Extended priority-based hybrid genetic algorithm for the less-than-container loading problem

• An extended priority-based hybrid genetic algorithm is developed for a less-than-container loading problem.
• Little research has been done to address the less-than-container problem.
• The deepest bottom left with fill method was applied to pack cargo into a container.
• This approach employed fuzzy logic controller (FLC) to improve efficiency.
• Numerical experiments have validated its practical viability.

The container loading problem has important industrial and commercial application in global logistics and supply chain management. In particular, small- and medium-sized enterprises may transport few items that cannot completely utilize the container space, called the less-than-container loading problem (LCL). To our best knowledge, few studies have addressed the present LCL problem. Furthermore, a freight forwarder in practice wants to optimize the space utilization in containers from different shippers, while avoiding the mixing of cargoes (boxes) from different customers to reduce the conflicts of loading and unloading goods in different ports. Focusing on realistic needs, this study aims to develop an extended priority-based hybrid genetic algorithm (EP-HGA) for the present LCL problem to determine the loading patterns. In particular, the proposed approach integrates the encoding based on cargo priority and cargo layer via the deepest-bottom-left fill method and the adaptive auto tuning parameters of the proposed EP-HGA to improve the efficiency and effectiveness. Numerical experiments were designed to compare the results of the proposed EP-HGA with conventional approaches for validation. The results have shown the practical viability of the proposed approach to effectively solve the LCL problem. This study concludes with a discussion of future research.