Collaborative order picking with multiple pickers and robots: Integrated approach for order batching, sequencing and picker-robot routing

With the growing demand for speed and flexibility in order fulfillment, it is crucial to employ an efficient picking system that allows faster delivery of items from warehouse storage to a depot. The use of autonomous mobile robots (AMRs) for intra-logistics can improve picking productivity, while alleviating the strain on human workers. In this research, a collaborative human–robot order-picking system (CHR-OPS) is considered, where humans perform item retrieval tasks and AMRs handle item transportation to the depot. The delivery performance of a CHR-OPS for a given set of orders and their due dates depend on three subproblems: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts system employing multiple pickers and AMRs. An optimization model is developed to jointly optimize the three problems with the objective of minimizing the total tardiness of all orders. A simulated annealing algorithm with adaptive neighborhood search and optimization-based restart strategy is proposed for handling large instances. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human–robot team composition, AMR speed, AMR capacity and warehouse layout.