Online Minimum Bipartite Matching(OMBM Recently, most applications of the MBM issue need to be addressed in real-time, which is called the online MBM(OMBM) problem CAR 神州专车 ∪BER DIDi More than a journey
⚫ Recently, most applications of the MBM issue need to be addressed in real-time, which is called the online MBM (OMBM) problem Online Minimum Bipartite Matching (OMBM)
Online Minimum Bipartite Matching(OMBM Recently, most applications of the MBM issue need to be addressed in real-time, which is called the online MBM(OMBM) problem CAR Gigal 神州荟车 ∪BER taskrabbit zy Life is busy. We hel DiDi grubHub happy eating aze
⚫ Recently, most applications of the MBM issue need to be addressed in real-time, which is called the online MBM (OMBM) problem Online Minimum Bipartite Matching (OMBM)
9 Challenges of the OMBM Problem o Real-time taxi-calling services usually adopt nearest neighbor (NN strategy to address the mbm issues Once a task appears, it should be assigned immediately 的标 有+Q确工 A知 金曰: 青云北 有 。一: The cost of the m nN strategy ia. 友道社 各
The cost of the NN strategy ⚫ Real-time taxi-calling services usually adopt ‘nearest neighbor (NN)’ strategy to address the OMBM issues. ⚫ Once a task appears, it should be assigned immediately. Challenges of the OMBM Problem 9
10 Challenges of the OMBM Problem Real-time taxi-calling services usually adopt nearest neighbor(NN strategy to address task assignment issues. Once a task appears, it should be assigned immediately. If we know everything in advance, the offline oPt is shown 是1 容 青五北区 The offline 定棒树北 cost 大 m 出 小肃社区 友社 中A
⚫ Real-time taxi-calling services usually adopt ‘nearest neighbor (NN)’ strategy to address task assignment issues. ⚫ Once a task appears, it should be assigned immediately. ⚫ If we know everything in advance, the offline OPT is shown. Challenges of the OMBM Problem The offline cost 10
Contributions of our work Inspired by many observations in applications and a comprehensive experimental study of the OMBM problem, our contributions are Motivations Contributions 1 Is Greedy really the worst? Greedy has good performance Is the worst-case analysis 2 appropriate for the OMB Worst-case VS. Average-case analysIs problem in practice? Are implementations and 3 experimental evaluations Uniform implementations and evaluations are provided uniform? Open question: the average-case approximation ratio (aka competitive ratio) of the simple greedy algorithm for the OMBM problem should be constant
⚫ Inspired by many observations in applications and a comprehensive experimental study of the OMBM problem, our contributions are Contributions of Our Work 11 Motivations Contributions 1 Is Greedy really the worst?Greedy has good performance. 2 Is the worst-case analysis appropriate for the OMBM problem in practice? Worst-case vs. Average-case analysis. 3 Are implementations and experimental evaluations uniform? Uniform implementations and evaluations are provided. Open question: the average-case approximation ratio (a.k.a competitive ratio) of the simple greedy algorithm for the OMBM problem should be constant!