Creating a genetic algorithm for third-party logistics’ warehouse delivery scheduling via a large language model

Purpose The purpose of this paper is to present the design and implementation of a genetic algorithm (GA), using a large language model (LLM) for optimizing the delivery scheduling process in warehouses of third-party logistics (3PL) companies, within the context of a simplified case study, and to highlight the main directions for implementing this methodology in business realities. Design/methodology/approach Using a simplified case study of an international 3PL company, this study applies a GA developed in RStudio by LLM to generate test scenarios and input data. The GA was optimized to minimize the time and distance of movement in the process of preparing goods for shipment, demonstrating its effectiveness in improving warehouse delivery scheduling. Findings The study confirms that the GA, supported by LLM, significantly improves the delivery planning process in the warehouse. Specifically, the implementation of the GA led to notable improvements in scheduling efficiency and a reduction in the distance traveled within the warehouse. These enhancements enable more efficient generation, evaluation and optimization of logistic scenarios. Additionally, the use of LLM greatly facilitates the creation and refinement of complex algorithms like GA, through automation and innovative approaches in logistics. Research limitations/implications The study highlights limitations related to data quality, the dynamic nature of logistic operations, computational complexity and the need for generalization of results. It also points out the lack of research in business realities that demonstrate the effectiveness of combining the benefits of LLM and GA in practice. Originality/value This paper makes a significant contribution to the literature by demonstrating the capabilities of advanced technologies such as GA and LLM in 3PL logistics. It presents an innovative approach to optimizing logistic processes, offering perspectives for further innovations and automation in supply chain management. It also indicates new opportunities for 3PL companies in terms of improving operational and cost efficiency, emphasizing the importance of continuously seeking innovative solutions in the face of increasing market demands.