Accepted author manuscript, 146 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Licence: None
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - A Two Stage Algorithm for Guiding Data Collection Towards Minimising Input Uncertainty
AU - Parmar, Drupad
AU - Morgan, Lucy
AU - Titman, Andrew
AU - Williams, Richard
AU - Sanchez, Susan
PY - 2021/3/22
Y1 - 2021/3/22
N2 - In stochastic simulation the input models used to drive the simulation are often estimated by collecting data from the real-world system. This can be an expensive and time consuming process so it would therefore be useful to have some guidance on how much data to collect for each input model. Estimating the input models via data introduces a source of variance in the simulation response known as input uncertainty. In this paper we propose a two stage algorithm that guides the initial data collection procedure for a simulation experiment that has a fixed data collection budget, with the objective of minimising input uncertainty in the simulation response. Results show that the algorithm is able to allocate data in a close to optimal manner and compared to two alternative data collection approaches returns a reduced level of input uncertainty.
AB - In stochastic simulation the input models used to drive the simulation are often estimated by collecting data from the real-world system. This can be an expensive and time consuming process so it would therefore be useful to have some guidance on how much data to collect for each input model. Estimating the input models via data introduces a source of variance in the simulation response known as input uncertainty. In this paper we propose a two stage algorithm that guides the initial data collection procedure for a simulation experiment that has a fixed data collection budget, with the objective of minimising input uncertainty in the simulation response. Results show that the algorithm is able to allocate data in a close to optimal manner and compared to two alternative data collection approaches returns a reduced level of input uncertainty.
KW - Input Uncertainty
KW - Budget Allocation
KW - Data Collection
U2 - 10.36819/SW21.013
DO - 10.36819/SW21.013
M3 - Conference contribution/Paper
T3 - Operational Research Society 10th Simulation Workshop, SW 2021 - Proceedings
SP - 127
EP - 136
BT - Operational Research Society 10th Simulation Workshop, SW 2021 - Proceedings
A2 - Fakhimi, Masoud
A2 - Boness, Tom
A2 - Robertson, Duncan
PB - Operational Research Society
CY - Birmingham
ER -