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In this study, we propose a novel approach for investigating optimizationperformance by flexible robot coordination in automated warehouses withmulti-agent reinforcement learning (MARL)-based control. Automated systemsusing robots are expected to achieve efficient operations compared with manualsystems in terms of overall optimization performance. However, the impact ofoverall optimization on performance remains unclear in most automated systemsdue to a lack of suitable control methods. Thus, we proposed a centralizedtraining-and-decentralized execution MARL framework as a practical overalloptimization control method. In the proposed framework, we also proposed asingle shared critic, trained with global states and rewards, applicable to acase in which heterogeneous agents make decisions asynchronously. Our proposedMARL framework was applied to the task selection of material handling equipmentthrough automated order picking simulation, and its performance was evaluatedto determine how far overall optimization outperforms partial optimization bycomparing it with other MARL frameworks and rule-based control methods.

The Impact of Overall Optimization on Warehouse Automation