Optimization of Storage and Retrieval Strategies in Warehousing Based on Enhanced Genetic Algorithm

Abstract

The Shuttle-Based Storage and Retrieval System (SBS/RS) faces challenges of low efficiency due to the constraints of single outbound or inbound operations. To overcome this limitation, a scheme enabling simultaneous outbound and inbound operations has been proposed. This involved developing a physical model that incorporates costs related to outbound cargo urgency, shelf stability, time, and warehouse busyness. The model was solved using a Genetic Algorithm with priority selection, adaptive operators, and a decay factor (GA-DF). Experimental results, validated across various environments, demonstrate that the proposed GA-DF algorithm achieves 50% higher efficiency compared to the IOSA algorithm when the shelf occupancy rate is 50% and multiple cost environments are considered. Additionally, GA-DF outperforms the Simulated Annealing algorithm, traditional Genetic Algorithm, and IOSA algorithm in optimizing storage and retrieval locations, significantly enhancing system optimization. This provides a crucial reference for optimizing such systems, particularly in dynamic and complex warehousing environments. The GA-DF algorithm’s applicability and advantages have been widely recognized through further validation, highlighting its potential to drive improvements in warehousing system efficiency and optimization strategies.

Keywords

• Shuttle-based storage and retrieval system
• physical model
• genetic algorithm