A Multi-Objective Optimization via Simulation Framework for Restructuring Traffic Networks Subject to Increases in Population

Abstract This paper considers the problem of robust optimization, and presents the technique called Robust Optimization and Probabilistic Analysis of Robustness (ROPAR). It has been developed for finding robust optimum solutions of a particular class in model-based multi-objective optimization (MOO) problems (i.e. when the objective function is not known analytically), where some of the parameters or inputs to this model are assumed to be uncertain. A Monte Carlo simulation framework is used. It can be straightforwardly implemented in a distributed computing environment which allows the results to be obtained relatively fast. The technique is exemplified in the two case studies: (a) a benchmark problem commonly used to test MOO algorithms (a version of the ZDT1 function); and (b) a design problem of a simple storm drainage system, where the uncertainty is associated with design rainfall events. It is shown that the design found by ROPAR can adequately cope with these uncertainties. The approach can be useful for assisting in a wide range of risk-based decisions.

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

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3462187 ◽

2021 ◽

Vol 31 (4) ◽

pp. 1-15

Author(s):

Christine S. M. Currie ◽

Thomas Monks

Keyword(s):

Ranking And Selection ◽

Multi Objective Optimization ◽

Selection Methods ◽

Two Stage ◽

Multi Objective ◽

Common Random Numbers ◽

Statistical Knowledge ◽

Optimization Via Simulation ◽

Selection For ◽

The One

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.

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Replicated Computational Results (RCR) Report for“A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation”

ACM Transactions on Modeling and Computer Simulation ◽

10.1145/3453987 ◽

2021 ◽

Vol 31 (4) ◽

pp. 1-2

Author(s):

Philipp Andelfinger

Keyword(s):

Subset Selection ◽

Ranking And Selection ◽

Practical Approach ◽

Multi Objective Optimization ◽

Multi Objective ◽

Optimization Via Simulation ◽

Stage Scheme ◽

Simulation Based ◽

Selection For ◽

Configuration Problem

In “A Practical Approach to Subset Selection for Multi-Objective Optimization via Simulation,” Currie and Monks propose an algorithm for multi-objective simulation-based optimization. In contrast to sequential ranking and selection schemes, their algorithm follows a two-stage scheme. The approach is evaluated by comparing the results to those obtained using the existing OCBA-m algorithm for synthetic problems and for a hospital ward configuration problem. The authors provide the Python code used in the experiments in the form of Jupyter notebooks. The code successfully reproduced the results shown in the article.

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