Optimal management of equipments of the BMT Containers Terminal (Bejaia’s Harbor)

Abstract

The BMT (Bejaia Mediterranean Terminal) Company of Bejaia’s harbor became aware that the performances of the terminal with containers is measured by the time of stopover, the speed of the operations, the quality of the service and the cost of container’s transit. For this end, the company has devoted several studies to analyze the performance of its terminal: elaboration of a global model for the “loaded / unloaded” process, modeling of the system by another approach which consists of the decomposition of the system into four independent subsystems (namely: the “loading” process, the “unloading” process, the “full-stock” process and the “empty-stock” process - (see Aïssani et al, 2009; Aïssani et al, 2009). The models used in this last study describe in detail the comportment of the real systems and the obtained results given by the simulators corresponding to each model are approximately the same as the real values. It is the reason for which the company wants to exploit these models in order to determine an optimal management of its equipments.

Indeed, this work consists, more specifically, in determining the optimal number of trucks to be used in each process that minimizes the waiting time of trucks and GQ (Gantry of Quay). This is a multi-objectives optimization problem, exactly a stochastic bi-objectives optimization problem. For that, we have modeled the problem by an open network which is the most suitable for this situation. After the identification of the process parameters, we conclude that the model is an open network of unspecified queues (G ^[X]/G/1, M/G/1, G/G/N/0, ...). In the literature, there is no exact method for analyzing this kind of networks. For this, we have established a simulation model that can imitate the functioning of each system.

The simulations allowed us to evaluate the performances of the park with containers according to the number of the trucks used, on the basis of current conditions and in the case of variation of the flow of arrivals of ships and the service rate of the trucks. This allowed us to determine the optimal number of trucks to be used in the loading process and unloading process. We have also determined the performance of the stock, on basis of current conditions.