SIMULATION OF VESSEL BERTHING USING ARENA: CASE OF BEIRUT CONTAINER TERMINAL

The main objective of this research is to analyze the current situation of Beirut Container Terminal. The proposed methodology is to mimic current terminal operations using a simulation model using ARENA software in order to identify causes of queueing occurring at berth allocation. Field research was conducted and both qualitative and quantitate data were collected using interviews, on–site observations, and online vessel tracking. A base model is developed to simulate the current operations at Beirut Container Terminal. Then, three different feasible scenarios are proposed to minimize the total time spent by the vessel at the quay side. Proposed scenarios take into consideration physical and resources expansion subject to political and financial constraints. The aim of this research is to provide a tool for the decision maker at Beirut Container Terminal in formulating an investment strategy for future expansion.

A Comprehensive Modeling of the Discrete and Dynamic Problem of Berth Allocation in Maritime Terminals

In this study, the discrete and dynamic problem of berth allocation in maritime terminals, is investigated. The suggested resolution method relies on a paradigm of optimization with two techniques: heuristic and multi-agent. Indeed, a set of techniques such as the protocol of negotiation named contract net, the multi-agent interactions, and Worst-Fit arrangement technique, are involved. The main objective of the study is to propose a solution for attributing m parallel machines to a set of activities. The contribution of the study is to provide a detailed modeling of the discrete and dynamic berth allocation problem by establishing the corresponding models using a multi-agent methodology. A set of numerical experiments are detailed to prove the performance of the introduced multi-agent strategy compared with genetic algorithm and tabu search.

Berth Allocation Model for Container Terminal using Genetic Algorithm Technique: Case of Apapa Wharf, Lagos, Nigeria

The Nigeria ports plays a vital role in socio-economic growth by being a cheap mode of conveying shipments for importation and exportation. The number of vessels coming into the Nigerian ports every year is on the average of about 4,900. A well flourishing and efficient ports and cargo management will in no doubt put a developing economy such as Nigeria in a leading pedestal with developed nations. Thus, stakeholders in container terminals are concerned about discharging containers as fast as possible, with the purpose of saving terminal costs. This study is driven to minimize the time being used up by ships in container terminal using genetic algorithm (GA) and thus attain maximum efficiency. The limited berth space in the wharf lead to berth allocation problem (BAP) and an optimal solution is required. Moreover, high berth occupancy results in congestion where vessels are queuing to be served. This leads to high turn-around time and results in bad service for the container terminal. The aim of this study is to develop and implement a genetic algorithm based model for berth allocation (i.e. GAMBA) with the view to minimize the total delay times of vessels at container terminals. A study of the operations in Apapa wharf was done with the view to understand the berth allocation process vis-à-vis the challenges therein. The relevant parameters required for berth allocation were identified and GAMBA was developed using the identified parameters. GAMBA was implemented using real life data collected from the container terminal, Apapa, Lagos, Nigeria. The results showed that increasing the quay length by 250m has a very similar outcome on the container port’s efficiency as reducing the proportion of increasing handling time by 0.0025 h/m. This revealed that the outcome on the container port’s efficiency by increasing the quayside length was the same as reducing the proportion of increasing management time. Based on these results, the optimized allocation of container storage and the automation of the handling process can be proposed as cheaper alternatives to construction and development of the containers port in relation to increasing the productivity of the port.

A New Memetic Approach to Solve the Strategic Berth Template Problem

The berth allocation problem is one of the main concerns of port operators at a container terminal. In this paper, the authors study the berth allocation problem at the strategic level commonly known as the strategic berth template problem (SBTP). This problem aims to find the best berth template for a set of calling ships accepted to be served at the port. At strategic level, port operator can reject some ships to be served for avoid congestion. Since the computational complexity of the mathematical formulation proposed for SBTP, solution approaches presented so far for the problem are limited especially at level of large-scale instances. In order to find high quality solutions with a short computational time, this work proposes a population based memetic algorithm which combine a first-come-first-served (FCFS) technique, two genetics operators, and a simulating annealing algorithm. Different computational experiences and comparisons against the best known solutions so far have been presented to show the performance and effectiveness of the proposed method.