Nonlinear optimization codes for real time solution of large scale problems: a case study on the parallel vector supercomputers CRAY X-MP and IBM 3090/VF

2020 ◽  
pp. 201-211
Author(s):  
D Conforti ◽  
L Grandinetti
Keyword(s):  
Nonlinear Optimization ◽  
Real Time ◽  
Large Scale ◽  
Large Scale Problems ◽  
Time Solution

scholarly journals Space-time clustering-based method to optimize shareability in real-time ride-sharing

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262499
Author(s):  
Negin Alisoltani ◽  
Mostafa Ameli ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computation Time ◽  
Clustering Method ◽  
Matching Problem ◽  
Solution Quality ◽  
Mobility Service ◽  
Ride Sharing ◽  
Large Scale Problems ◽  
Spatio Temporal

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

scholarly journals Measuring Traffic in Cities Through a Large-Scale Online Platform

2019 ◽  
Vol 1 (2-3) ◽  
pp. 161-173 ◽  
Author(s):  
Vilhelm Verendel ◽  
Sonia Yeh
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Availability ◽  
Traffic Information ◽  
Traffic Data ◽  
Online Platform ◽  
Real Time Traffic ◽  
Data Source ◽  
Road Segments

Abstract Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.

scholarly journals Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Peter O’Donovan ◽  
Ken Bruton ◽  
Dominic T.J. O’Sullivan
Keyword(s):  
Information Technology ◽  
Real Time ◽  
Large Scale ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Reference Architecture ◽  
Air Handling Unit ◽  
Significant Effort

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a large-scale Air Handling Unit (AHU).

scholarly journals Pinpointing Patterns of Violence: A Comparative Genocide Studies Approach to Violence Escalation in the Ukrainian Holodomor

2021 ◽  
Vol 15 (2) ◽  
pp. 10-36
Author(s):  
Kristina Hook
Keyword(s):  
Mixed Methods ◽  
Real Time ◽  
Large Scale ◽  
Cross Cultural ◽  
Victim Selection ◽  
Contextual Variation ◽  
Genocide Studies ◽  
Genocidal Intent ◽  
Precise Moment

This article utilizes the case study of the 1930s Ukrainian Holodomor, an artificially induced famine under Joseph Stalin, to advance comparative genocide studies debates regarding the nature, onset, and prevention of large-scale violence. Fieldwide debates question how to 1) distinguish genocide from other forms of large-scale violence and 2) trace genocides as unfolding processes, rather than crescendoing events. To circumvent unproductive definitional arguments, methodologies that track large-scale violence according to numerically-based thresholds have substituted for dynamics-based analyses. Able to address aspects of the genocide puzzle, these methodologies struggle to incorporate cross-cultural contextual variation or elicit ripe moments for specific, real-time interventions. Demonstrating how genocide’s precise, changing dynamics can be mapped over its duration, I present and apply a new mixed methods methodology, affirming that historical cases can inform modern prevention efforts. By coding 1932–1933 Ukraine-specific correspondences to/from Stalin, I pinpoint the precise moment when genocidal intent and victim selection overlaps.

scholarly journals Into the thirteenth Month: A Case Study on the Outbreak Analytics and Modeling the spread of SARS-CoV-2 Infection in Pune City, India

2021 ◽  
Author(s):  
Joy Monteiro ◽  
Bhalchandra Pujari ◽  
Sarika Maitra Bhattacharrya ◽  
Anu Raghunathan ◽  
Ashwini Keskar ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Information Dissemination ◽  
Public Access ◽  
Accurate Information ◽  
Time Data ◽  
Outbreak Response ◽  
First Case ◽  
And Control

With more than 140 million people infected globally and 3 million deaths, the COVID 19 pandemic has left a lasting impact. A modern response to a pandemic of such proportions needs to focus on exploiting all available data to inform the response in real-time and allow evidence-based decision-making. The intermittent lockdowns in the last 13 months have created economic adversity to prevent anticipated large-scale mortality and relax the lockdowns have been an attempt at recovering and balancing economic needs and public health realities. This article is a comprehensive case study of the outbreak in the city limits of Pune, Maharashtra, India, to understand the evolution of the disease and transmission dynamics starting from the first case on March 9, 2020. A unique collaborative effort between the Pune Municipal Corporation (PMC), a government entity, and the Pune knowledge Cluster (PKC) allowed us to layout a context for outbreak response and intervention. We report here how access to granular data for a metropolitan city with pockets of very high-density populations will help analyze, in real-time, the dynamics of the pandemic and forecasts for better management and control of SARS-CoV-2. Outbreak data analytics resulted in a real-time data visualization dashboard for accurate information dissemination for public access on the epidemic's progress. As government agencies craft testing and vaccination policies and implement intervention strategies to mitigate a second wave, our case study underscores the criticality of data quality and analytics to decode community transmission of COVID-19.

scholarly journals Identification of potential precursors for the occurrence of Large-Scale Traveling Ionospheric Disturbances in a case study during September 2017

2020 ◽  
Vol 10 ◽  
pp. 32
Author(s):  
Arthur Amaral Ferreira ◽  
Claudia Borries ◽  
Chao Xiong ◽  
Renato Alves Borges ◽  
Jens Mielich ◽  
...  
Keyword(s):  
Real Time ◽  
Electron Density ◽  
Large Scale ◽  
Rate Of Change ◽  
Electron Content ◽  
Ionospheric Disturbances ◽  
Total Electron ◽  
Traveling Ionospheric Disturbances ◽  
Auroral Activity

Traveling Ionospheric Disturbances (TIDs) reflect changes in the ionospheric electron density which are caused by atmospheric gravity waves. These changes in the electron density impact the functionality of different applications such as precise navigation and high-frequency geolocation. The Horizon 2020 project TechTIDE establishes a warning system for the occurrence of TIDs with the motivation to mitigate their impact on communication and navigation applications. This requires the identification of appropriate indicators for the generation of TIDs and for this purpose we investigate potential precursors for the TID occurrence. This paper presents a case study of the double main phase geomagnetic storm, starting from the night of 7th September and lasting until the end of 8th September 2017. Detrended Total Electron Content (TEC) derived from Global Navigation Satellite System (GNSS) measurements from more than 880 ground stations in Europe was used to identify the occurrence of different types of large scale traveling ionospheric disturbances (LSTIDs) propagating over the European sector. In this case study, LSTIDs were observed more frequently and with higher amplitude during periods of enhanced auroral activity, as indicated by increased electrojet index (IE) from the International Monitor for Auroral Geomagnetic Effects (IMAGE). Our investigation suggests that Joule heating due to the dissipation of Pedersen currents is the main contributor to the excitation of the observed LSTIDs. We observe that the LSTIDs are excited predominantly after strong ionospheric perturbations at high-latitudes. Ionospheric parameters including TEC gradients, the Along Arc TEC Rate (AATR) index and the Rate Of change of TEC index (ROTI) have been analysed for their suitability to serve as a precursor for LSTID occurrence in mid-latitude Europe, aiming for near real-time indication and warning of LSTID activity. The results of the presented case study suggest that the AATR index and TEC gradients are promising candidates for near real-time indication and warning of the LSTIDs occurrence in mid-latitude Europe since they have a close relation to the source mechanisms of LSTIDs during periods of increased auroral activity.

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