A Practical Approach to Subset Selection for Multi-objective Optimization via Simulation

Abstract

We describe a practical two-stage algorithm, BootComp, for multi-objective optimization via simulation. Our algorithm finds a subset of good designs that a decision-maker can compare to identify the one that works best when considering all aspects of the system, including those that cannot be modeled. BootComp is designed to be straightforward to implement by a practitioner with basic statistical knowledge in a simulation package that does not support sequential ranking and selection. These requirements restrict us to a two-stage procedure that works with any distributions of the outputs and allows for the use of common random numbers. Comparisons with sequential ranking and selection methods suggest that it performs well, and we also demonstrate its use analyzing a real simulation aiming to determine the optimal ward configuration for a UK hospital.

Open Science for Computer Simulation

“…free simulation on the web; albeit with very limited computational resources, flexibilty and security relative to a full open science gateway. For example, in R users might consider a Shiny App or in Python users might consider Streamlit or BinderHub (for example, Currie and Monks (2021) ; Monks (2020)). The suitability of these tools will vary on the application of modelling and simulation, but offer a simple way for others to access an executable model remotely.…”