Real time task planning for order picking in intelligent logistics warehousing

With the rapid growth of e-commerce and ongoing innovations in the logistics industry, intelligent unmanned logistics warehousing systems have emerged to significantly enhance operational efficiency and reduce costs. In these systems, the two critical stages of order assignment and path planning are interconnected through racks in the order picking process. However, prior research has largely overlooked their joint optimization. In this paper, we investigate the real time task planning problem (RTTP) in intelligent unmanned logistics warehousing, where racks are dynamically assigned to orders arriving in real time, and robots are responsible for delivering the racks to workstations according to planned paths, with the goal of jointly minimizing total order processing time and travel costs. To solve the RTTP, we first design a joint optimization evaluation indicator and propose a joint optimization task planning (JOTP) algorithm. Furthermore, we innovatively introduce a reinforcement learning-based approach (JOTP-RL) by modeling order selection as a partially observable Markov decision process (POMDP), and employing the Q-Mix algorithm to solve it. To enhance path planning efficiency, we optimize the improved THA <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mrow></mml:mrow> <mml:mrow></mml:mrow> <mml:mo>∗</mml:mo></mml:mmultiscripts> </mml:math> algorithm by eliminating redundant calculations and accounting for congestion times. Finally, extensive experiments conducted on two datasets demonstrate that our proposed algorithms significantly outperform the baseline and state-of-the-art methods, achieving superior efficiency and effectiveness in both execution time and task optimization.