Optimizing Resource Distribution in a One-Dimensional Logistic Diffusion Model

TL;DR

This paper investigates optimal resource distribution in a one-dimensional logistic diffusion model, introducing a block decomposition and advantage function to analyze and characterize optimal solutions, especially for small total resources.

Contribution

It introduces a novel block decomposition and advantage function approach to analyze and explicitly characterize optimal resource distributions in a one-dimensional logistic model.

Findings

Optimal resources are bang-bang for large dispersal rates.

Superlinearity of the advantage function for small total resources.

Explicit characterization of optimal control when total resource is small.

Abstract

In this article, we study the optimization of resource distributions in a one-dimensional logistic diffusive model. The goal is to determine a distribution on a bounded one-dimensional domain that maximizes the total population at equilibrium. Previous works have shown that optimal resources are bang-bang, and in one dimension, a sufficiently large dispersal rate forces the optimal resource to be concentrated. For general dispersal rates, however, the analysis becomes more difficult because the equilibrium population may behave irregularly, and the optimal resource may be fragmented. To address this, we introduce a block decomposition that reduces fragmented resources to a collection of concentrated blocks. We then define an advantage function, which measures the gain in the equilibrium population obtained by allocating resources on a fixed interval and is used to analyze the contribution of each block to the total population. This function also allows us to reformulate the optimization problem as a convexity analysis of the advantage function. We prove the superlinearity of this function when the total resource is small enough, and this property leads to an explicit characterization of the optimal control with sufficiently small total resource.