Optimization of the logistic function by controlling risks using influence diagram: cases of risks related to road transport

  • Abdelaziz Lakehal Mohamed Chérif Messaadia University
  • Fouad Tachi Algerian Petroleum Institute

Abstract

The growth in the number of logistics platforms served by road, rail, waterway, and sea is a logical consequence of the extensive and rapid development of merchandise trade in a globalized economy. Transport and logistics are part of the same activity chain that allows goods to be transported to their destination. Dependent on the requirements of their customers and suppliers and subject to strong competition, companies in this sector must manage challenges concerning deadlines, flexibility, and diversity of goods, while handling other risks associated with transport and logistics. The Bayesian approach, proposed in this paper, covers all the steps necessary to implement decision support solutions for risk management and control, starting from the identification of risks and the preparation of intervention to the conducting of various operations in crisis In this work, the prediction and the control of the road risks are conducted using the influence diagram method, whose final objective is the optimization of the logistics function. After identifying and analyzing the different risks, the Bayesian networks (BNs) are initially used to model these risks and to prevent the various challenging situations from taking place in the logistics chain. As a second step, we use the influence diagram as a tool for the decisionmaking procedure. Finally, a case study is presented to highlight the substantial contribution of this tool to controlling road risks while transporting goods.

Keywords

logistics, transport, control of road risks, Bayesian networks, influence diagram, decision making,

References

[1] T. Bedford, R.M. Cooke. Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press, New York, 2001.
[2] J.M. Bernardo, A.F.M. Smith. Bayesian Theory. John Wiley & Sons, 1994.
[3] Y. Biao, Y. Ying. Postponement in supply chain risk management: a complexity perspective. International Journal of Production Research, 48(7): 1901–1912, 2010.
[4] V.M. Bier, L.A. Cox. Probabilistic risk analysis for engineered systems. In: Advances in Decision Analysis - From Foundations to Applications, Ward Edwards, Ralph F. Miles, Detlof von Winterfeldt [Eds.], Cambridge, pp. 279–301, 2007.
[5] C. Caliendo, M.L. De Guglielmo. Quantitative risk analysis on the transport of dangerous goods through a bidirectional road tunnel. Risk Analysis, 37(1): 116–129, 2017.
[6] E. Castillo, Z. Grande, E. Mora, H.K. Lo, X. Xu. Complexity reduction and sensitivity analysis in road probabilistic safety assessment Bayesian network models. Computer-Aided Civil and Infrastructure Engineering, 32(7): 546–561, 2017.
[7] L. Dablanc, C. Ross. Atlanta: a mega logistics center in the Piedmont Atlantic Megaregion (PAM). Journal of Transport Geography, 24: 432–442, 2012.
[8] N. Fabbe-Costes. Syst`eme d’information logistique et Transport. ag8030, Techniques de l’Ing ́enieur, France, 1999.
[9] GeNIe. http://genie.sis.pitt.edu/about.html .
[10] A. Jarzemskis. Determination and evaluation of the factors of outsourcing logistics. Transport, 21(1): 44-47, 2006.
[11] O. Lavastre, A. Spalanzani. Comment g ́erer les risquesli ́es `a la chaˆıne logistique? Une r ́eponse par les ratiqu es de SCRM (Supply Chain Risk Management). In XIX`eme Conf ́erence Internationale de Management Strateique, Luxembourg, 2–4 juin 2010.
[12] P. Naim, P.H. Wuillemin, P. Leray, O. Pourret, A. Becker. R ́eseaux bay ́esiens. 2nd ed. France: Eyrolles, 2004.
[13] M.E. Pat ́e-Cornell. Uncertainties in risk analysis: six levels of treatment. Reliab. Eng. Syst. Safe, 54: 95–111, 1996.
[14] A. Qazi, J. Quigley, A. Dickson, S. ̈O. Ekici. Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies. European Journal of Operational Research, 259(1): 189–204, 2017.
[15] M. Savy. Le Transport de Marchandises. Eyrolles, France, 2007.
[16] K. Sedki, P. Polet, F. Vanderhaegen. Using the BCD model for risk analysis: An influence diagram based approach. Engineering Applications of Artificial Intelligence, 26: 2172–2183, 2013.
[17] U.B. Kjærulff, A.L. Madsen. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Springer, Science and Business Media, LLC, 2008.
[18] A.S. Valladeau, B. And ́eol-Aussage. Transport routier de marchandises. Guide pour l’ ́evaluation des risques professionnels. INRS, ED 6095, 2011.
Published
Jun 11, 2018
How to Cite
LAKEHAL, Abdelaziz; TACHI, Fouad. Optimization of the logistic function by controlling risks using influence diagram: cases of risks related to road transport. Computer Assisted Methods in Engineering and Science, [S.l.], v. 24, n. 4, p. 239–252, june 2018. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/195>. Date accessed: 26 jan. 2022. doi: http://dx.doi.org/10.24423/cames.195.
Section
Articles