Human–Robot Collaborative Order Picking in Smart Warehouses with Fuzzy Transportation and Processing Time
TL;DR
This work model the concerned problem with the objective of minimizing fuzzy makespan and design an adaptive large-neighborhood-based variable neighborhood descent algorithm to efficiently solve it, which adopts three-dimensional coupling encoding and multi-stage heuristic decoding mechanisms.
Abstract
Robot mobile fulfillment systems (RMFSs), as human–robot collaborative smart warehouses, transform the traditional person-to-goods order picking mode into a goods-to-person mode. Order picking optimization is a core decision-making challenge in RMFSs to improve the efficiency of the system, which needs to jointly optimize pod selection, robot scheduling, station assignment, and manual picking. Although recent studies have widely investigated integrated operational optimization in RMFSs, most of them rely on deterministic transportation and processing time and ignore uncertainties in practical human–robot collaborative operations. It remains challenging to jointly optimize these coupled decisions under uncertain operation times. To address this challenge, we model the concerned problem with the objective of minimizing fuzzy makespan and design an adaptive large-neighborhood-based variable neighborhood descent algorithm to efficiently solve it. The algorithm adopts three-dimensional coupling encoding and multi-stage heuristic decoding mechanisms. It further integrates a learning-based adaptive destroy operator selection method and a variable neighborhood descent search strategy to enhance its exploration and exploitation abilities. In a large number of systematic experiments, ALVND achieved great performance in solving the concerned problem. The objective function value obtained by it was 5.6–25.1% lower than its competitors, demonstrating its effectiveness in uncertain human–robot collaborative warehouse scenarios.