scholarly journals Measuring Disruptions in Last-Mile Delivery Operations

Logistics ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Andrés Muñoz-Villamizar ◽  
Elyn L. Solano-Charris ◽  
Lorena Reyes-Rubiano ◽  
Javier Faulin
Keyword(s):  
Real Data ◽  
Application Programming Interface ◽  
Transportation Systems ◽  
Service Levels ◽  
Key Stakeholders ◽  
Last Mile ◽  
Development Plans ◽  
Application Programming ◽  
Last Mile Delivery ◽  
The Impact

The rapid growth of urbanisation and e-commerce has increased the number of home deliveries that need to be made in retail operations. Consequently, there is also an increase in unexpected incidents, such as adverse traffic, unavailability of parking space, and vehicle breakdowns. These disruptions result in delays, higher costs, and lower service levels in the last-mile delivery operation. Motivated by free, innovative, and efficient tools, such as the Google application programming interface (API) and Google OR, we built a model to measure the impact of disruptions in the last-mile delivery operation. Our model considers customers’ geographic information, speed estimation between nodes, routing optimisation, and disruption evaluation. Disruptions are considered here as external factors such as accidents and road works that imply the closure of or slow access to certain roads. Computational experiments, based on a set of real data from three different cities around the world, which contrast in size and characteristics (i.e., Boston, US; Bogotá, Colombia; and Pamplona, Spain), were conducted to validate our approach. The tests consider 50 different instances of up to 100 customers per city and analyse the impact of disruptions in terms of travelled time and distance. Our results provide managerial insights for key stakeholders (i.e., carriers, consumers, and government) to define policies and development plans that improve the resilience and capabilities of cities’ transportation systems.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals A comparison of Galactic electron density models using PyGEDM

2021 ◽  
Vol 38 ◽  
Author(s):  
D. C. Price ◽  
C. Flynn ◽  
A. Deller
Keyword(s):  
Electron Density ◽  
Large Scale ◽  
Galactic Halo ◽  
Application Programming Interface ◽  
Dispersion Measure ◽  
Radio Pulsars ◽  
Distribution Models ◽  
Distance Measurements ◽  
Application Programming ◽  
The Impact

Abstract Galactic electron density distribution models are crucial tools for estimating the impact of the ionised interstellar medium on the impulsive signals from radio pulsars and fast radio bursts. The two prevailing Galactic electron density models (GEDMs) are YMW16 (Yao et al. 2017, ApJ, 835, 29) and NE2001 (Cordes & Lazio 2002, arXiv e-prints, pp astro–ph/0207156). Here, we introduce a software package PyGEDM which provides a unified application programming interface for these models and the YT20 (Yamasaki & Totani 2020, ApJ, 888, 105) model of the Galactic halo. We use PyGEDM to compute all-sky maps of Galactic dispersion measure (DM) for YMW16 and NE2001 and compare the large-scale differences between the two. In general, YMW16 predicts higher DM values towards the Galactic anticentre. YMW16 predicts higher DMs at low Galactic latitudes, but NE2001 predicts higher DMs in most other directions. We identify lines of sight for which the models are most discrepant, using pulsars with independent distance measurements. YMW16 performs better on average than NE2001, but both models show significant outliers. We suggest that future campaigns to determine pulsar distances should focus on targets where the models show large discrepancies, so future models can use those measurements to better estimate distances along those line of sight. We also suggest that the Galactic halo should be considered as a component in future GEDMs, to avoid overestimating the Galactic DM contribution for extragalactic sources such as FRBs.

scholarly journals Comparing protein structures with RINspector automation in Cytoscape

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 563 ◽  
Author(s):  
Guillaume Brysbaert ◽  
Théo Mauri ◽  
Marc F. Lensink
Keyword(s):  
Protein Structures ◽  
Application Programming Interface ◽  
Easy Access ◽  
Network Centrality ◽  
Backbone Flexibility ◽  
Residue Interaction ◽  
Application Programming ◽  
Quaternary Structures ◽  
The Impact ◽  
Programming Interface

Residue interaction networks (RINs) have been shown to be relevant representations of the tertiary or quaternary structures of proteins, in particular thanks to network centrality analyses. We recently developed the RINspector 1.0.0 Cytoscape app, which couples centrality analyses with backbone flexibility predictions. This combined approach permits the identification of crucial residues for the folding or function of the protein that can constitute good targets for mutagenesis experiments. Here we present an application programming interface (API) for RINspector 1.1.0 that enables interplay between Cytoscape, RINspector and external languages, such as R or Python. This API provides easy access to batch centrality calculations and flexibility predictions, and allows for the easy comparison of results between different structures. These comparisons can lead to the identification of specific and conserved central residues, and show the impact of mutations to these and other residues on the flexibility of the proteins. We give two use cases to demonstrate the interest of these functionalities and provide the corresponding scripts: the first concerns NMR conformers, the second focuses on mutations in a structure.

Stochastic Modeling of the Last Mile Problem for Delivery Fleet Planning

2017 ◽  
Author(s):  
Jay R. Brown ◽  
Alfred L. Guiffrida
Keyword(s):  
Stochastic Representation ◽  
Service Levels ◽  
Fleet Size ◽  
Fleet Planning ◽  
Last Mile ◽  
Payload Capacity ◽  
Transportation Capacity ◽  
Last Mile Delivery ◽  
Alternative Delivery ◽  
Last Mile Problem

This paper presents a stochastic representation of the last mile problem that quantifies expected maintenance, regular labor, overtime labor, fuel, and carbon emission costs resulting from different delivery fleet options. The last mile delivery fleet planning model presented herein can be used in a decision framework to evaluate alternative delivery strategies involving fleet size and delivery frequency with information regarding cost, carbon emissions, service levels for available delivery hours, and payload capacity, as well as the transportation capacity needed to meet customer demand and lends itself well to performing what-if analyses.

"Agent-based simulation of restaurant deliveries facilitating cargo-bikes and urban consolidation"

2018 ◽  
Author(s):  
Christian Fikar ◽  
Manfred Gronalt
Keyword(s):  
Urban Areas ◽  
Optimization Procedure ◽  
Simulation Models ◽  
Food Delivery ◽  
Agent Based ◽  
Last Mile ◽  
Last Mile Delivery ◽  
Cargo Bike ◽  
The Impact ◽  
Urban Consolidation

"Last-mile distribution in urban areas is challenged by congestion and restriction for motorized traffic. To support operations, this work investigate the impact of operating urban consolidation points and facilitating cargo-bikes for urban last-mile distribution. Motivated by sample setting originating from the food delivery industry, a decision support system combining agentbased simulation with heuristic optimization procedure is developed. It considers a logistics provider who performs the last-mile delivery for multiple competing restaurants in an urban area. Therefore, both demand and the availability of cargo-bikes, which are operated by freelancers, are subject to randomness. Computational experiments investigate the impact of the available amount of cargo-bike drivers as well as the number of operated consolidation points, highlighting the importance of facilitating simulation models to support operations in highly dynamic and uncertain settings."

scholarly journals Comparing protein structures with RINspector automation in Cytoscape

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 563 ◽  
Author(s):  
Guillaume Brysbaert ◽  
Théo Mauri ◽  
Marc F. Lensink
Keyword(s):  
Protein Structures ◽  
Application Programming Interface ◽  
Easy Access ◽  
Network Centrality ◽  
Backbone Flexibility ◽  
Residue Interaction ◽  
Application Programming ◽  
Quaternary Structures ◽  
The Impact ◽  
Programming Interface

Residue interaction networks (RINs) have been shown to be relevant representations of the tertiary or quaternary structures of proteins, in particular thanks to network centrality analyses. We recently developed the RINspector Cytoscape app, which couples centrality analyses with backbone flexibility predictions. This combined approach permits the identification of crucial residues for the folding or function of the protein that can constitute good targets for mutagenesis experiments. Here we present an application programming interface (API) for RINspector that enables interplay between Cytoscape, RINspector and external languages, such as R or Python. This API provides easy access to batch centrality calculations and flexibility predictions, and allows for the easy comparison of results between different structures. These comparisons can lead to the identification of specific and conserved central residues, and show the impact of mutations to these and other residues on the flexibility of the proteins. We give two use cases to demonstrate the interest of these functionalities and provide the corresponding scripts: the first concerns NMR conformers, the second focuses on mutations in a structure.

A new model for Last-Mile Delivery and Satellite Depots management: The impact of the on-demand economy

2021 ◽  
Vol 145 ◽  
pp. 102184
Author(s):  
Guido Perboli ◽  
Luce Brotcorne ◽  
Maria Elena Bruni ◽  
Mariangela Rosano
Keyword(s):  
New Model ◽  
On Demand ◽  
Last Mile ◽  
Last Mile Delivery ◽  
The Impact

scholarly journals Reddit discussions about buprenorphine associated precipitated withdrawal in the era of fentanyl

2021 ◽  
Author(s):  
Anthony Spadaro ◽  
Abeed Sarker ◽  
Whitney Hogg-Bremmer ◽  
Jennifer S Love ◽  
Nicole O'Donnell ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Application Programming Interface ◽  
The United States ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
Induction Strategy ◽  
Application Programming ◽  
The Impact ◽  
Media Data

Background: Buprenorphine is an evidence-based treatment for Opioid Use Disorder (OUD). Standard buprenorphine induction requires a period of opioid abstinence to minimize risk of precipitated opioid withdrawal (POW). Our objective was to study the impact of the increasing presence of fentanyl and its analogs in the opioid supply of the United States, on buprenorphine induction and POW, using social media data from Reddit. Methods: This is a data-driven, mixed methods study of opioid-related forums, called subreddits, on Reddit to analyze posts related to fentanyl, POW, and buprenorphine induction. The posts were collected from seven subreddits using an application programming interface for Reddit. We applied natural language processing to identify subsets of salient posts relevant to buprenorphine induction, and performed manual, qualitative, thematic analyses of them. Results: 267,136 posts were retrieved from seven subreddits. Fentanyl mentions increased from 3 in 2013 to 3870 in 2020, and POW mentions increased from 2 (2012) to 332 (2020). Manual review of 384 POW-mentioning posts and 106 'Bernese method' (a microdosing induction strategy) mentioning posts revealed common themes and peoples' experiences. Specifically, presence of fentanyl caused POWs despite long abstinence durations, and alternative induction via microdosing were frequently recommended in peer-to-peer discussions. Conclusions: This study found that increased social media chatter on Reddit about POW correlated with fentanyl mentions. A subset of posts described microdosing as a self-management strategy to avoid POW. Reddit posts suggest that people are utilizing these strategies to initiate buprenorphine due to challenges arising from fentanyl prevalence in the opioid supply.

scholarly journals Sentiment Analysis on Overseas Tweets on the Impact of COVID-19 in Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 304-313
Author(s):  
Tigor Nirman Simanjuntak ◽  
Setia Pramana
Keyword(s):  
Big Data ◽  
Application Programming Interface ◽  
Text Format ◽  
Python Programming Language ◽  
Application Programming ◽  
Python Programming ◽  
Negative Sentiment ◽  
Analytical Tools ◽  
The Impact ◽  
Programming Interface

This study aims to conduct analysis to determine the trend of sentiment on tweets about Covid-19 in Indonesia from the Twitter accounts overseas on big data perspective. The data was obtained from Twitter in the period of April 2020, with the word query "Indonesian Corona Virus" from foreign user accounts in English. The process of retrieving data comes from Twitter tweets by crawling the text using Twitter's API (Application Programming Interface) by employing Python programming language. Twitter was chosen because it is very fast and easy to spread through status updates from and among the user accounts. The number of tweets obtained was 8,740 in text format, with a total engagement of 217,316. The data was sorted from the tweets with the largest to smallest engagement, then cleaned from unnecessary fonts and symbols as well as typo words and abbreviations. The sentiment classification was carried out by analytical tools, extracting information with text mining, into positive, negative, and neutral polarity. To sharpen the analysis, the cleaned data was selected only with the largest engagement until those with 100 engagements; then was grouped into 30 sub-topics to be analyzed. The interesting facts are found that most tweets and sub-topics were dominated by the negative sentiment; and some unthinkable sub-topics were talked by many users.

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