Pack, Remove, Reserve -- Online Knapsack with Second Thoughts

2026-07-15

AI Summary

This study addresses the online proportional knapsack problem equipped with a dual compensation mechanism—reservation (at cost αx) and removal (at cost βy). For arbitrary parameter pairs (α, β), the work provides the first complete characterization of the competitive ratio across a three-region partition of the parameter space, revealing a synergistic effect in an intermediate region where combining both mechanisms strictly outperforms using either one alone. Through competitive analysis, threshold-based policy design, and piecewise optimality proofs, the authors develop a hybrid online algorithm integrating reservation and removal operations, establishing tight upper and lower bounds on the competitive ratio for all (α, β). In the synergy region, they propose a natural strategy: reserve items up to a threshold, then pack greedily while dynamically removing items as needed.

Abstract

We study the online proportional knapsack problem with two paid forms of recourse. Items arrive one by one and must be handled immediately, without knowledge of the future: an algorithm may pack an item $x$, reject it, or reserve it for possible later use at proportional cost $αx$; additionally, it may at any time remove previously packed items, at proportional cost $βy$ for each removed item $y$. Reservation and removal have each been analyzed in isolation, but their combination raises a natural question: is the better of the two mechanisms always optimal on its own, or is there a region in the parameter space spanned by $α$ and $β$ in which they genuinely enter into a symbiosis? So far, this question has only been answered for the special case of free removal ($β= 0$), leaving the vast majority of the parameter space unexplored. We close this gap, determining matching upper and lower bounds on the competitive ratio for every pair of cost parameters $(α, β)$ and revealing three qualitatively different regimes. In some regions, reservation alone already achieves the optimal ratio; in others, removal alone does. However, most interestingly, in the heart of the parameter space lies a symbiosis region in which combining both mechanisms is strictly better than either one on its own. The optimal algorithm in the symbiosis region is a natural blend of the two known single-mechanism strategies: postponing commitment by reserving until a threshold is reached, then packing greedily and revising via removal.

Problem

• online knapsack
• recourse
• reservation
• removal
• competitive ratio

Innovation

• online knapsack
• recourse mechanisms
• competitive analysis
• reservation and removal
• symbiosis region