A better semi-online algorithm for Q3/s1 = s2≤ s3/Cmin with the known largest size

2011 ◽  
Vol 28 (1) ◽  
pp. 111-116
Author(s):  
Sheng-yi Cai ◽  
Qi-fan Yang
Keyword(s):  
Algorithmica ◽  
2021 ◽  
Author(s):  
Matthias Englert ◽  
David Mezlaf ◽  
Matthias Westermann

AbstractIn the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and $$\approx 1.4659$$ ≈ 1.4659 . They show that $$k = O(m)$$ k = O ( m ) is sufficient to achieve this bound and no $$k = o(n)$$ k = o ( n ) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a $$\delta = \varTheta (1)$$ δ = Θ ( 1 ) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than $$1.4659 + \delta $$ 1.4659 + δ with $$k = o(n)$$ k = o ( n ) . We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and $$\approx 1.7992$$ ≈ 1.7992 with $$k = O(m)$$ k = O ( m ) . We also show that $$k = \varOmega (m)$$ k = Ω ( m ) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.


2019 ◽  
Vol 56 (2) ◽  
pp. 517-528 ◽  
Author(s):  
Juan D. Jurado ◽  
Clark C. McGehee

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 192393-192407
Author(s):  
Shengling Geng ◽  
Banghe Han ◽  
Ruize Wu ◽  
Runqing Xu

Author(s):  
Hui Liu ◽  
Zhan Shi ◽  
Jia-Chen Gu ◽  
Quan Liu ◽  
Si Wei ◽  
...  

Dialogue disentanglement aims to separate intermingled messages into detached sessions. The existing research focuses on two-step architectures, in which a model first retrieves the relationships between two messages and then divides the message stream into separate clusters. Almost all existing work puts significant efforts on selecting features for message-pair classification and clustering, while ignoring the semantic coherence within each session. In this paper, we introduce the first end-to- end transition-based model for online dialogue disentanglement. Our model captures the sequential information of each session as the online algorithm proceeds on processing a dialogue. The coherence in a session is hence modeled when messages are sequentially added into their best-matching sessions. Meanwhile, the research field still lacks data for studying end-to-end dialogue disentanglement, so we construct a large-scale dataset by extracting coherent dialogues from online movie scripts. We evaluate our model on both the dataset we developed and the publicly available Ubuntu IRC dataset [Kummerfeld et al., 2019]. The results show that our model significantly outperforms the existing algorithms. Further experiments demonstrate that our model better captures the sequential semantics and obtains more coherent disentangled sessions.


2021 ◽  
Author(s):  
Angeliki Mathioudaki ◽  
Georgios Tsaousoglou ◽  
Emmanouel varvarigos ◽  
Dimitris Fotakis

We present an online algorithm for scheduling the charging of Electric Vehicles (EVs) in a Charging Station, aiming to optimize the overall quality of service through sum of weighted completion time minimization. Upon arrival of each EV, the algorithm generates a menu of service-price options. By letting the EV users pick their most preferable option, the algorithm offers guaranteed quality of service, achieves near optimal performance, and prevents the users from acting strategically.


10.37236/7231 ◽  
2018 ◽  
Vol 25 (2) ◽  
Author(s):  
Bartłomiej Bosek ◽  
H. A. Kierstead ◽  
Tomasz Krawczyk ◽  
Grzegorz Matecki ◽  
Matthew E. Smith
Keyword(s):  

Bosek and Krawczyk exhibited an online algorithm for partitioning an online poset of width $w$ into $w^{14\lg w}$ chains. We improve this to $w^{6.5 \lg w + 7}$ with a simpler and shorter proof by combining the work of Bosek & Krawczyk with work of Kierstead & Smith on First-Fit chain partitioning of ladder-free posets. We also provide examples illustrating the limits of our approach.


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