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Design Optimization
Published in Xiaolin Chen, Yijun Liu, Finite Element Modeling and Simulation with ANSYS Workbench, 2018
Optimization is an integral part of engineering design. Traditionally, optimization tasks have been carried out mostly by trial and error, when unexpected failure of a design to meet certain criteria is identified. Often, a best design is not obtained after many design iterations. Only a feasible design meeting all the requirements is created. The conventional way of changing a design when it is found to be deficient in certain criteria can be incredibly costly and time-consuming. For today's engineers, a more productive and cost-effective practice is to use numerical optimization techniques to guide in the evaluation of design trade-offs. Often, a best design is put forward after running simulation-based optimization a few times. In this chapter, we will cover materials relating to the topics of design optimization via simulation. The concepts of topology optimization, parametric optimization, and design space exploration will be introduced, along with optimization examples using ANSYS® Workbench.
Kanban Allocation Policies of Multi-product Production Control Strategies
Published in Khojasteh Yacob, Production Management, 2017
Oladipupo Olaitan, Paul Young, John Geraghty
One approach that is widely adopted is the simulation-based optimization (Koulouriotis et al. 2010), which, according to Bowden and Hall (1998), is “the practice of linking an optimization method with a simulation model to determine appropriate settings of certain input parameters so as to maximize the performance of the simulated system.” The system’s performance is usually expressed in the form of an objective function that would assign penalty costs or benefits to a statistical measure, which can be outputted directly from the simulation model.
A Framework for Simulation-Based Structure and Parameter Optimization of Discrete-Event Systems
Published in Gabriel A. Wainer, Pieter J. Mosterman, Discrete-Event Modeling and Simulation, 2018
Olaf Hagendorf, Thorsten Pawletta
The focus of this chapter is the description of a methodology for a simulation-based parameter and structure optimization for modular, hierarchical discrete-event systems. In contrast to current approaches that use modeling and simulation, here the model structure is variable and thus it is open to optimization. The variation of model structure and model parameters is controlled by a super-ordinate optimization module. The introduced simulation-based optimization framework consists of three main elements: (i) model management, (ii) modeling and simulation, and (iii) optimization. As a basis for the model management method the System Entity Structure/Model Base (SES/MB) approach, introduced by Rozenblit, Zeigler et al. [20,27,28] is employed. The SES/MB approach is a generative, knowledge base framework consisting of a tree-like SES and a MB containing basic components. It supports the definition of a set of modular, hierarchical models and the generation of specific model structures using predefined basic components from a MB. Because of this characteristic a modular, hierarchical modeling and simulation method has to be employed.The modeling and simulation approach based on the Discrete Event System Specification (DEVS) formalism introduced by Zeigler [26,27] is an established method in the field of modular, hierarchical modeling and simulation. Dynamic Structure DEVS (DSDEVS) as an extension of DEVS offers methods to allow structural changes during a simulation run [6,15,25,27]. In countless applications, for example in Hagendorf and colleagues [8,9], the advantages of a dynamic structure modeling and simulation method are considerable. A DSDEVS method based on work in [9,15,16] is integrated in the novel simulation-based optimization approach. However, detailed aspects of DSDEVS systems are not considered in this chapter.The optimization method controls the variation of model parameters and structure. Genetic algorithms have delivered robust solutions for various simulation-based optimization problems, for example in [17,18,24]. The genetic algorithm documented in [24] will be employed as an optimization method in the framework.
A Simulation Based Meta-heuristic Approach for the Inbound Container Housekeeping Problem in the Automated Container Terminals
Published in Maritime Policy & Management, 2023
Hu Qin, Xinxin Su, Guoxin Li, Xin Jin, Mingzhu Yu
The simulation-based optimization (SO) is mainly applied to the case where the objective function, constraints or model parameters cannot be expressed by explicit functional relationships or values as in traditional optimization problems. The evaluation of the optimization object can only be achieved by the statistical indicators obtained by the simulation. For complex random optimization problems, simulation-based optimization is an ideal choice. In recent years, simulation-based optimization methods are applied in complex engineering systems, supply chain and logistics systems, manufacturing systems and socio-economic systems at home and abroad (Clarke, McLay, and McLeskey Jr. 2014; Dragović, Škurić, and Kofjač 2014; Ferrara et al. 2014; Nguyen, Reiter, and Rigo 2014; Sahay and Ierapetritou 2014; Wang et al. 2017, 2017; Yu et al. 2017; Cao and Lam 2019). In theory, Gosavi (2015) introduced the simulation-based optimization algorithm in detail from the aspects of origin, goal, limitation, simulation basis, applicable problem and specific algorithm. This book details the functions of computer simulation in simulation-based optimization methods.
Efficient hybrid Bayesian optimization algorithm with adaptive expected improvement acquisition function
Published in Engineering Optimization, 2021
Zhaoyi Xu, Yanjie Guo, Joseph H. Saleh
Once an engineering system has been mathematically modelled, computational simulation can provide insights into its performance under different operating conditions. Computational simulation enables the analysis of real-world engineering design problems without recourse to an analytical, closed-form solution to system performance. This mapping from the system’s input to the performance output is best described as a black box function, which is known only at the simulation points and is often computationally expensive and noisy to evaluate. Traditionally, the method known as parametric design (Woodbury 2010 ), which varies parts of the input design features while holding others constant, has been used in conjunction with computational simulation to identify the setting of design parameters that lead to improvement in system performance. This method, however, can only partially improve system performance and often misses important latent correlations between different design variables. With the development of computational resources, iterative simulation-based optimizations, generally subsumed under the heading of numerical optimization (Carson and Maria 1997; Gosavi 2015), are used to optimize the performance of engineering systems. Simulation-based optimization is widely used in aerospace engineering, for example in configuration design (Chen et al. 2016) and in combustion system design (Channappagoudra et al. 2013). Based on the application type, simulation-based optimization is categorized into two classes, discrete and continuous optimization. This work focuses primarily on continuous optimization.
Integrating operational planning decisions throughout the forest products industry supply chain under supply and demand uncertainty
Published in International Journal of Forest Engineering, 2018
Shashi Shahi, Reino Pulkki, Mathew Leitch, Christopher Gaston
Simulation-based optimization is the process of finding the best values of the decision variables for a system where the performance is evaluated based on the output of a simulation model of the system. The model treats the objective function evaluator as a black box that uses metaheuristic searches to find optimal values for decision variables without the knowledge of how the objective function value is calculated. The simulation model returns the objective function value corresponding to a set of decision variables, suggested by the user. An optimization routine, OptQuest uses the outputs from the simulation model and generates new values of decision variables based on scatter search strategy that uses present and past evaluations (Olafsson 2006). For each new set of values for the decision variables, a simulation is again performed and the process repeats. The system allows for linear constraints to be imposed on the newly created decision variables, before the objective function value is calculated. The optimization procedure is designed to carry out a special non-monotonic search, where the successively generated inputs produce varying evaluations, not all of them improving, but which over time provide a highly efficient trajectory to the best solutions. The search process continues until the metaheuristic algorithm reaches some termination criteria, in this case a maximum number of simulations (1000 iteration counts). For each simulation, 10 replications are run. It was noticed that 1000 iterations provide a stable solution. The model was built in Oracle® software (Reference). Optimization was performed using Intel® Core™ i7 CPU with 8 GB RAM. The overall flow chart of the simulation-based optimization process in the integrated supply chain model is shown in Figure 2.