Heterogeneous Multi-agent Routing Strategy for Robot-and-Picker-to-Good Order Fulfillment System
In this research heterogeneous multi-agent routing strategy for a robot-and-picker-to-good order fulfillment system based on collaboration between mobile robots and human workers is proposed. As it is intractable to solve all agents’ routing problems as a large global optimization problem, individual agent’s routing is planned separately based on utility-based heuristics. Both the robot and the worker routing problems are formulated as graph optimization problems based on the graph representation of robot’s task and dynamically induced subtasks of the worker. The total travel distance of the robot and the total waiting time criteria are optimized by genetic algorithm. The long-term performance of the proposed routing strategy is evaluated according to different indicators using hybrid event simulation. The results show that order fulfillment system with the proposed routing strategy can either extensively save manual labor or improve the system efficiency.
KeywordsHeterogeneous multi-agent system Routing strategy Order fulfillment system
This work is supported by the National Key R&D Program of China (Grant 2017YFB1303601); Natural Science Foundation of China (Grant 61773261 and 61573243); and the Innovation Action Plan of STCSM (Grant 16111106202).
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