Lower bounds on the performance of online algorithms for relaxed packing problems

TL;DR

This paper establishes new lower bounds on the competitive ratios of online algorithms for two relaxed packing problems, highlighting fundamental limitations in their efficiency.

Contribution

It introduces novel lower bounds for online removable knapsack and appointment scheduling problems, advancing understanding of their inherent computational challenges.

Findings

Derived lower bounds for competitive ratios

Identified limitations in online packing efficiency

Enhanced theoretical understanding of relaxed packing problems

Abstract

We prove new lower bounds for suitable competitive ratio measures of two relaxed online packing problems: online removable multiple knapsack, and a recently introduced online minimum peak appointment scheduling problem. The high level objective in both problems is to pack arriving items of sizes at most 1 into bins of capacity 1 as efficiently as possible, but the exact formalizations differ. In the appointment scheduling problem, every item has to be assigned to a position, which can be seen as a time interval during a workday of length 1. That is, items are not assigned to bins, but only once all the items are processed, the optimal number of bins subject to chosen positions is determined, and this is the cost of the online algorithm. On the other hand, in the removable knapsack problem there is a fixed number of bins, and the goal of packing items, which consists in choosing a particular bin for every packed item (and nothing else), is to pack as valuable a subset as possible. In this last problem it is possible to reject items, that is, deliberately not pack them, as well as to remove packed items at any later point in time, which adds flexibility to the problem.