Task scheduling method for intelligent warehouse item sorting robot based on improved chimpanzee optimization algorithm

Abstract

Warehouse automation relies on accurate item sorting to boost production while cutting workforce needs. Intelligent warehouse item sorting robots have become indispensable for job scheduling, particularly for the best distribution and order of sorting jobs. Optimizing job scheduling, robot usage, and coordination at the centralised control system level can maximise warehouse sorting robots’ productivity by allocating tasks more effectively. The paper proposes an Enhanced Chimpanzee Optimization algorithm-based Task Scheduling Framework (ECOA-TSF) to meet warehouse automation requirements. The proposed method utilizes communication, grouping, and learning techniques for warehouse item sorting. The ECOA-TSF is a local search mechanism to improve job sequencing, allocation, and optimization of item sorting operations, making it more adept at exploitation and exploration. The efficacy of the ECOA-TSF proposed technique is evaluated via comprehensive simulation results compared to more conventional scheduling methods. Based on the findings, the ECOA-TSF outperforms the baseline techniques regarding work completion time, resource utilization, and overall effectiveness of the system. This work provides a trustworthy and efficient task-scheduling method for warehouse product sorting.