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

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Języki publikacji
EN
Abstrakty
EN
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. Transportand logistics are part of the same activity chain that allowsgoods 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 concerningdeadlines, flexibility, and diversity of goods,while handling other risks associated with transport and logistics. The Bayesian approach, proposed inthis paper, covers all the steps necessary to implement decision support solutions for risk managementand control, starting from the identification of risks and the preparation of intervention to the conductingof various operations in crisis In this work, the predictionand the control of the road risks are conductedusing 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 modelthese risks and to prevent the various challenging situations from taking place in the logistics chain. Asa second step, we use the influence diagram as a tool for the decision-making procedure. Finally, a casestudy is presented to highlight the substantial contribution of this tool to controlling road risks whiletransporting goods.
Rocznik
Strony
239--252
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • Department of Mechanical Engineering Mohamed Chérif Messaadia University P.O. Box 1553, Souk-Ahras, 41000, Algeria
autor
  • Algerian Petroleum Institute School of Skikda Algeria
Bibliografia
  • [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, Detl of 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 bi-directional 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. Système d’information logistique et Transport. ag8030, Techniques de l’Ingénieur, 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 gérer les risquesliés à la chaîne logistique? Une réponse par les pratiques de SCRM (Supply Chain Risk Management). In XIXème Conférence Internationale de Management Stratégique, Luxembourg, 2–4 juin 2010.
  • [12] P. Naim, P.H. Wuillemin, P. Leray, O. Pourret, A. Becker. Réseaux bayésiens. 2nd ed. France: Eyrolles, 2004.
  • [13] M.E. Paté-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.Ö. 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. Andéol-Aussage. Transport routier de marchandises. Guide pour l’évaluation des risques professionnels. INRS, ED 6095, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f0caa528-1aab-468b-b3d1-b6fa0a6d293f
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