An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Abstract

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization.

Embedding ensemble learning into simulation-based optimisation: a learning-based optimisation approach for construction planning

“…These results are consist with what has been presented in previous research, i.e. a learning model can outperform a SO method in terms of optimisation quality when the learning model and optimisation algorithm are well defined (Jun et al, 2010).…”

“…Similar examples have already been applied, e.g. supply chain management (Jun et al. , 2010) and engineering design (Nakayama et al.…”

“…Similar examples have already been applied, e.g. supply chain management (Jun et al, 2010) and engineering design (Nakayama et al, 2002), to reduce computational loads. However, the difference from previous research is that the interested parameters in construction contexts are both massive and changeable according to dynamic construction situations.…”

“…, 2017). However, SO may not be an ideal optimisation approach in this situation because it is unable to provide a real-time response due to its high computational loads (Jun et al. , 2010; Nguyen et al.…”

“…From a statistical perspective, machine learning is the technology that is able to learn from data and make accurate predictions (Yan and Wang, 2014). The significant advantages are the real-time and accurate learning abilities, the features of which may help overcome above shortcomings of SO methods (Jun et al. , 2010).…”