Simulation Optimization: A Review on Theory and Applications

Wang, Long-Fei; Shi, Leyuan

doi:10.1016/s1874-1029(13)60081-6

Cited by 11 publications

(4 citation statements)

References 130 publications

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Section: Des Modelmentioning

confidence: 99%

“…For a given simulation, if the number of rescue teams that cover a given demand point i for a certain priority p and period t is h, then the DES model will provide an estimate of T Worked jpt for the h rescue teams and thus will allow to compute q ihpt by using Equation (9). However, the DES model will give no estimates of T Worked jpt for k rescue teams if k6 ¼h to compute q ikpt by using Equation (9). Thus, we replaced Equation ( 9) by the average of the existing busy fraction estimates for demand nodes that are covered by exactly k rescue teams within S p during period t, which can be formulated as follows:…”

Section: Des Modelmentioning

confidence: 99%

“…6 Many researchers agree that DSBO outperforms analytic models for investigating complex stochastic systems. 7 In the literature, many review papers describe the theory and applications of DSBO, [8][9][10] According to Wang and Shi 9 and Chen et al, 11 a DSBO problem can be formulated as follows:…”

Section: Introductionmentioning

confidence: 99%

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A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services

2022

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Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest.

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Section: Des Modelmentioning

confidence: 99%

Section: Des Modelmentioning

confidence: 99%

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A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services

2022

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“…These simulation runs will be undertaken for the parameter values chosen from the sampling method utilised, to obtain the most information about the constrained design space. Using the outputs from the selected simulation runs, a "meta-model" can be constructed to describe the relationship between x n and f (x n ) (Wang and Shi 2013). Therefore, reducing the CPU cost associated with running multiple simulations, as only the n sample points are required.…”

Section: The Optimisation Processmentioning

confidence: 99%

The use of CFD and multi-objective optimisation techniques to customise an industrial pre-mixer

Thompson

Walton

Hassan

et al. 2017

Struct Multidisc Optim

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Considering the use of commercial-off-the-shelf (COTS) devices in industry, a case study has been undertaken implementing the goal driven optimisation of a venturi-type pre-mixer. This COTS device is used to supply the correct ratio of a specific fuel and oxidiser to a pre-mix burner. Providing the correct quantity of excess air results in a reduction o f the NO x emissions; and this, in addition to the requirement for complete mixing of the constituents has been used to define the multi-objective optimisation of the device. A Computational Fluid Dynamics (CFD) simulation has been carried out using Fluent v14.5; verified by experimental data. To find an optimised design of the pre-mixer, four geometrical dimensions of its current design have been considered. An assessment of varying sampling and interpolation techniques has been made, in order to establish the most appropriate for use with this specific problem. The evolutionary-based NSGA-II and deterministic Nelder Mead optimisation algorithms have been used and their results compared to verify the optimisation outcome. Based on these findings suggested customisation of the COTS pre-mixer's design could result in improvements in mixing performance, in the region of 30 %.

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