New Approximation Algorithms for the Heterogeneous Weighted Delivery Problem

Abstract

We study the heterogeneous weighted delivery (HWD) problem introduced in [Bärtschi et al., STACS’17] where k heterogeneous mobile agents (e.g., robots, vehicles, etc.), initially positioned on vertices of an n-vertex edge-weighted graph G, have to deliver m messages. Each message is initially placed on a source vertex of G and needs to be delivered to a target vertex of G. Each agent can move along the edges of G and carry at most one message at any time. Each agent has a rate of energy consumption per unit of traveled distance and the goal is that of delivering all messages using the minimum overall amount of energy.

This problem has been shown to be NP-hard even when $k=1$, and is $4\rho$-approximable where $\rho$ is the ratio between the maximum and minimum energy consumption of the agents. In this paper, we provide approximation algorithms with approximation ratios independent of the energy consumption rates. First, we design a polynomial-time 8-approximation algorithm for $k=O(\sqrt{\log n})$, closing a problem left open in [Bärtschi et al., ATMOS’17]. This algorithm can be turned into a O(k)-approximation algorithm that always runs in polynomial-time, regardless of the values of k. Then, we show that HWD problem is 36-approximable in polynomial-time when each agent has one of two possible consumption rates. Finally, we design a polynomial-time $\widetilde{O}(\log ^3 n)$-approximation algorithm for the general case.

This work was partially funded by the grants “Fondo di Ateneo per la Ricerca 2020” from the University of Sassari, and “ALgorithmic aspects of BLOckchain TECHnology” (E89C20000620005) from the University of Rome “Tor Vergata”.