MATEC Web Conf.
Volume 183, 201812th International Conference Quality Production Improvement – QPI 2018
|Number of page(s)||8|
|Published online||31 July 2018|
Probabilistic Assessment of Road Risks for Improving Logistics Processes
Department of Mechanical Engineering, Mohamed Chérif Messaadia University, P.O. Box 1553, Souk-Ahras, 41000, Algeria. e-mail: email@example.com
2 Sonatrach, Algerian Petroleum Institute, School of Skikda, Algeria
* Corresponding author: firstname.lastname@example.org
The Intermodal transport represents a solution, which has proved its effectiveness, for the supply of the various logistic platforms. Road transport is also one of the means of transport used in the logistic function and is the most common. This type of transport is especially recommended for medium and short distance journeys. Transport is an important link in the logistical chain. Several constraints accompany this transport function such as: delays, flexibility, diversity of merchandise, and road risks. To identify this last problem of road risk and to minimize its influence, a Bayesian network has been developed in this paper. Through experts’ surveys and research in the literature, the various risks were identified. The structure of the Bayesian network is defined on the basis of this census. The network settings vary from one situation to another. The exploitation of statistics and historical files of the transport company has allowed to define the parameters (probabilities) given in the example studied in this paper. To prevent risks and anticipate failures in the logistics function, while optimizing a utility function, an influence diagram was used. This tool has provided the ability to control actions and make decisions safely. An example of merchandise transport between two port companies has shown promising results and better efficiency in the anticipation of actions.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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