Beam-ACO Based on Stochastic Sampling for Makespan Optimization Concerning the TSP with Time Windows

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

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Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

Journal of Climate ◽

10.1175/jcli-d-18-0669.1 ◽

2020 ◽

Vol 33 (1) ◽

pp. 397-404 ◽

Cited By ~ 1

Author(s):

Nicholas Lewis ◽

Judith Curry

Keyword(s):

Time Windows ◽

Internal Variability ◽

Historical Record ◽

Cmip5 Models ◽

Temperature Record ◽

Ocean Heat Uptake ◽

Transient Climate Response ◽

Total Uncertainty ◽

The Impact ◽

The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

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Spatiotemporal tracing of pandemic spread from infection data

Scientific Reports ◽

10.1038/s41598-021-97207-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Satyaki Roy ◽

Preetom Biswas ◽

Preetam Ghosh

Keyword(s):

New York ◽

Network Inference ◽

Spatial Interaction ◽

Time Windows ◽

New York State ◽

Mitigation Measures ◽

Contact Tracing ◽

Long Distance ◽

Spatiotemporal Models ◽

Spread Of Infection

AbstractCOVID-19, a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus, has claimed millions of lives worldwide. Amid soaring contagion due to newer strains of the virus, it is imperative to design dynamic, spatiotemporal models to contain the spread of infection during future outbreaks of the same or variants of the virus. The reliance on existing prediction and contact tracing approaches on prior knowledge of inter- or intra-zone mobility renders them impracticable. We present a spatiotemporal approach that employs a network inference approach with sliding time windows solely on the date and number of daily infection numbers of zones within a geographical region to generate temporal networks capturing the influence of each zone on another. It helps analyze the spatial interaction among the hotspot or spreader zones and highly affected zones based on the flow of network contagion traffic. We apply the proposed approach to the daily infection counts of New York State as well as the states of USA to show that it effectively measures the phase shifts in the pandemic timeline. It identifies the spreaders and affected zones at different time points and helps infer the trajectory of the pandemic spread across the country. A small set of zones periodically exhibit a very high outflow of contagion traffic over time, suggesting that they act as the key spreaders of infection. Moreover, the strong influence between the majority of non-neighbor regions suggests that the overall spread of infection is a result of the unavoidable long-distance trips by a large number of people as opposed to the shorter trips at a county level, thereby informing future mitigation measures and public policies.

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Transshipment through crossdocks with inventory and time windows

Naval Research Logistics (NRL) ◽

10.1002/nav.20113 ◽

2005 ◽

Vol 52 (8) ◽

pp. 724-733 ◽

Cited By ~ 42

Author(s):

Andrew Lim ◽

Zhaowei Miao ◽

Brian Rodrigues ◽

Zhou Xu

Keyword(s):

Time Windows

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Frequency Selective Auto-Encoder for Smart Meter Data Compression

Sensors ◽

10.3390/s21041521 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1521

Author(s):

Jihoon Lee ◽

Seungwook Yoon ◽

Euiseok Hwang

Keyword(s):

Data Compression ◽

High Frequency ◽

Spectral Properties ◽

Situational Awareness ◽

Time Windows ◽

Low Frequency ◽

Smart Meters ◽

Compression Performance ◽

Reconstruction Performance ◽

Frequency Selective

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.

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