BERTH ALLOCATION MODEL FOR CONTAINER TERMINAL USING GENETIC ALGORITHM TECHNIQUE: CASE OF APAPA WHARF, LAGOS, NIGERIA

ABSTRACT:
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.

Keywords: Berth Allocation, Wharf, Quay, Container Terminal, Genetic Algorithm, Port, Productivity, Optimization, Nigeria.