Penataan Muatan Truk Untuk Memenuhi Regulasi Muatan Sumbu Terberat dan Mencapai Efisiensi Pengiriman menggunakan Metode Genetik Algorithm

Purpose: This study aims to optimize truck cargo arrangements by considering maximum axle load regulations and delivery sequence requirements to improve loading and unloading efficiency. Methodology: This study employs an optimization approach with a multi-objective function that minimizes deviations from the prescribed axle load limits and the required cargo delivery sequence based on the Last In, First Out (LIFO) principle. The optimization model is solved using a genetic algorithm implemented in MATLAB. Algorithm performance is evaluated by testing different population sizes, crossover probabilities, and mutation probabilities to identify the parameter combination that yields the fastest solution search time. Results: The proposed program generates cargo arrangements that satisfy axle load regulations and delivery sequence requirements, achieving a load distribution fitness value of 1 and a delivery sequence fitness of 100%. Parameter testing identifies a population size of 40, a crossover probability of 0.7, and a mutation probability of 0.1 as the combination that produces the fastest solution search time among the tested configurations. Conclusion: The proposed approach enables truck cargo arrangements to account for axle load compliance and delivery sequence requirements simultaneously, supporting safer transportation and more efficient distribution operations. Originality: This study integrates regulatory axle load constraints and delivery sequence considerations into a single genetic algorithm-based cargo arrangement optimization model, providing a decision-making approach that addresses transportation safety and distribution efficiency concurrently.