFSO), donde esta última también considera la incertidumbre en los parámetros estructurales aleatorios. Se han llevado a cabo varios ejemplos de aplicación, incluyendo un panel curvo rigidizado del fuselaje de un avión y la sección trasera del fuselaje de un avión. La última contribución de esta investigación es el desarrollo de un enfoque (DamageCreator) para generar automáticamente un conjunto suficientemente amplio de posibles escenarios de daño a partir de la malla de una aeronave, debido a un evento de fallo del motor o a un despendimiento de las palas de la hélice. Los parámetros que definen los escombros, como el número de impactos, el área de impacto, los ángulos de propagación, la ubicación de los agujeros, la orientación, el tamaño y la velocidad de los escombros, pueden considerarse aleatorios o deterministas. La herramienta se aplica a una estructura de barril cilíndrico y a un conjunto fuselaje-ala correspondiente a un avión de fuselaje estrecho. Los códigos de programación de las metodologías propuestas fueron implementados íntegramente por la autora utilizando los entornos Matlab y Python, así como Abaqus y Nastran como solvers de elementos finitos.[Resumo] Este traballo presenta unha investigación sobre a optimización de tamaño a proba de fallos de estruturas aeronáuticas en réxime probabilístico. O obxectivo é deseñar estruturas de peso mínimo tendo en conta os posibles escenarios de dano, así como diversas fontes de incerteza. O primeiro tipo de incerteza refírese á presente nos parámetros estruturais, que poden caracterizarse como incerteza aleatoria, epistémica ou híbrida. O segundo tipo de incerteza refírese ao descoñecemento de que colapso parcial se producirá nun caso de fallo accidental. O último tipo de incerteza está relacionado coa caracterización dos cascallos en caso de fallo do motor, debido á aleatoriedade nos parámetros que definen os cascallos, como o número de impactos ou a localización e tamaño dos buratos da fuselaxe. Desenvolvéronse varias metodoloxías para facer fronte ao primeiro tipo de incerteza: a optimización de deseño baseada na fiabilidade a proba de fallos (RBDO a proba de fallos) empregando o método de optimización secuencial e avaliación da fiabilidade (SORA), a optimización de deseño baseada na evidencia a proba de fallos (EBDO a proba de fallos) empregando o enfoque EBDO desacoplado, e a optimizacion de deseño baseada na fiabilidade híbrida a proba de fallos (HRBDO a proba de fallos) empregando unha estratexia desacoplada de converxencia rápida que foi desenvolvida pola autora para tratar as variables aleatorias e de evidencia simultáneamente. En canto ao segundo tipo de incerteza, esta investigación propón dúas metodoloxías para tratar a probabilidade de aparición de cada escenario de dano: o enfoque da probabilidade-dano para a optimización do deseño a proba de fallos (PDFSO) e a estratexia baseada no índice de fiabilidade para o enfoque da probabilidade-dano na optimización do deseño a proba de fallos ( -PDFSO), onde este último tamén considera a incerteza nos parámetros estruturais aleatorios. Leváronse a cabo varios exemplos de aplicación, incluíndo un panel curvo rixidizado dunha fuselaxe de avión e a sección traseira dunha fuselaxe de avión. A última contribución desta investigación é o desenvolvemento dun enfoque (DamageCreator) para xerar automaticamente un conxunto suficientemente amplo de posibles escenarios de dano a partir da malla dunha aeronave, debido ao fallo do motor ou ao desprendemento das palas da hélice. Os parámetros que definen os cascallos, como o número de impactos, a área de impacto, os ángulos de propagación, a localización dos buracos, a orientación, o tamaño e a velocidade dos cascallos, poden considerarse aleatorios ou deterministas. A ferramenta aplícase a unha estrutura de barril cilíndrica e a un conxunto fuselaxe-ás correspondentes a un avión de corpo estreito. Os códigos de programación das metodoloxías propostas foron totalmente implementados pola autora empregando entornos de Matlab e Python, así como Abaqus e Nastran como solvers de elementos finitos.Funding for this work, including the research stay at Cambridge University, has been possible thanks to the sponsorship of the Galician Government through the grant “axudas de apoio á etapa predoutoral cofinanciadas parcialmente polo programa operativo FSE Galicia 2014-2020” under identification number ED481A-2018/193. I am fully grateful for the supportXunta de Galicia; ED481A-2018/19

Petri Net Models Optimized for Simulation

Petri nets and simulation are a modeling paradigm and a tool, respectively, which may be successfully combined for diverse applications, such as performance evaluation, decision support, or training on complex systems. Simulation may require significant computer resources; hence, in this chapter, two Petri net-based formalisms are analyzed for profiting from their respective advantages for modeling, simulation, and decision-making support: a set of alternative Petri nets and a compound Petri net. These formalisms, as well as the transformation algorithms between them, are detailed and an illustrative example is provided. Among the main advantages of these formalisms, their intuitive application for modeling discrete event systems in the process of being designed, as well as the compactness that may present the resulting model, in the case of a compound Petri net, leading to efficient decision making, can be mentioned

Metamodel-based importance sampling for structural reliability analysis

Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance functions. In modern engineering such functions usually resort to running an expensive-to-evaluate computational model (e.g. a finite element model). In this respect simulation methods, which may require $10^{3-6}$ runs cannot be used directly. Surrogate models such as quadratic response surfaces, polynomial chaos expansions or kriging (which are built from a limited number of runs of the original model) are then introduced as a substitute of the original model to cope with the computational cost. In practice it is almost impossible to quantify the error made by this substitution though. In this paper we propose to use a kriging surrogate of the performance function as a means to build a quasi-optimal importance sampling density. The probability of failure is eventually obtained as the product of an augmented probability computed by substituting the meta-model for the original performance function and a correction term which ensures that there is no bias in the estimation even if the meta-model is not fully accurate. The approach is applied to analytical and finite element reliability problems and proves efficient up to 100 random variables.Comment: 20 pages, 7 figures, 2 tables. Preprint submitted to Probabilistic Engineering Mechanic

Strategies for multiobjective genetic algorithm development: Application to optimal batch plant design in process systems engineering

This work deals with multiobjective optimization problems using Genetic Algorithms (GA). A MultiObjective GA (MOGA) is proposed to solve multiobjective problems combining both continuous and discrete variables. This kind of problem is commonly found in chemical engineering since process design and operability involve structural and decisional choices as well as the determination of operating conditions. In this paper, a design of a basic MOGA which copes successfully with a range of typical chemical engineering optimization problems is considered and the key points of its architecture described in detail. Several performance tests are presented, based on the influence of bit ranging encoding in a chromosome. Four mathematical functions were used as a test bench. The MOGA was able to find the optimal solution for each objective function, as well as an important number of Pareto optimal solutions. Then, the results of two multiobjective case studies in batch plant design and retrofit were presented, showing the flexibility and adaptability of the MOGA to deal with various engineering problems

Optimization-Based Architecture for Managing Complex Integrated Product Development Projects

By the mid-1990\u27s, the importance of early introduction of new products to both market share and profitability became fully understood. Thus, reducing product time-to-market became an essential requirement for continuous competition. Integrated Product Development (IPD) is a holistic approach that helps to overcome problems that arise in a complex product development project. IPD emphasis is to provide a framework for an effective planning and managing of engineering projects. Coupled with the fact that about 70% of the life cycle cost of a product is committed at early design phases, the motivation for developing and implementing more effective methodologies for managing the design process of IPD projects became very strong. The main objective of this dissertation is to develop an optimization-based architecture that helps guiding the project manager efforts for managing the design process of complex integrated product development projects. The proposed architecture consists of three major phases: system decomposition, process re-engineering, and project scheduling and time-cost trade-off analysis. The presented research contributes to five areas of research: (1) Improving system performance through efficient re-engineering of its structure. The Dependency Structure Matrix (DSM) provides an effective tool for system structure understanding. An optimization algorithm called Simulated Annealing (SA) was implemented to find an optimal activity sequence of the DSM representing a design project. (2) A simulation-based optimization framework that integrates simulated annealing with a commercial risk analysis software called Crystal Ball was developed to optimally re-sequence the DSM activities given stochastic activity data. (3) Since SA was originally developed to handle deterministic objective functions, a modified SA algorithm able to handle stochastic objective functions was presented. (4) A methodology for the conversion of the optimally sequenced DSM into an equivalent DSM, and then into a project schedule was proposed. (5) Finally, a new hybrid time-cost trade-off model based on the trade-off of resources for project networks was presented. These areas of research were further implemented through a developed excel add-in called “optDSM”. The tool was developed by the author using Visual Basic for Application (VBA) programming language