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Remote Laboratory Management: Respiratory Virus Diagnostics

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Published on: April 6, 2019

Simulation-based multi-criteria decision making: an interactive method with a case study on infectious disease

Fabian Dunke¹, Stefan Nickel¹

¹Institute of Operations Research, Discrete Optimization and Logistics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany.

Annals of Operations Research

|October 18, 2021

Summary

This study introduces an interactive simulation method for complex decision-making with multiple goals and uncertainty. It aids in planning and understanding system dynamics for reliable decision alternatives.

Keywords:

Infectious disease epidemic simulation Multi-criteria decision making Sensitivity analysis Simulation-based decision making

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Area of Science:

Systems Science
Decision Science
Computational Science

Background:

Complex dynamic systems often involve multiple conflicting objectives and parameter uncertainty.
Centralized decision-making requires robust methods to navigate trade-offs between competing goals.
Existing methodologies may not adequately address the interplay of system dynamics, uncertainty, and multi-criteria decision-making.

Purpose of the Study:

To devise an interactive simulation-based methodology for planning and decision-making in complex dynamic systems.
To integrate global sensitivity analysis and interactive adjustments for enhanced knowledge acquisition.
To support decision-makers in situations with time dynamics, uncertainty, and multiple objectives.

Main Methods:

Interactive simulation employing simulation models and global sensitivity analysis (Sobol' sensitivity indices).
Decision-maker participation through interactive adjustment of control variables and system parameters.
Utilizing Pareto optimality for evaluating decision alternatives.

Main Results:

The methodology successfully integrates system dynamics, parameter uncertainty, and multi-criteria decision-making.
Sensitivity analysis results enhance confidence in the reliability of decision alternatives.
The approach was validated in a case study simulating an infectious disease epidemic.

Conclusions:

The developed simulation-based methodology is a viable tool for complex decision support.
It effectively addresses challenges in dynamic systems with uncertainty and multiple objectives.
The interactive nature facilitates informed decision-making in critical scenarios like pandemics.