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Heuristic algorithm for path planning in high-density automated storage and retrieval system raw material box reorganization.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
3 authors.
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Abstract
The automated storage system effectively reduces storage costs, making it a key management strategy adopted by various companies. High-density automated storage systems are more complex, and optimizing the system's efficiency by minimizing the number of raw material box (RMB) movements has been proven to be an NP-hard problem. In a robot-based compact storage and retrieval system (RCSRS), RMBs are neatly stored in vertical stacks. Automatic guided vehicles (AGVs) move along the top of the storage system and retrieve RMBs vertically. Besides retrieving the target box, AGVs must also reorganize obstructing boxes stacked above it. As a result, RCSRS requires substantial RMB reorganization, which accounts for a large portion of storage operation time. To address the AGVs box reorganization path planning problem in AutoStore, a high-density robotic storage system, this study formulates a mathematical model to optimize the AGV's reorganization path. Given the number and locations of target boxes, the model aims to minimize the total operating time for AGVs during block reshuffling in AutoStore. Additionally, a heuristic algorithm is developed to optimize the complete order processing workflow, reducing AutoStore's overall operational time and improving system efficiency.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.