2000 character limit reached
Deep Reinforcement Learning for Picker Routing Problem in Warehousing
Published 5 Feb 2024 in cs.LG and cs.AI | (2402.03525v1)
Abstract: Order Picker Routing is a critical issue in Warehouse Operations Management. Due to the complexity of the problem and the need for quick solutions, suboptimal algorithms are frequently employed in practice. However, Reinforcement Learning offers an appealing alternative to traditional heuristics, potentially outperforming existing methods in terms of speed and accuracy. We introduce an attention based neural network for modeling picker tours, which is trained using Reinforcement Learning. Our method is evaluated against existing heuristics across a range of problem parameters to demonstrate its efficacy. A key advantage of our proposed method is its ability to offer an option to reduce the perceived complexity of routes.
- Layer normalization. arXiv preprint arXiv:1607.06450.
- Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
- Deep reinforcement learning for a multi-objective online order batching problem. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 32, pages 435–443.
- Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940.
- Solving the order batching and sequencing problem using deep reinforcement learning. arXiv preprint arXiv:2006.09507.
- An evaluation of picking routing policies to improve warehouse efficiency. International Journal of Industrial Engineering and Management, 8(4):229.
- Design and control of warehouse order picking: A literature review. European journal of operational research, 182(2):481–501.
- Learning heuristics for the tsp by policy gradient. In International conference on the integration of constraint programming, artificial intelligence, and operations research, pages 170–181. Springer.
- Order-picking methods: improving order-picking efficiency. International Journal of Logistics Systems and Management, 3(4):451–460.
- Hall, R. W. (1993). Distance approximations for routing manual pickers in a warehouse. IIE transactions, 25(4):76–87.
- Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
- Tabu search heuristics for the order batching problem in manual order picking systems. European Journal of Operational Research, 222(3):484–494.
- Deep reinforcement learning based agvs real-time scheduling with mixed rule for flexible shop floor in industry 4.0. Computers & Industrial Engineering, 149:106749.
- Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Attention, learn to solve routing problems! arXiv preprint arXiv:1803.08475.
- Routing orderpickers in a warehouse: a comparison between optimal and heuristic solutions. IIE transactions, 30(5):469–480.
- Task selection by autonomous mobile robots in a warehouse using deep reinforcement learning. In 2019 Winter Simulation Conference (WSC), pages 680–689. IEEE.
- An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research, 248(1):107–122.
- Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning. arXiv preprint arXiv:1911.04936.
- Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612.
- Reinforcement learning for combinatorial optimization: A survey. Computers & Operations Research, 134:105400.
- Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems, 31.
- A comparative analysis of several asymmetric traveling salesman problem formulations. Computers & Operations Research, 36(3):637–654.
- Exact algorithms for the order picking problem. Computers & Operations Research, 100:117–127.
- Combinatorial optimization: algorithms and complexity. Courier Corporation.
- Petersen, C. (1995). Routeing and storage policy interaction in order picking operations. In Decision Sciences Institute Proceedings, volume 3, pages 1614–1616.
- Petersen, C. G. (1997). An evaluation of order picking routeing policies. International Journal of Operations & Production Management.
- An evaluation of routing and volume-based storage policies in an order picking operation. Decision Sciences, 30(2):481–501.
- Wave order picking under the mixed-shelves storage strategy: A solution method and advantages. Computers & Operations Research, 137:105556.
- Order-picking in a rectangular warehouse: a solvable case of the traveling salesman problem. Operations research, 31(3):507–521.
- Routing methods for warehouses with multiple cross aisles. International Journal of Production Research - INT J PROD RES, 39:1865–1883.
- Routing order pickers in a warehouse with a middle aisle. European Journal of Operational Research, 133(1):32–43.
- A new mathematical programming formulation for the single-picker routing problem. European Journal of Operational Research, 253(1):68–84.
- Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27.
- Facilities planning. John Wiley & Sons.
- Attention is all you need. Advances in neural information processing systems, 30.
- Graph attention networks. arXiv preprint arXiv:1710.10903.
- Pointer networks. Advances in neural information processing systems, 28.
- Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8(3):229–256.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.