740 research outputs found
A budget allocation strategy minimizing the sample set quantile for initial experimental design
No full textThe increased complexity of manufacturing systems makes the acquisition of the system performance estimate a black-box procedure (e.g., simulation tools). The efficiency of most black-box optimization algorithms is affected significantly by initial designs (populations). In most population initializers, points are spread out to explore the entire domain, e.g., space-filling designs. Some population initializers also consider exploitation procedures to speed up the optimization process. However, they are either application-dependent or require an additional budget. This article proposes a generic method to generate, without an additional budget, several good solutions in the initial design. The aim of the method is to optimize the quantile of the objective function values in the generated sample set. The proposed method is based on a clustering of the solution space; feasible solutions are clustered into groups and the budget is allocated to each group dynamically based on the observed information. The asymptotic performance of the proposed method is analyzed theoretically. The numerical results show that, if proper clustering rules are applied, an unbalanced design is generated in which promising solutions have higher sampling probabilities than non-promising solutions. The numerical results also show that the method is robust to wrong clustering rules
Combining simulation experiments and analytical models with area-based accuracy for performance evaluation of manufacturing systems
No full textSimulation is considered as one of the most practical tools to estimate manufacturing system performance, but it is slow in its execution. Analytical models are generally available to provide fast, but biased, estimates of the system performance. These two approaches are commonly used distinctly in a sequential approach, or one as alternative to the other, for assessing manufacturing system performance. This article proposes a method to combine simulation experiments with analytical results in a single performance evaluation model. The method is based on kernel regression and allows considering more than one analytical methods. A high-fidelity model is combined with low-fidelity models for manufacturing system performance evaluation. Multiple area-based low-fidelity models can be considered for the prediction. The numerical results show that the proposed method is able to identify the reliability of low-fidelity models in different areas and provide estimates with higher accuracy. Comparison with alternative approaches shows that the method is more accurate in a studied manufacturing application
Nested partitioning algorithm for the optimization of control parameters in energy efficient production lines
No full textEnergy efficient control policies that switch off/on the machine using buffer occupancy information have been recently proposed in literature, considering that machines may need a transitory before resuming the service. However, how a simultaneous control of the machines may affect system performance due to the propagation of blocking and starvation effects is not trivial. The optimization of control policies parameters requires system performance evaluation through simulation, but it is highly time consuming as system complexity increases. This work aims to design an efficient Nested Partitioning algorithm for the optimization of switching control policies in production lines based on the structural properties that the optimal control might have at system level. The performance of the algorithm will be evaluated using discrete event simulation and compared with a commercial optimization tool (OptQuest). The effect of the control on the system throughput will also be reported
Analysis of the near flatness phenomenon for multi-loop closed manufacturing systems
No full textWith the recent development of the electrical vehicle (EV) industry, the study of manufacturing systems producing key components in this sector is becoming increasingly important. Multi-loop closed manufacturing systems (MCMS), whose operation and control are rarely studied in the literature, are widely used in the EV industry. This work provides innovative guidelines for MCMS operation also valid in a general context and not necessarily limited to the EV field. The main focus is on a specific topic of MCMS operation, namely the near flatness phenomenon. The near flatness indicates how system throughput is influenced by its population, i.e. the number of items circulating in the MCMS, especially in high-throughput conditions. The study of near flatness aims at enhancing MCMS flexibility in terms of population control and handling while guaranteeing high system throughput. In this work, a new indicator quantitatively describing the near flatness is provided. Numerical studies are conducted to analyze the effect of machine efficiency in isolation, mean times to repair and buffer capacities on the near flatness. Experiments are also carried out on a real case of MCMS in the EV field. Based on the experimental results, practical specifications to improve MCMS design and management are provided
Extended kernel regression: A multi-resolution method to combine simulation experiments with analytical methods
No full textSimulation is widely used to predict the performance of complex systems. The main drawback of simulation is that it is slow in execution and the related compute experiments can be very expensive. On the other hand, analytical methods are used to rapidly provide performance estimates, but they are often approximate because of their restrictive assumptions. Recently, Extended Kernel Regression (EKR) has been proposed to combine simulation with analytical methods for reducing the computational effort. This paper has different purposes. Firstly, EKR is tested on different cases and compared with other techniques. Secondly, two different methods for calculation of confidence band are proposed. Numerical results show that the EKR method provides accurate predictions, particularly when the computational effort is low. Results also show that the performance of the two confidence band methods depends on the case analyzed. Thus, further studies are necessary to develop a robust method for confidence band calculation
Multi-fidelity Models for Decomposed Simulation Optimization Problems
No full textHierarchical problem decomposition methods are widely used in optimization
when the scale of the problem is large. The master problem is hierarchically
decomposed to several sub-problems and the detail level of the sub-problems
increases during the optimization from bottom to top. When simulation is used
to estimate unknown functions, models with different detail are used at each
level. However, the simulation outputs used to solve the sub-problems of a
hierarchy level are not used anymore at higher levels. An approach is proposed
in this paper to reuse these experiment data to improve the efficiency of the
simulation-optimization algorithm. A multi-fidelity surrogate model is built in
each sub-problem to guide the search of the optimum. The performance of the
approach is numerically assessed with the goal of understanding its potentialities
and the effect of algorithm parameters over optimization results
Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems
No full textThe buffer allocation problem (BAP) for flow lines has been extensively addressed in the literature. In the framework of iterative approaches, algorithms alternate an evaluative method and a generative method. Since an accurate estimation of system performance typically requires high computational effort, an efficient generative method reducing the number of iterations is desirable, for searching for the optimal buffer configuration in a reasonable time. In this work, an iterative optimization algorithm is proposed in which a highly accurate simulation is used as the evaluative method and a surrogate-based optimization is used as the generative method. The surrogate model of the system performance is built to select promising solutions so that an expensive simulation budget is avoided. The performance of the surrogate model is improved with the help of fast but rough estimators obtained with approximated analytical methods. The algorithm is embedded in a problem decomposition framework: several problem portions are solved hierarchically to reduce the solution space and to ease the search of the optimum solution. Further, the paper investigates a jumping strategy for practical application of the approach so that the algorithm response time is reduced. Numerical results are based on balanced and unbalanced flow lines composed of single-machine stations
A new partition-based random search method for deterministic optimization problems
No full textThe Nested Partition (NP) method is efficient in large-scale optimization problems. The most promising region is identified and partitioned iteratively. To guarantee the global convergence, a backtracking mechanism is introduced. Nevertheless, if inappropriate partitioning rules are used, lots of backtracking occur reducing largely the algorithm efficiency. A new partition-based random search method is developed in this paper. In the proposed method, all generated regions are stored for further partitioning and each region has a partition speed related to its posterior probability of being the most promising region. Promising regions have higher partition speeds while non-promising regions are partitioned slowly. The numerical results show that the proposed method finds the global optimum faster than the pure NP method if numerous high-quality local optima exist. It can also find all the identical global optima, if exist, in the studied case
Molecular dynamics simulation of graphene sinking during chemical vapor deposition growth on semi-molten Cu substrate
No full text© 2020, The Author(s).Copper foil is the most promising catalyst for the synthesis of large-area, high-quality monolayer graphene. Experimentally, it has been found that the Cu substrate is semi-molten at graphene growth temperatures. In this study, based on a self-developed C–Cu empirical potential and density functional theory (DFT) methods, we performed systematic molecular dynamics simulations to explore the stability of graphene nanostructures, i.e., carbon nanoclusters and graphene nanoribbons, on semi-molten Cu substrates. Many atomic details observed in the classical MD simulations agree well with those seen in DFT-MD simulations, confirming the high accuracy of the C–Cu potential. Depending on the size of the graphene island, two different sunken-modes are observed: (i) graphene island sinks into the first layer of the metal substrate and (ii) many metal atoms surround the graphene island. Further study reveals that the sinking graphene leads to the unidirectional alignment and seamless stitching of the graphene islands, which explains the growth of large single-crystal graphene on Cu foil. This study deepens our physical insights into the CVD growth of graphene on semi-molten Cu substrate with multiple experimental mysteries well explained and provides theoretic references for the controlled synthesis of large-area single-crystalline monolayer graphen
A new teaching approach exploiting lab-scale models of manufacturing systems for simulation classes
No full textTeaching in higher education is often challenging for the lack of practical implementation and difficulties in student involvement. In engineering classes, students are often deeply involved in computer laboratories and projects in which they are challenged with decision-making problems. The lack of the real system that is being modelled may hinder the effectiveness of the teaching activities. In this paper, we propose a new teaching approach based on the student’s interaction with lab-scale models of manufacturing systems. Students have the possibility to make observations, collect data, and implement improvements to a system, all within a course duration. The flexibility of the proposed approach enables its application to a wide range of courses, for instance manufacturing system engineering, production management, Industry 4.0. As case study, we target a course on simulation of manufacturing systems for industrial and mechanical engineering, in which students are asked to build, validate, and use a discrete event simulation model of a production system. The application of this project methodology changed the way of teaching simulation in the course and significantly improved students’ evaluation and satisfaction
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