TY - GEN
T1 - Reaction time optimization based on sensor data-driven simulation for snow removal projects
AU - Jafari, Parinaz
AU - Mohamed, Emad
AU - Ali, Mostafa
AU - Francis Siu, Ming Fung
AU - Abourizk, Simaan
AU - Jewkes, Lance
AU - Wales, Rod
N1 - Funding Information:
This research work is funded by a Collaborative Research and Development Grant (CRDPJ 492657) from the National Science and Engineering Research Council and by the Ledcor Group. The authors would also like to thank Dr. Catherine Pretzlaw for her assistance with manuscript editing.
Publisher Copyright:
© 2018 ASCE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - Reaction time of a snow removal project, which is defined as the duration between the time that snow begins accumulating at a road section and the time that snow is plowed, is a project performance indicator that can be used to evaluate the effectiveness of truck allocation strategies. While sensors, such as truck GPS (global positioning system) and weather RWIS (road weather information system), which track working hours and weather conditions, respectively, are used to collect large amounts of data, these data are not fully utilized to optimize reaction times of snow removal projects. In this research, the relationship between truck performance and weather information was analyzed. Sensor data were extracted, clustered, and refined; stochastic truck travelling speed and stochastic plowing speed were then mined and associated with the weather conditions of corresponding road sections. A data-driven, simulation-based optimization approach, which uses this mined data as input, was also developed to minimize reaction time. A practical case study of a project in Alberta, Canada, was conducted to validate and demonstrate the functionality of the proposed approach, which was simulated and optimized using the in-house simulation software, Simphony.NET. The resultant model allows project managers to predict the impact various truck allocation strategies on project time and cost to ensure that maximum project reaction time is minimized.
AB - Reaction time of a snow removal project, which is defined as the duration between the time that snow begins accumulating at a road section and the time that snow is plowed, is a project performance indicator that can be used to evaluate the effectiveness of truck allocation strategies. While sensors, such as truck GPS (global positioning system) and weather RWIS (road weather information system), which track working hours and weather conditions, respectively, are used to collect large amounts of data, these data are not fully utilized to optimize reaction times of snow removal projects. In this research, the relationship between truck performance and weather information was analyzed. Sensor data were extracted, clustered, and refined; stochastic truck travelling speed and stochastic plowing speed were then mined and associated with the weather conditions of corresponding road sections. A data-driven, simulation-based optimization approach, which uses this mined data as input, was also developed to minimize reaction time. A practical case study of a project in Alberta, Canada, was conducted to validate and demonstrate the functionality of the proposed approach, which was simulated and optimized using the in-house simulation software, Simphony.NET. The resultant model allows project managers to predict the impact various truck allocation strategies on project time and cost to ensure that maximum project reaction time is minimized.
KW - Optimization
KW - Reaction time
KW - Sensor data
KW - Simulation
KW - Snow removal
UR - http://www.scopus.com/inward/record.url?scp=85048668551&partnerID=8YFLogxK
U2 - 10.1061/9780784481288.047
DO - 10.1061/9780784481288.047
M3 - Conference article published in proceeding or book
AN - SCOPUS:85048668551
T3 - Construction Research Congress 2018: Safety and Disaster Management - Selected Papers from the Construction Research Congress 2018
SP - 482
EP - 491
BT - Construction Research Congress 2018
A2 - Harper, Christofer
A2 - Lee, Yongcheol
A2 - Harris, Rebecca
A2 - Berryman, Charles
A2 - Wang, Chao
PB - American Society of Civil Engineers (ASCE)
T2 - Construction Research Congress 2018: Safety and Disaster Management, CRC 2018
Y2 - 2 April 2018 through 4 April 2018
ER -