改进的移动社交网络中无关群众工作者调度算法
Improved Algorithms for Unrelated Crowd Worker Scheduling in Mobile Social Networks
AI总结：
本文改进了移动社交网络中无关群众工作者调度问题的算法，针对不同任务所需服务时间差异，提出更优的近似比和竞争比，同时引入随机近似算法并提升计算效率。
摘要：
本文针对移动社交网络中无关群众工作者调度问题，提出改进的算法。针对不同任务所需服务时间差异，改进了Largest-Ratio-First（LRF）调度算法的近似比，并为其在线版本提供了更新的竞争比。在无关群众工作者环境下，引入了随机近似算法，实现了期望近似比为1.45的结果，优于之前工作的1.5近似比。此外，我们还提供了该算法的确定性方法。为进一步提升计算效率，我们提出了一种利用单个群众工作者最优调度特性（按Smith比率非递增顺序安排任务）的算法。实验结果表明，所提方法在三个LRF算法变体上表现更优。
英文摘要：
This paper studies task scheduling with unrelated crowd workers in mobile social networks, where the service time of a task depends on the crowd worker that performs it, and the objective is to minimize the total weighted completion time. For identical crowd workers, we tighten the approximation ratio of the Largest-Ratio-First (LRF) algorithm and update the competitive ratio of its online version. For unrelated crowd workers, we show that the time-indexed rounding framework of Im and Li remains valid when worker-dependent contact delays are added, which yields a randomized algorithm with an expected approximation ratio of 1.45; this improves upon the expected ratio of $1.5+ε$ obtained in our previous work for identical crowd workers. We also derandomize this algorithm. To improve computational efficiency, we further propose an algorithm that exploits the optimality of the Smith-ratio order on a single crowd worker, and we prove that it is a deterministic 1.5-approximation algorithm and that its polynomial-time version achieves an approximation ratio arbitrarily close to 1.5. Experiments on synthetic instances and on instances derived from real contact traces show that this algorithm outperforms three variants of LRF in all tested settings, with the largest advantage when the crowd workers are highly heterogeneous, performs on par with a local-search heuristic for unrelated machines, and is within 0.4\% of the optimum on average on small instances that can be solved exactly.
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