Back
 JAMP  Vol.6 No.12 , December 2018
Pod Layout Problem in Kiva Mobile Fulfillment System Using Synchronized Zoning
Abstract: This article studies the pod layout problem in the Kiva mobile fulfillment system which adopts the synchronized zoning strategy. An integer programming model for the pod layout problem is formulated under the premise of knowing the relationship of the pods and items. A three-stage algorithm is proposed based on the Spectral Clustering algorithm. Firstly, the pod similarity matrix and the Laplacian matrix are constructed according to the relationship of the pods and items. Secondly, the pods are clustered by the Spectral Clustering algorithm and assigned to each zone based on the cluster results. Finally, the exact locations of pods in each zone are determined by the historical retrieval frequency of items, using the real data of a large-scale Kiva mobile fulfillment system to simulate and calculate the order picking efficiency before and after the adjustment of the pod layout. The results showed that the pod layout using synchronized zoning strategy can effectively improve the picking efficiency.
Cite this paper: Guan, M. and Li, Z. (2018) Pod Layout Problem in Kiva Mobile Fulfillment System Using Synchronized Zoning. Journal of Applied Mathematics and Physics, 6, 2553-2562. doi: 10.4236/jamp.2018.612213.
References

[1]   Wurman, P.R., D’Andrea, R. and Mountz, M. (2008) Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses. AI Magazine, 29, 9-20.

[2]   Wu, Y., Meng, X., Wang, Y. and Hu, J. (2016) Order Sequence Optimization for “Part-to-Picker” Order Picking System. Journal of Mechanical Engineering, 52, 206-212.
https://www.scopus.com/inward/record.uri?eid=2-s2.0-4962651709&partnerID=40&md5=55071d82e8f39afe5f05270f98f2ae4f

[3]   Han, M.M., Liu, J., Wu, S. and Wang, J.T. (2017) Path Planning of Mobile Robot Based on Particle Swarm Optimization Algorithm. Journal of Computer Applications, 37, 2258-2263.

[4]   Dong, J. and Xin, X. (2017) Multi-Robot Dynamic Task Allocation Algorithm Based on Pareto Improvement. Journal of Computer Applications, 37, 3620-3624.

[5]   Shen, B., Ningbo, Y.U. and Liu, J. (2014) Intelligent Scheduling and Path Planning of Warehouse Mobile Robots. CAAI Transactions on Intelligent Systems, 9, 659-664.

[6]   Yan, X., Zhang, C. and Qi, M. (2017) Multi-AGVs Collision-Avoidance and Deadlock-Control for Item-to-Human Automated Warehouse. 2017 International Conference on Industrial Engineering, Management Science and Application (ICIMSA), Seoul, 13-15 June 2017, 1-5.

[7]   Li, Z.P., Zhang, J.L., Zhang, H.J. and Hua, G.W. (2017) Optimal Selection of Movable Shelves under Cargo-to-Person Picking Mode. International Journal of Simulation Modelling (IJSIMM), 16, 145-156.
https://doi.org/10.2507/IJSIMM16(1)CO2

[8]   de Koster, R., Le-Duc, T. and Roodbergen, K.J. (2007) Design and Control of Warehouse Order Picking: A Literature Review. European Journal of Operational Research, 182, 481-501.
https://doi.org/10.1016/j.ejor.2006.07.009

[9]   Guan, T. and Yang, T. (2014) Analysis of General Model and Classical Algorithms for Spectral Clustering. Pattern Recognition & Artificial Intelligence, 27, 1015-1025.

[10]   Arias-Castro, E., Lerman, G. and Zhang, T. (2017) Spectral Clustering Based on Local PCA. The Journal of Machine Learning Research, 18, 253-309.

[11]   DeFord, D.R. and Pauls, S.D. (2017) Spectral Clustering Methods for Multiplex Networks. arXiv:1703.05355.

[12]   Chen, X.j., Peng, S., Huang, J.Z., Nie, F.P. and Ming, Y. (2017) Local PurTree Spectral Clustering for Massive Customer Transaction Data. IEEE Intelligent Systems, 32, 37-44.
https://doi.org/10.1109/MIS.2017.41

[13]   Jiang, C., Xie, H. and Bai, Z. (2017) Robust and Efficient Computation of Eigenvectors in a Generalized Spectral Method for Constrained Clustering. In: Singh, A. and Zhu, J., Eds., Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, 757-766.

[14]   Han, X., Yao, H. and Zhong, G. (2017) Handwritten Text Line Segmentation by Spectral Clustering. Eighth International Conference on Graphic and Image Processing (ICGIP 2016), International Society for Optics and Photonics, 102251A.

[15]   Dhillon, I.S., Guan, Y. and Kulis, B. (2004) A Unified View of Kernel K-Means, Spectral Clustering and Graph Cuts. Citeseer.

[16]   Shi, J. and Malik, J. (2005) Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22, 888-905.

 
 
Top