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Author Barrera, J.; Carrasco, R.A.; Moreno, E.
Title Real-time fleet management decision support system with security constraints Type
Year 2020 Publication TOP Abbreviated Journal TOP
Volume 28 Issue 3 Pages 728-748
Keywords Fleet management; Real-time control; Data analytics; GPS tracking; Decision support system; Conflict detection and resolution
Abstract 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.
Address [Barrera, Javiera; Carrasco, Rodrigo A.; Moreno, Eduardo] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: javiera.barrera@uai.cl;
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1134-5764 ISBN Medium
Area Expedition Conference
Notes WOS:000534967700001 Approved
Call Number UAI @ eduardo.moreno @ Serial 1200
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Author Rozas Andaur, J.M.; Ruz, G.A.; Goycoolea, M.
Title Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company Type
Year 2021 Publication Electronics Abbreviated Journal Electronics
Volume 10 Issue 22 Pages 2787
Keywords out of stock; machine learning; classification algorithms; imbalance data; supply chain management; decision support; retail industry application
Abstract For decades, Out-of-Stock (OOS) events have been a problem for retailers and manufacturers. In grocery retailing, an OOS event is used to characterize the condition in which customers do not find a certain commodity while attempting to buy it. This paper focuses on addressing this problem from a manufacturer’s perspective, conducting a case study in a retail packaged foods manufacturing company located in Latin America. We developed two machine learning based systems to detect OOS events automatically. The first is based on a single Random Forest classifier with balanced data, and the second is an ensemble of six different classification algorithms. We used transactional data from the manufacturer information system and physical audits. The novelty of this work is our use of new predictor variables of OOS events. The system was successfully implemented and tested in a retail packaged foods manufacturer company. By incorporating the new predictive variables in our Random Forest and Ensemble classifier, we were able to improve their system’s predictive power. In particular, the Random Forest classifier presented the best performance in a real-world setting, achieving a detection precision of 72% and identifying 68% of the total OOS events. Finally, the incorporation of our new predictor variables allowed us to improve the performance of the Random Forest by 0.24 points in the F-measure.
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2079-9292 ISBN Medium
Area Expedition Conference
Notes Approved
Call Number UAI @ alexi.delcanto @ Serial 1487
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