Simulation of multi-constraints cargo arrangement and optimization

Abstract

Efficient arrangement of cargo in logistics is crucial in minimizing the operational cost and it can be a complex task as it involves multiple constraints like cargo with various volumes and weights. Cargo arrangement is categorized as a problem that involves mathematical models and efficient optimization algorithms. In the mathematical models, the volume and weight of the vehicle container are used for calculations. The objectives of this research are to model a multi-constrain cargo optimization (MCCO) arrangement to achieve optimal solution using a computational optimization Genetic Algorithm (GA) using 3-dimensional bin packing problem and with different constraints parameters. There are 250 samples of cargoes with various combination of volume and weights have been designed for testing. By adding constraint parameters and adaptive fitness functions, the algorithm is more effective and feasible. The results show that the proposed algorithm can be used to solve 3D loading optimization problems with constraints and proposed better solution. The GA evolutionary result has proposed more than 75% space utilization with the best weight combination.