This study presents a route optimization framework for manual forklifts in warehouse environ- ments, utilizing regression-based travel time modeling alongside the ACOpt algorithm, which integrates Ant Colony Optimization (ACO) with K-opt techniques. Recognizing the significant impact of travel time on warehouse efficiency, a regression model was developed to accurately predict travel time based on key factors such as Euclidean distance, average height, and acceler- ation adjustments. This model refines route planning by enabling the selection of optimized paths for forklifts, with model predictions validated against actual travel times to confirm align- ment. Experimental results based on one month’s worth of Work Orders (WO) demonstrate that ACOpt surpasses traditional Greedy and LKH algorithms, achieving a higher rate of route improvements while maintaining reasonable computational times. These findings underscore the potential of the proposed framework to enhance operational efficiency and suggest scalability to multi-aisle systems for broader warehouse applications.
