Supervised Learning for Arrival Time Estimations in Restaurant Meal Delivery

2021 ◽  
Author(s):  
Florentin D. Hildebrandt ◽  
Marlin W. Ulmer
Keyword(s):  
Supervised Learning ◽  
Arrival Time ◽  
Computational Study ◽  
Time Estimation ◽  
Computational Time ◽  
Arrival Times ◽  
Meal Preparation ◽  
Online Simulation ◽  
Online Simulations ◽  
Meal Delivery

Restaurant meal delivery companies have begun to provide customers with meal arrival time estimations to inform the customers’ selection. Accurate estimations increase customer experience, whereas inaccurate estimations may lead to dissatisfaction. Estimating arrival times is a challenging prediction problem because of uncertainty in both delivery and meal preparation process. To account for both processes, we present an offline and online-offline estimation approaches. Our offline method uses supervised learning to map state features directly to expected arrival times. Our online-offline method pairs online simulations with an offline approximation of the delivery vehicles’ routing policy, again achieved via supervised learning. Our computational study shows that both methods perform comparably to a full near-optimal online simulation at a fraction of the computational time. We present an extensive analysis on how arrival time estimation changes the experience for customers, restaurants, and the platform. Our results indicate that accurate arrival times not only raise service perception but also improve the overall delivery system by guiding customer selections, effectively resulting in faster delivery and fresher food.

scholarly journals Generating Uncertainty Distributions for Seismic Signal Onset Times

2020 ◽  
Author(s):  
Matt Peterson ◽  
Charlie Vollmer ◽  
Ronald Brogan ◽  
David J. Stracuzzi ◽  
Chistopher J. Young
Keyword(s):  
Arrival Time ◽  
Seismic Analysis ◽  
Critical Role ◽  
Time Estimation ◽  
Seismic Signal ◽  
Location Estimation ◽  
Source Characterization ◽  
Arrival Times ◽  
Signal Arrival ◽  
Uncertainty Estimates

ABSTRACT Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.

scholarly journals Short-Term Scheduling Model of Cluster Tool in Wafer Fabrication

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1029
Author(s):  
Ying-Mei Tu
Keyword(s):  
Arrival Time ◽  
Time Estimation ◽  
Advanced Technology ◽  
Shop Floor ◽  
Process Time ◽  
Short Term ◽  
Work In Process ◽  
Scheduling Model ◽  
Cluster Tools ◽  
Cluster Tool

Since last decade, the cluster tool has been mainstream in modern semiconductor manufacturing factories. In general, the cluster tool occupies 60% to 70% of production machines for advanced technology factories. The most characteristic feature of this kind of equipment is to integrate the relevant processes into one single machine to reduce wafer transportation time and prevent wafer contaminations as well. Nevertheless, cluster tools also increase the difficulty of production planning significantly, particularly for shop floor control due to complicated machine configurations. The main objective of this study is to propose a short-term scheduling model. The noteworthy goal of scheduling is to maximize the throughput within time constraints. There are two modules included in this scheduling model—arrival time estimation and short-term scheduling. The concept of the dynamic cycle time of the product’s step is applied to estimate the arrival time of the work in process (WIP) in front of machine. Furthermore, in order to avoid violating the time constraint of the WIP, an algorithm to calculate the latest time of the WIP to process on the machine is developed. Based on the latest process time of the WIP and the combination efficiency table, the production schedule of the cluster tools can be re-arranged to fulfill the production goal. The scheduling process will be renewed every three hours to make sure of the effectiveness and good performance of the schedule.

scholarly journals Detection of specific areas and densities for ultrasound tomography

2019 ◽  
pp. 121-127
Author(s):  
Victoria Erofeeva ◽  
Vasilisa Galyamina ◽  
Kseniya Gonta ◽  
Anna Leonova ◽  
Oleg Granichin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Ultrasound Imaging ◽  
Arrival Time ◽  
Detection Accuracy ◽  
Problem Solution ◽  
Ultrasound Tomography ◽  
Arrival Times ◽  
Whole Process ◽  
Imaging Reconstruction ◽  
Tomography Data

In this paper we consider the problem of ultrasound tomography. Recently, an increased interest in ultrasound tomography has been caused by non-invasiveness of the method and increased detection accuracy (as compared to radiation tomography), and also ultrasound tomography does not put at risk human health. We study possibilities of detection of specific areas and determining their density using ultrasound tomography data. The process of image reconstruction based on ultrasound data is computationally complex and time consuming. It contains the following parts: calculation of the time-of-flight (TOF) of a signal, detection of specific areas, calculation of density of specific areas. The calculation of the arrival time of a signal is a very important part, because the errors in the calculation of quantities strongly influence the total problem solution. We offer ultrasound imaging reconstruction technology that can be easily parallelized. The whole process is described: from extracting the arrival times of signals raw data feeding from physical receivers to obtaining the desired results.

scholarly journals On optimal periodic dividend and capital injection strategies for spectrally negative Lévy models

2018 ◽  
Vol 55 (4) ◽  
pp. 1272-1286 ◽  
Author(s):  
Kei Noba ◽  
José-Luis Pérez ◽  
Kazutoshi Yamazaki ◽  
Kouji Yano
Keyword(s):  
Arrival Time ◽  
Arrival Times ◽  
Classical Sense ◽  
Capital Injection ◽  
Dividend Payments ◽  
Poisson Arrival ◽  
Optimal Dividend ◽  
Injection Strategies

Abstract De Finetti’s optimal dividend problem has recently been extended to the case when dividend payments can be made only at Poisson arrival times. In this paper we consider the version with bail-outs where the surplus must be nonnegative uniformly in time. For a general spectrally negative Lévy model, we show the optimality of a Parisian-classical reflection strategy that pays the excess above a given barrier at each Poisson arrival time and also reflects from below at 0 in the classical sense.

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