This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

This book describes the current state of the art for simulating paint shop applications, their advantages and limitations, as well as corresponding high-performance computing (HPC) methods utilized in this domain. The authors provide a comprehensive introduction to fluid simulations, corresponding optimization methods from the HPC domain, as well as industrial paint shop applications. They showcase how the complexity of these applications bring corresponding fluid simulation methods to their limits and how these shortcomings can be overcome by employing HPC methods. To that end, this book covers various optimization techniques for three individual fluid simulation techniques, namely grid-based methods, volumetric decomposition methods, and particle-based methods.

Bridges constitute a crucial component of urban highways due to the complexity and uncertain nature of their construction process. Simulation is an alternative method of analyzing and planning the construction processes, especially the ones with repetitive and cyclic nature, and it helps managers to make appropriate decisions. Furthermore, there is an inverse relationship between the cost and time of a project and finding a proper trade-off between these two key elements using optimization methods is important. Thus, the integration of simulation models with optimization techniques leads to an advancement in the decision making process. In addition, the large number of resources required in complex and large scale bridge construction projects results in a very large search space. Therefore, there is a need for using parallel computing in order to reduce the computational time of the simulation-based optimization. Most of the construction simulation tools need an integration platform to be combined with optimization techniques. Also, these simulation tools are not usually compatible with Linux environment which is used in most of the massive parallel computing systems or clusters. In this research, an integrated simulation-based optimization framework is proposed within one platform to alleviate those limitations. A master-slave (or global) parallel Genetic Algorithm (GA) is used as a parallel computing technique to decrease the computation time and to efficiently use the full capacity of the computer. In addition, sensitivity analysis is applied to identify the promising configuration for GA and analyzing the impact of GA parameters on the overall performance of the specific simulation-based optimization problem used in this research. Finally, a case study is implemented and tested on a server machine as well as a cluster to explore the feasibility of the proposed approach. The results of this research showed better performance of the proposed framework in comparison with other GA optimization techniques from the points of view of the quality of the optimum solutions and the computation time. Also, acceptable improvements in the computation time were achieved for both deterministic and probabilistic simulation models using master-salve parallel paradigm (8.32 and 20.3 times speedups were achieved using 12 cores, respectively). Moreover, performing the proposed framework on multiple nodes using a cluster system led to 31% saving on the computation time on average. Furthermore, the GA was tuned using sensitivity analyses which resulted in the best parameters (500 generations, population size of 200 and 0.7 as the crossover probability).

Building on the author’s earlier Applied Simulation and Optimization, this book presents novel methods for solving problems in industry, based on hybrid simulation-optimization approaches that combine the advantages of both paradigms. The book serves as a comprehensive guide to tackling scheduling, routing problems, resource allocations and other issues in industrial environments, the service industry, production processes, or supply chains and aviation. Logistics, manufacturing and operational problems can either be modelled using optimization techniques or approaches based on simulation methodologies. Optimization techniques have the advantage of performing efficiently when the problems are properly defined, but they are often developed through rigid representations that do not include or accurately represent the stochasticity inherent in real systems. Furthermore, important information is lost during the abstraction process to fit each problem into the optimization technique. On the other hand, simulation approaches possess high description levels, but the optimization is generally performed through sampling of all the possible configurations of the system. The methods explored in this book are of use to researchers and practising engineers in fields ranging from supply chains to the aviation industry.

After v. 1, each volume's t.p. names a different panel at the beginning of its author statement.

This SpringerBrief bridges the gap between the areas of simulation studies on the one hand, and optimization with natural computing on the other. Since natural computing methods have been applied with great success in several application areas, a review concerning potential benefits and pitfalls for simulation studies is merited. The brief presents such an overview and combines it with an introduction to natural computing and selected major approaches, as well as with a concise treatment of general simulation-based optimization. As such, it is the first review which covers both the methodological background and recent application cases. The brief is intended to serve two purposes: First, it can be used to gain more information concerning natural computing, its major dialects, and their usage for simulation studies. It also covers the areas of multi-objective optimization and neuroevolution. While the latter is only seldom mentioned in connection with simulation studies, it is a powerful potential technique. Second, the reader is provided with an overview of several areas of simulation-based optimization which range from logistic problems to engineering tasks. Additionally, the brief focuses on the usage of surrogate and meta-models. The brief presents recent application examples.