跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1072新增论文
+845其中 AI 相关
+94已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描:2026-11-09 13:24 · 全站最近入库:2026-11-09 13:22各信源进度 →

统计口径与数据新鲜度

统计 2026-11-08 14:23 — 2026-11-09 14:23(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:2026-11-09 13:24最近发现新增版本:2026-11-09 13:22库内论文最新更新时间:2026-11-07 02:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

18,172 已收录论文15,506 AI 相关4,527 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常,查看状态 →

找到 2 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-09 13:22(北京时间)
新论文 · v1arXiv:2611.09611

Online Timely Bin Packing Revisited: A Simpler Algorithm with Optimality in Special Cases

Pei-Lin Chiang, Wing-Kai Hon, Ying-Chia Huang, Li-Tzu Yang

原文摘要

Online Timely Bin Packing is a variant of classical online bin packing in which items arrive over time with sizes and deadlines. Packing an item may be deferred until its deadline, but once a collection of items is packed into a bin, the bin is closed and cannot be used again. The objective is to minimize the total number of bins used. The problem was introduced by Khattar in 2023. In 2025, Aminian proposed Discrete-Greedy, a deterministic online algorithm with a competitive ratio of exactly $2$, which remains the best-known upper bound. Its analysis is particularly simple; however, the algorithm requires maintaining a fractional simulation internally and may leave usable space in some bins unfilled. We propose Thrifty-Greedy, a simple deterministic online algorithm that does not require a fractional simulation. We prove that its competitive ratio is exactly $2$, matching the best-known upper bound. At the same time, the algorithm automatically achieves the optimal competitive ratio on two natural classes of inputs: it is $1$-competitive when all items have the same size, and $1.5$-competitive when every item has size in $\{1/3,2/3\}$. In contrast, Discrete-Greedy has a competitive ratio of at least $2$ on both classes.

cs.DS

新论文 · v1arXiv:2611.08792

Online Bin Packing with Dispatch Deadlines

Pei-Lin Chiang, Wing-Kai Hon, Kazuo Iwama, Li-Tzu Yang

原文摘要

We study a generalization of online bin packing by introducing dispatch deadlines. Items arrive over time in an online fashion. Each item has an arrival time, a size in $(0,1]$, and a deadline, all of which are revealed only when the item arrives. The algorithm must immediately and irrevocably assign the item to a unit-capacity bin that has not yet been dispatched. Each bin must be dispatched no later than the earliest deadline of the items assigned to it, and once it is dispatched, it cannot be used for future items. The goal is to minimize the number of bins used. Applications arise naturally in logistics. Additionally, the offline version of this problem has appeared under several names, including the clique partition problem in weighted interval graphs and the BinSchedule problem. This problem is also a special case of online bin packing with conflicts and is closely related to online bin packing models with delay costs. We study the competitive ratios of several classical online bin packing algorithms when applied to this problem, and show that a similar hierarchy of competitive ratios carries over. Our main technical results are that the competitive ratio of First-Fit is exactly $16/7 \approx 2.28571$, and that no deterministic online algorithm has a competitive ratio less than $11/5 = 2.2$, showing that First-Fit is quite close to best possible. In addition, Next-Fit and Worst-Fit have competitive ratio exactly $3$, and every algorithm in the Any-Fit class has competitive ratio between $16/7$ and $3$.

cs.DS