The logistics management in the sizing of the fleet of containers per ships in dedicated route - The use of computer simulation: A Brazilian shipping company case

Main Article Content

Delmo Alves de Moura
Rui Carlos Botter
João Ferreira Netto


The aim of this paper is provide the use of the simulation in the  to manage one important point in the logistics systems to shipping companies that is the imbalance of containers, movement of empty containers from surplus ports to deficit ports.

From a survey of data from a shipping company operating in Brazil, at various ports, it was possible to model and simulate the needs in six major domestic ports of empty and full containers and seek to meet demand in the shipping market, reducing storage of containers and maintaining the level of excellence in service.

Based on the discrete event simulation it was possible to analyze the problem of empty and full containers at the ports in the maritime transportation system. It was possible study the imbalance situation in the ports e provide one tool the companies to manage yours service.

The data are confined to one company located in São Paulo and operating in Brazil at maritime transportation.

The research shows that the imbalance problem between full and empty containers is a real case to all companies in the maritime transportation and can have effective solutions using discrete event simulation.

To have excellent supply chain management it is important to have also one effective transportation system. This paper contributes to research in the inbound and outbound part of the supply chain management.

Article Details

Author Biographies

Delmo Alves de Moura, Federal University of ABC

Professor at  Federal University of ABC - Industrial Engineering

Rui Carlos Botter, University of São Paulo

Full Professor at the Polytechnic School, Department of Naval and Oceanic Engineering at University of São Paulo, Brazil.

João Ferreira Netto, Innovation Center for Logistics and Ports Infrastructure - CILIP - USP.

He also researches modeling techniques of stochastic systems


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