TY - JOUR
T1 - Real-time fleet management decision support system with security constraints
AU - Barrera, Javiera
AU - Carrasco, Rodrigo A.
AU - Moreno, Eduardo
N1 - Publisher Copyright:
© 2020, Sociedad de Estadística e Investigación Operativa.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Intelligent transportation, and in particular, fleet management, has been a forefront concern for a plethora of industries. This statement is especially true for the production of commodities, where transportation represents a central element for operational continuity. Additionally, in many industries, and in particular those with hazardous environments, fleet control must satisfy a wide range of security restrictions to ensure that risks are kept at bay and accidents are minimum. Furthermore, in these environments, any decision support tool must cope with noisy and incomplete data and give recommendations every few minutes. In this work, a fast and efficient decision support tool is presented to help fleet managers oversee and control ore trucks, in a mining setting. The main objective of this system is to help managers avoid interactions between ore trucks and personnel buses, one of the most critical security constraints in our case study, keeping a minimum security distance between the two at all times. Furthermore, additional algorithms are developed and implemented, so that this approach can work with real-life noisy GPS data. Through the use of historical data, the performance of this decision support system is studied, validating that it works under the real-life conditions presented by the company. The experimental results show that the proposed approach improved truck and road utilization significantly while allowing the fleet manager to control the security distance required by their procedures.
AB - Intelligent transportation, and in particular, fleet management, has been a forefront concern for a plethora of industries. This statement is especially true for the production of commodities, where transportation represents a central element for operational continuity. Additionally, in many industries, and in particular those with hazardous environments, fleet control must satisfy a wide range of security restrictions to ensure that risks are kept at bay and accidents are minimum. Furthermore, in these environments, any decision support tool must cope with noisy and incomplete data and give recommendations every few minutes. In this work, a fast and efficient decision support tool is presented to help fleet managers oversee and control ore trucks, in a mining setting. The main objective of this system is to help managers avoid interactions between ore trucks and personnel buses, one of the most critical security constraints in our case study, keeping a minimum security distance between the two at all times. Furthermore, additional algorithms are developed and implemented, so that this approach can work with real-life noisy GPS data. Through the use of historical data, the performance of this decision support system is studied, validating that it works under the real-life conditions presented by the company. The experimental results show that the proposed approach improved truck and road utilization significantly while allowing the fleet manager to control the security distance required by their procedures.
KW - Conflict detection and resolution
KW - Data analytics
KW - Decision support system
KW - Fleet management
KW - GPS tracking
KW - Real-time control
UR - http://www.scopus.com/inward/record.url?scp=85085393726&partnerID=8YFLogxK
U2 - 10.1007/s11750-020-00565-y
DO - 10.1007/s11750-020-00565-y
M3 - Article
AN - SCOPUS:85085393726
SN - 1134-5764
VL - 28
SP - 728
EP - 748
JO - TOP
JF - TOP
IS - 3
ER -