Multistage Online Maxmin Allocation of Indivisible Entities

TL;DR

This paper introduces a multistage online algorithm for maxmin allocation of indivisible entities, improving approximation ratios by incorporating lookahead and stability considerations in dynamic settings.

Contribution

It develops a $w$-lookahead algorithm for online maxmin allocation that enhances previous ratios by accounting for changing restrictions, values, and stability rewards.

Findings

Achieves a competitive ratio of $(1-c)\rho$, surpassing previous bounds for 1-lookahead.

Provides a new algorithm applicable for any fixed lookahead window $w \geq 1$.

Improves approximation guarantees in dynamic, multi-stage allocation scenarios.

Abstract

We consider an online allocation problem that involves a set $P$ of $n$ players and a set $E$ of $m$ indivisible entities over discrete time steps $1,2,\ldots,\tau$. At each time step $t \in [1,\tau]$, for every entity $e \in E$, there is a restriction list $L_t(e)$ that prescribes the subset of players to whom $e$ can be assigned and a non-negative value $v_t(e,p)$ of $e$ to every player $p \in P$. The sets $P$ and $E$ are fixed beforehand. The sets $L_t(\cdot)$ and values $v_t(\cdot,\cdot)$ are given in an online fashion. An allocation is a distribution of $E$ among $P$, and we are interested in the minimum total value of the entities received by a player according to the allocation. In the static case, it is NP-hard to find an optimal allocation the maximizes this minimum value. On the other hand, $\rho$-approximation algorithms have been developed for certain values of $\rho \in (0,1]$. We propose a $w$-lookahead algorithm for the multistage online maxmin allocation problem for any fixed $w \geq 1$ in which the restriction lists and values of entities may change between time steps, and there is a fixed stability reward for an entity to be assigned to the same player from one time step to the next. The objective is to maximize the sum of the minimum values and stability rewards over the time steps $1, 2, \ldots, \tau$. Our algorithm achieves a competitive ratio of $(1-c)\rho$, where $c$ is the positive root of the equation $wc^2 = \rho (w+1)(1-c)$. When $w = 1$, it is greater than $\frac{\rho}{4\rho+2} + \frac{\rho}{10}$, which improves upon the previous ratio of $\frac{\rho}{4\rho+2 - 2^{1-\tau}(2\rho+1)}$ obtained for the case of 1-lookahead.