A Hybrid Genetic Algorithm–Tabu Search Method for The Storage Location Assignment Problem in Robotic Mobile Fulfillment Systems
DOI:
https://doi.org/10.23055/ijietap.2026.33.5.11911Abstract
With the advent of artificial intelligence, the robotic mobile fulfillment system (RMFS) has emerged as a novel automated warehousing system. Using a goods-to-person approach, RMFS employs robots to transport pods to workstations, where human pickers retrieve the required items, thereby significantly reducing walking distance. In addition, both material assignment to pods and pod storage location assignment play critical roles in improving picking efficiency. This study addresses these two decision problems through a two-stage solution framework under a simplified single-AGV, single-workstation RMFS. In the first stage, Apriori-based association analysis and DBSCAN clustering are employed to identify highly associated materials and assign them to pods. In the second stage, a hybrid genetic algorithm (GA) and tabu search (TS) algorithm is developed to optimize pod storage locations by minimizing the total round-trip picking distance required to fulfill orders. To verify the feasibility and effectiveness of the proposed approach, the developed algorithms were applied to one week of real production-order data from a Taiwanese computer equipment manufacturing company. Experimental results show that, compared with the company's current method, the proposed approach reduces the average total pod movements by approximately 47.12% and the average total picking distance by approximately 29.60% under the studied operational setting.
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