Analysis of space optimization of three-dimensional container loading problem

Abstract

The container loading problem (CLP) has been studied for maximising container space utilization as a method of lowering costs and increasing supply chain efficiency. This paper presents an approach to the CLP, in which a container is to be filled with a selection of cargoes from an available set so that the volume utilization of the container is maximized. By minimizing the outer volume of all the cargoes placed, tight arrangement of the cargoes can be achieved. Genetic algorithm (GA) with adaptive chromosome length formation has been used to find the optimum solution for the arranged cargoes to be placed with minimum space utilization. Two experiments have been conducted, to simulate the space optimization with fixed and unfixed number of cargoes. Our findings show that the GA with adaptive chromosome length proposes better arrangements of cargoes to give optimum space utilization for the container with 93.4% fitness difference percentage than the first generation’s fitness value.