Simulation-based Optimization Process Research Articles

Investigating the performance of genetic algorithm and particle swarm for optimizing daylighting and energy performance of offices in Alexandria, Egypt

PurposeThis study aims to compare the performance of two nature-inspired metaheuristics inside Grasshopper in optimizing daylighting and energy performance against brute force in terms of the resemblance to ideal solution and calculation time.Design/methodology/approachThe simulation-based optimization process was controlled using two population-based metaheuristic algorithms, namely, the genetic algorithm (GA) and particle swarm optimization (PSO). The objectives of the optimization routine were optimizing daylighting and energy consumption of a standard reference office while varying the urban context configuration in Alexandria, Egypt.FindingsThe results from the GA and PSO were compared to those from brute force. The GA and PSO demonstrated much faster performance to converge to design solution after conducting only 25 and 43% of the required simulation runs, respectively. Also, the average proportion of the resulted weighted sum optimization (WSO) per case using the GA and PSO to that from brute force algorithm was 85 and 95%, respectively.Originality/valueThe work of this paper goes beyond the current practices for showing that the performance of the optimization algorithm can differ by changing the urban context configuration while solving the same problem under the same design variables and objectives.

Blank Shape Optimization Considering 3D Space Targets for External and Internal Boundaries in Sheet Metal Forming Processes

Optimum design of the shape of the initial blank is an important task before sheet metal forming processes. It reduces production costs, material waste and improves the quality of the product significantly. In the present work, the blank shape optimization problem is considered based on the 3D space target contours and moreover the sheet metal can have an internal boundary. In other words, the shape optimization process can be applied on the internal boundary, external boundary or both. To the best knowledge of the authors, there are no published works in this subject. Using the iterative simulation-based optimization process, a special shape error index and also updating algorithm were proposed to modify the blank geometry in each iteration until capturing the optimum shape. The sheet forming process is highly nonlinear in nature due to plastic behaviors, large deformations and frictional contact surfaces. Therefore, the updating formula should be robust enough to have less sensitivity with respect to the initial guess. To evaluate the proposed updating formula and its robustness, some numerical examples were considered and the effects of different tools geometries, 2D and 3D target contours, internal and external boundaries and different initial guesses were examined.

Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals

At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics—Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search—guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable.

Online Knowledge Extraction and Preference Guided Multi-Objective Optimization in Manufacturing

The integration of simulation-based optimization and data mining is an emerging approach to support decision-making in the design and improvement of manufacturing systems. In such an approach, knowledge extracted from the optimal solutions generated by the simulation-based optimization process can provide important information to decision makers, such as the importance of the decision variables and their influence on the design objectives, which cannot easily be obtained by other means. However, can the extracted knowledge be directly used during the optimization process to further enhance the quality of the solutions? This paper proposes such an online knowledge extraction approach that is used together with a preference-guided multi-objective optimization algorithm on simulation models of manufacturing systems. Specifically, it introduces a combination of the multi-objective evolutionary optimization algorithm, NSGA-II, and a customized data mining algorithm, called Flexible Pattern Mining (FPM), which can extract knowledge in the form of rules in an online and automatic manner, in order to guide the optimization to converge towards a decision maker’s preferred region in the objective space. Through a set of application problems, this paper demonstrates how the proposed FPM-NSGA-II can be used to support higher quality decision-making in manufacturing.

Managing geological uncertainty in expensive reservoir simulation optimization

A method to manage geological uncertainty as part of an expensive simulation-based optimization process is presented. When the number of realizations representing the uncertainty is high, the computational cost to optimize the system can be considerable, and often prohibitively, as each forward evaluation is expensive to evaluate. To overcome this limitation, an iterative procedure is developed that selects a subset of realizations, based on a binary nonlinear optimization subproblem, to match the statistical properties of the target function at known sample points. This results in a reduced-order model that is optimized in place of the full system at a much lower computational cost. The result is validated over the ensemble of all realizations giving rise to one new sample point per iteration. The process repeats until the stipulated stopping conditions are met. Demonstration of the proposed method on a publicly available realistic reservoir model with 50 realizations shows that comparable results to full optimization can be obtained but far more efficiently.

Multi-objective optimization of water injection in spark-ignition engines using the stochastic reactor model with tabulated chemistry

Water injection is investigated for turbocharged spark-ignition engines to reduce knock probability and enable higher engine efficiency. The novel approach of this work is the development of a simulation-based optimization process combining the advantages of detailed chemistry, the stochastic reactor model and genetic optimization to assess water injection. The fast running quasi-dimensional stochastic reactor model with tabulated chemistry accounts for water effects on laminar flame speed and combustion chemistry. The stochastic reactor model is coupled with the Non-dominated Sorting Genetic Algorithm to find an optimum set of operating conditions for high engine efficiency. Subsequently, the feasibility of the simulation-based optimization process is tested for a three-dimensional computational fluid dynamic numerical test case. The newly proposed optimization method predicts a trade-off between fuel efficiency and low knock probability, which highlights the present target conflict for spark-ignition engine development. Overall, the optimization shows that water injection is beneficial to decrease fuel consumption and knock probability at the same time. The application of the fast running quasi-dimensional stochastic reactor model allows to run large optimization problems with low computational costs. The incorporation with the Non-dominated Sorting Genetic Algorithm shows a well-performing multi-objective optimization and an optimized set of engine operating parameters with water injection and high compression ratio is found.

Automated optimization for the integrated design process: the energy, thermal and visual comfort nexus

Nowadays, the design of energy efficient and comfortable building results as the solution to a complex optimization that involves a great number of interdependent variables. In such a complex system that is characterized by many requirements, sometimes conflicting, the use of an integrated design approach can speed up the design process, allow evaluating and comparing a great number of design options and lead to optimized performance at multiple levels.We present the conception of a replicable methodology, including the integrated tools and metrics, for supporting the integrated design of the building envelope, aiming at minimizing the building total energy demand (resulting from the sum of heating, cooling and lighting energy needs) in a thermal and visual comfort context. The methodology relies on a simulation-based optimization process. For each of the two thousand of design solutions that are analyzed within the design space of the problem, the thermal and visual comfort conditions are evaluated at the same time. A “filter” approach is used for combining the different objectives.The methodology is applied to the design of a school classroom as a reference case study. Results are compared for four different building orientations and two different climatic conditions. The differences in design between the optimized building configurations in the different location and orientation emerge from the study, however resulting in similar energy performance (total primary energy demands in the range 78.6–82.8 kWh/m2y for Turin and 57.7–60.1 for Palermo). In all cases, energy-optimal solutions result as nearly optimal also from the comfort point of view, which demonstrate the validity of the implemented total energy approach.Beyond the numerical results, we provide an effective integrated methodology that is able to support designers in defining a unique energy and comfort optimized solution to be implemented in the real design analyzing the design space and determining the optimal set of passive solutions to minimize the total energy needs of a building in a context of good comfort conditions, going beyond the Pareto optimality concept and without dealing through complex multi-objective optimization processes.

Optimizing fin design for a PCM-based thermal storage device using dynamic Kriging

A key challenge in the development of a practical thermal storage device (TSD) is the low thermal conductivity of common phase change materials (PCM). This low conductivity impedes both heat input and extraction. The most common solution is to use conductive metal fins to spread heat through the device. However, optimizing the effectiveness of the container and the fin arrangement is difficult due to the large number of potential design parameters. This paper develops a strategy to make simulation-based optimization process affordable and accurate. First, numerical techniques are designed to accurately and efficiently compute heat and mass transport in a variety of geometries without generating grids to conform to each geometry. This facilitates rapid prototyping and mitigates the expense of individual simulations. Second, a pre-screening process identifies the independent variables with the largest and most nonlinear effect on the objective function in the optimization process, thus narrowing the parameter space. Finally, a dynamic Kriging-based optimization approach constructs a multidimensional response surface using sparse input datasets; the response surface is then used to identify an optimal design. The combination of the above three strategies is shown to result in an approach that can aid in the design of optimal thermal storage devices that rely on a mixture of PCM and metal fins.

Simulation-Based Early Prediction of Rocket, Artillery, and Mortar Trajectories and Real-Time Optimization for Counter-RAM Systems

The threat imposed by terrorist attacks is a major hazard for military installations, for example, in Iraq and Afghanistan. The large amounts of rockets, artillery projectiles, and mortar grenades (RAM) that are available pose serious threats to military forces. An important task for international research and development is to protect military installations and implement an accurate early warning system against RAM threats on conventional computer systems in out-of-area field camps. This work presents a method for determining the trajectory, caliber, and type of a projectile based on the estimation of the ballistic coefficient. A simulation-based optimization process is presented that enables iterative adjustment of predicted trajectories in real time. Analytical and numerical methods are used to reduce computing time for out-of-area missions and low-end computer systems. A GUI is programmed to present the results. It allows for comparison between predicted and actual trajectories. Finally, different aspects and restrictions for measuring the quality of the results are discussed.

A Simulation-based Approach for Sustainable Transportation Systems Evaluation and Optimization: Theory, Systematic Framework and Applications

Diverse strategies have been proposed and implemented to relieve increasingly serious urban transportation problems. How to evaluate and optimize combined strategies to increase sustainability and improve efficiency of multimodal transportation systems is still a great challenge. This paper proposes a simulation-based systematic framework for sustainable transportation systems evaluation and optimization. Simulation-based optimization (SBO) is incorporated to seek an optimal combination of transportation planning and operations strategies, e.g. congestion pricing for private cars, bus and rail fares, to minimize generalized costs of multimodal traveling in a heuristic way. Applications in a small network of Tianjin Eco-city with multimodal travelers demonstrate the feasibility and applicability of the SBO approach in sustainable transportation systems evaluation and optimization. The objective is to reduce multimodal travel costs subject to a minimum mode share of green transportation. The SBO process also validates that the efficiency of each strategy may be influenced by the strengths of other strategies.

Maximizing Design Confidence in Sequential Simulation-Based Optimization

Computational simulation models support a rapid design process. Given model approximation and operating conditions uncertainty, designers must have confidence that the designs obtained using simulations will perform as expected. The traditional approach to address this need consists of model validation efforts conducted predominantly prior to the optimization process. We argue that model validation is too daunting of a task to be conducted with meaningful success for design optimization problems associated with high-dimensional space and parameter spaces. In contrast, we propose a methodology for maximizing confidence in designs generated during the simulation-based optimization process. Specifically, we adopt a trust-region-like sequential optimization process and utilize a Bayesian hypothesis testing technique to quantify model confidence, which we maximize by calibrating the simulation model within local domains if and when necessary. This ensures that the design iterates generated during the sequential optimization process are associated with maximized confidence in the utilized simulation model. The proposed methodology is illustrated using a cantilever beam design subject to vibration.

A variable-size local domain approach for increased design confidence in simulation-based optimization

Simulation-based design optimization utilizes computational models that rely on assumptions and approximations. There is a need therefore, to ensure that the obtained designs will exhibit the desired behavior as anticipated given the model predictions. The common approach to accomplish that is to validate the utilized computational models prior to the design optimization process. However, this is practically an impossible task especially for design problems with high-dimensional design and parameter spaces. We have recently proposed a different approach for maximizing confidence in the designs generated during a sequential simulation-based optimization process based on calibrating the computational models when necessary and within local subdomains of the design space. In that work, the size of the local domains was held fixed and not linked to uncertainty, and the confidence in designs was quantified using Bayesian hypothesis testing. In this article, we present an improved methodology. Specifically, we use a statistical methodology to account for uncertainty and to determine the size of the local domains at each stage of the sequential design optimization process using parametric bootstrapping that involves maximum likelihood estimators of model parameters. The sequential process continues until the local domain does not change from stage to stage during the design optimization process, ensuring convergence to an optimal design. The proposed methodology is illustrated with the design of a thermal insulator using one-dimensional, linear heat conduction in a solid slab with heat flux boundary conditions.