A multiagent simulator for supporting logistic decisions of unloading petroleum ships in habors

This work presents and evaluates the performance of a simulation model based on multiagent system technology in order to support logistic decisions in a harbor from oil supply chain. The main decisions are concerned to pier allocation, oil discharge, storage tanks management and refinery supply by a pipeline. The real elements as ships, piers, pipelines, and refineries are modeled as agents, and they negotiate by auctions to move oil in this system. The simulation results are compared with results obtained with an optimization mathematical model based on mixed integer linear programming (MILP). Both models are able to find optimal solutions or close to the optimal solution depending on the problem size. In problems with several elements, the multiagent model can find solutions in seconds, while the MILP model presents very high computational time to find the optimal solution. In some situations, the MILP model results in out of memory error. Test scenarios demonstrate the usefulness of the multiagent based simulator in supporting decision taken concerning the logistic in harbors.

[1]  D. Simchi-Levi.  Designing And Managing The Supply Chain , 2007 .

[2]  Marco Colombetti,et al.  Commitments for agent-based supply chain management , 2001, SECO.

[3]  Kun Chang Lee,et al.  MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties , 2007, Expert Syst. Appl..

[4]  Reid G. Smith,et al.  The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver , 1980, IEEE Transactions on Computers.

[5]  Mohammad Hossein Fazel Zarandi,et al.  A Fuzzy agent-based model for reduction of bullwhip effect in supply chain systems , 2008, Expert Syst. Appl..

[6]  Jomi Fred Hübner,et al.  Um modelo de reorganização de sistemas multiagentes. , 2003 .

[7]  Nicholas R. Jennings.  Agent-Oriented Software Engineering , 1999, MAAMAW.

[8]  Huaiqing Wang,et al.  On-demand e-supply chain integration: A multi-agent constraint-based approach , 2008, Expert Syst. Appl..

[9]  Flávio Neves,et al.  Um modelo de otimização da operação de terminais petrolíferos usando a teoria das restrições como pré-processamento , 2009 .

[10]  Nicolas Lhuillier,et al.  FOUNDATION FOR INTELLIGENT PHYSICAL AGENTS , 2003 .

[11]  Jaime Simão Sichman,et al.  SACI: Uma Ferramenta para Implementação e Monitaração da Comunicação entre Agentes , 2000, IBERAMIA-SBIA 2000 Open Discussion Track.

[12]  José M. Pinto,et al.  A general modeling framework for the operational planning of petroleum supply chains , 2004, Comput. Chem. Eng..