A Cloud Computing Model for Optimization of Transport Logistics Process

Zineb Benotmane 1 , Ghalem Belalem 1  and Abdelkader Neki 2
  • 1 Department of Computer Science, Faculty of Exact Sciences and Applied, University of Oran 1, Ahmed Ben Bella, , Oran, Algeria
  • 2 IUT de Cergy Pontoise QLIO Department, 95-97, rue Valère Collas 95100 Argenteuil. Paris, , Paris, France


In any increasing competitive environment and even in companies; we must adopt a good logistic chain management policy which is the main objective to increase the overall gain by maximizing profits and minimizing costs, including manufacturing costs such as: transaction, transport, storage, etc. In this paper, we propose a cloud platform of this chain logistic for decision support; in fact, this decision must be made to adopt new strategy for cost optimization, besides, the decision-maker must have knowledge on the consequences of this new strategy. Our proposed cloud computing platform has a multilayer structure; this later is contained from a set of web services to provide a link between applications using different technologies; to enable sending; and receiving data through protocols, which should be understandable by everyone. The chain logistic is a process-oriented business; it’s used to evaluate logistics process costs, to propose optimal solutions and to evaluate these solutions before their application. As a scenario, we have formulated the problem for the delivery process, and we have proposed a modified Bin-packing algorithm to improve vehicles loading.

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  • 1. Abduaziz, O., Cheng, J.K., Tahar R.M. and Varma, R. (2014) A hybrid Simulation model for Green Logistics Assessment in Automotive Industry. In: the 25th International Symposium on Intelligent Manufacturing and Automation, DAAAM, 2014. Vienna, Austria: Procedia Engineering, pp. 960-961.

  • 2. Bays, C. (1977) A comparison of next-fit, first-fit, and best-fit. Commun. ACM, Volume 20(3), pp. 191-192.

  • 3. Boukherroub, T., Ruiz, A., Guinet, .A and and Fondrevelle, J. (2015) An integrated approach for sustainable supply chain planning. Computers & Operations Research, 54, pp. 180-194.

  • 4. Carrera, S., Portmann, M.C. and Ramdane Cherif, W. (2010a) Scheduling problems for logistic platforms with fixed staircase component arrivals and various deliveries hypotheses. In: Lecture Notes in Management Science, Proceedings of the 2th International Conference on Applied Operational Research - ICAOR, Turku, Finlande, pp. 517-528.

  • 5. Carrera, S., Portmann, M.C. and Ramdane Cherif, W. (2010b) Scheduling supply chain node with fixed components arrivals and two partially flexible deliveries. In: 5th International Conference on Management and Control of Production and Logistics - MCPL 2010, Sep 2010, Coimbra, Portugal. IFAC Publisher, pp. 6, 2010 MCPL, Coimbra, Portugal.

  • 6. Chow, H.K.L., Choy, K.L. and Lee, W.B. (2007) A dynamic logistics process knowledge-based system - An RFID multi-agent approach. Knowledge-Based Systems, 20(4), pp. 357-372.

  • 7. Daniluk, D. and Holtkamp, B. (2014) Logistics Mall: A Cloud Platform for Logistics. Hompel, M., Rehof, J. and Wolf, O. Editors, Lecture Notes in Logistics, Springer, pp. 13-27.

  • 8. David, R., Gnimpieba, Z., Nait-Sidi-Moh, A., Durand, D. and Fortin, J. (2015) Vehicle routing problem with time-windows for perishable food delivery. In: The 12th International Conference on Mobile Systems and Pervasive Computing, Belfort, France.

  • 9. Guide méthodologique (2012), Information CO2 des prestations de transport. Application de l’article L. 1431-3 du code des transports. (In French)

  • 10. Holtkamp, B. (2014) The Logistics Mall: An IT-Architecture for Logistics-as-a-Product. Hompel, M., Rehof, J. and Wolf, O. Editors, Lecture Notes in Logistics, Springer, pp. 45-62.

  • 11. Korte, B. Vygen, J. (2006) Combinatorial Optimization: Theory and Algorithms. Algorithms and Combinatorics. Springer. pp. 426-441.

  • 12. Li, X., Wang, Y. and Chen, X. (2012) Cold chain logistics system based on cloud computing, Concurrency Computat. Practice and Experience, 24(17), pp. 2138-2150.

  • 13. Nettstrater, A., Geiben, T., Witthaut, M., Ebel, D. and Schoneboom, J. (2014) Logistics Software Systems and Functions: An Overview of ERP, WMS, TMS and SCM Systems. Hompel, M., Rehof, J. and Wolf, O. Editors, Lecture Notes in Logistics, Springer, pp.1-11.

  • 14. Neven Workgroupe (1989) Performance indicators in logistics. Technical report, IFS Publication, Springer-Verlag.

  • 15. Subramanian, N., Abdulrahman, M.D., and Zhou, X. (2014) Integration of logistics and cloud computing service providers: Cost and green benefits in the Chinese context. Transportation Research Part E: Logistics and Transportation Review, 70, pp. 86-98.


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