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The particular simulator and application addressed here is the optimization of fuel economy in hybrid electric vehicles (HEVs). Accurately estimating the energy consumption of hybrid electric vehicles is complicated by the fact that these vehicles have multiple power sources and complex control strategies. As a starting point of this research, to ensure that available vehicle simulators can be validated, a thorough literature review of energy consumption in HEVs was done both on a component and an overall level. This then allowed model validation to be performed. New methods of model validation for the case of vehicle simulators were also developed and are discussed in this dissertation. Also in this document, the optimization framework developed to robustly minimize fuel economy in a hybrid electric vehicle simulator is discussed. Since the vehicle simulator is a hybrid system using LUTs, the methodology developed here will be applicable in many simulation optimization environments.
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
This thesis concerns the development and analysis of derivative-free optimization algorithms for simulation-based functions that are computationally expensive to evaluate. The first contribution is the introduction of data profiles as a tool for analyzing the performance of derivative-free optimization solvers when constrained by a computational budget. Using these profiles, together with a convergence test that measures the decrease in function value, we find that on three different sets of test problems, a model-based solver performs better than the two direct search solvers tested. The next contribution is a new model-based derivative-free algorithm, ORBIT, for unconstrained local optimization. A trust-region framework using interpolating Radial Basis Function (RBF) models is employed. RBF models allow ORBIT to interpolate nonlinear functions using fewer function evaluations than many of the polynomial models considered by present techniques. We provide an analysis of the approximation guarantees obtained by interpolating the function at a set of sufficiently affinely independent points. We detail necessary and sufficient conditions that an RBF model must obey to fit within our framework and prove that this framework allows for convergence to first-order critical points. We present numerical results on test problems as well as three application problems from environmental engineering to support ORBIT's effectiveness when relatively few func- tion evaluations are available. The framework used by ORBIT is also extended to include other models, in particular undetermined interpolating quadratics. These quadratics are flexible in their ability to interpolate at dynamic numbers of previously evaluated points. The third contribution is a new multistart global optimization algorithm, GORBIT, that takes advantage of the expensive function evaluations done in the course of both the global exploration and local refinement phases. We modify ORBIT to handle both bound constraints and external functional evaluations and use it as the local solver. For the global exploration phase, a new procedure for making maximum use of the information from previous evaluations, MIPE, is introduced. Numerical tests motivating our approach are presented and we illustrate using GORBIT on the problem of finding error-prone systems for Gaussian elimination.
Supply chain management (SCM) has been recognized as one of the key issues in the process industry. The growing size of the distributed supply chain structures, market dynamics and variability involved in the internal operations pose a challenge to efficiently managing the whole network. Globalization of supply chains and advances in information technology have led to a greater need for integrated operations as they have caused a more distributed network with potentially larger number of customers. It is essential that the various bodies constituting the supply chain operate in an integrated manner and their activities are synchronized towards a common goal. Thus, there is a need for efficient integration of information and decision making among the various functions of the supply chains. The growing need for integrated information and decision-making necessitates the development of a framework which allows the different entities of a supply chain to have access to a common information system as well as provides them with advanced decision-making tools. With the advancements in information technology, it is possible for supply chain members to share information and several such tools are also commercially available. However there is a need to combine intelligent decision making with information sharing to develop the required framework. The main objective of this dissertation is the development of novel methodologies that will facilitate intelligent decision-making and their application in the analysis of supply chains for chemical industries. Simulation models are used to depict supply chain dynamics so that they represent the decision-making by various entities. In order to obtain improved decision-making, a hybrid simulation based optimization framework is proposed. The framework considers the decision rules followed by the different entities and guides the simulation model towards improved solutions. The benefits of these methodologies include a more realistic representation of supply chain dynamics and reduced computational times for large-scale problems. The framework is applied to a number of case studies. Uncertainty in supply chain is also considered and the framework is used to determine the flexibility of the supply chain and manage risk under uncertainty. A derivative free optimization method is also proposed which has been applied to optimize the performance of a multi-enterprise supply chain network.
This volume “Cell Engineerring 11 - Biopharmaceutical Manufacturing: Progress, Trends and Challenges” is a source of the latest innovative research and technical development in biomanufacturing systems. It is organised into 2 parts: 1) Manufacturing of recombinant therapeutic proteins (e.g. therapeutic antibodies, biosimilars/biogenerics) and 2) Manufacturing aspects of cell and gene therapy. Each with selected chapters on the following topics for both up- and downstream, such as: Advanced process strategies, especially continuous manufacturing, Advanced culture techniques, especially single-use systems, Process transfer, scale-up/scale-down models, Processing advances/Manufacturing productivity/efficiency, Model-assisted process understanding and development/Digital Twins, Process controls and analytics, Quality control, Quality by design, Facility design and full-scale commercial systems, manufacturing technology innovation. The book comprises contributions of experts from academia and industry active in the field of cell culture development for the production of recombinant proteins, cell therapy and gene therapy, with consideration of Digital Twin ́s and facility design. The knowledge and expertise of the authors cover disciplines like cell biology, engineering, biotechnology and biomedical sciences. Inevitably, some omissions will occur in the test, but the authors have sought to avoid duplications by extensive cross-referencing to chapters in other volumes of this series and elsewhere. We hope the volume provides a useful compendium of techniques for scientists in industrial and research laboratories active in this field.
After providing an in-depth introduction to derivative-free global optimization with various constraints, this book presents new original results from well-known experts on the subject. A primary focus of this book is the well-known class of deterministic DIRECT (DIviding RECTangle)-type algorithms. This book describes a new set of algorithms derived from newly developed partitioning, sampling, and selection approaches in the box- and generally-constrained global optimization, including extensions to multi-objective optimization. DIRECT-type optimization algorithms are discussed in terms of fundamental principles, potential, and boundaries of their applicability. The algorithms are analyzed from various perspectives to offer insight into their main features. This explains how and why they are effective at solving optimization problems. As part of this book, the authors also present several techniques for accelerating the DIRECT-type algorithms through parallelization and implementing efficient data structures by revealing the pros and cons of the design challenges involved. A collection of DIRECT-type algorithms described and analyzed in this book is available in DIRECTGO, a MATLAB toolbox on GitHub. Lastly, the authors demonstrate the performance of the algorithms for solving a wide range of global optimization problems with various constraints ranging from a few to hundreds of variables. Additionally, well-known practical problems from the literature are used to demonstrate the effectiveness of the developed algorithms. It is evident from these numerical results that the newly developed approaches are capable of solving problems with a wide variety of structures and complexity levels. Since implementations of the algorithms are publicly available, this monograph is full of examples showing how to use them and how to choose the most efficient ones, depending on the nature of the problem being solved. Therefore, many specialists, students, researchers, engineers, economists, computer scientists, operations researchers, and others will find this book interesting and helpful.
Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.
The 31st European Symposium on Computer Aided Process Engineering: ESCAPE-31, Volume 50 contains the papers presented at the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event held in Istanbul, Turkey. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students and consultants in the chemical industries. - Presents findings and discussions from the 31st European Symposium of Computer Aided Process Engineering (ESCAPE) event