Distributionally Robust Resource Allocation with Trust-aided Parametric Information Fusion

TL;DR

This paper introduces a distributionally robust resource allocation method that dynamically fuses information from multiple sources based on trust, improving decision accuracy in uncertain environments like wildfire suppression.

Contribution

It proposes a trust-based information fusion framework with a tractable linear programming reformulation for distributionally robust resource allocation.

Findings

Enhanced decision accuracy in wildfire resource allocation

Linear growth in computational time with data sources

Effective trust adaptation based on reference information quality

Abstract

Reference information plays an essential role for making decisions under uncertainty, yet may vary across multiple data sources. In this paper, we study resource allocation in stochastic dynamic environments, where we perform information fusion based on trust of different data sources, to design an ambiguity set for attaining distributionally robust resource allocation solutions. We dynamically update the trust parameter to simulate the decision maker's trust change based on losses caused by mis-specified reference information. We show an equivalent tractable linear programming reformulation of the distributionally robust optimization model and demonstrate the performance in a wildfire suppression application, where we use drone and satellite data to estimate the needs of resources in different regions. We demonstrate how our methods can improve trust and decision accuracy. The computational time grows linearly in the number of data sources and problem sizes.