Minmax Regret 1-Sink Location Problems on Dynamic Flow Path Networks with Parametric Weights

TL;DR

This paper introduces an algorithm for locating a sink in a dynamic flow network under parametric uncertainty, minimizing the worst-case regret, with complexity improvements for uniform capacity networks.

Contribution

It formulates the minmax regret 1-sink location problem on dynamic flow networks with parametric weights and proposes an efficient algorithm with complexity analysis.

Findings

Proposed an $O(n^4 2^{\alpha(n)} \alpha(n) \log n)$ time algorithm.

Reduced complexity to $O(n^3 2^{\alpha(n)} ext{ extasciicircum} \alpha(n) ext{ extasciicircum} \log n)$ for uniform capacity networks.

Established a new approach to handle uncertainty in dynamic flow network location problems.

Abstract

This paper addresses the minmax regret 1-sink location problem on dynamic flow path networks with parametric weights. We are given a dynamic flow network consisting of an undirected path with positive edge lengths, positive edge capacities, and nonnegative vertex weights. A path can be considered as a road, an edge length as the distance along the road and a vertex weight as the number of people at the site. An edge capacity limits the number of people that can enter the edge per unit time. We consider the problem of locating a sink in the network, to which all the people evacuate from the vertices as quickly as possible. In our model, each weight is represented by a linear function in a common parameter $t$, and the decision maker who determines the location of a sink does not know the value of $t$. We formulate the sink location problem under such uncertainty as the minmax regret problem. Given $t$ and a sink location $x$, the cost of $x$ under $t$ is the sum of arrival times at $x$ for all the people determined by $t$. The regret for $x$ under $t$ is the gap between the cost of $x$ under $t$ and the optimal cost under $t$. The task of the problem is formulated as the one to find a sink location that minimizes the maximum regret over all $t$. For the problem, we propose an $O(n^4 2^{\alpha(n)} \alpha(n) \log n)$ time algorithm where $n$ is the number of vertices in the network and $\alpha(\cdot)$ is the inverse Ackermann function. Also for the special case in which every edge has the same capacity, we show that the complexity can be reduced to $O(n^3 2^{\alpha(n)} \alpha(n) \log n)$.