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Congestion-Aware Warehouse Flow Analysis and Optimization

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Advances in Visual Computing (ISVC 2015)

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Abstract

Generating realistic configurations of urban models is a vital part of the modeling process, especially if these models are used for evaluation and analysis. In this work, we address the problem of assigning objects to their storage locations inside a warehouse which has a great impact on the quality of operations within a warehouse. Existing storage policies aim to improve the efficiency by minimizing travel time or by classifying the items based on some features. We go beyond existing methods as we analyze warehouse layout network in an attempt to understand the factors that affect traffic within the warehouse. We use simulated annealing based sampling to assign items to their storage locations while reducing traffic congestion and enhancing the speed of order picking processes. The proposed method enables a range of applications including efficient storage assignment, warehouse reliability evaluation and traffic congestion estimation.

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Acknowledgments

We thank the anonymous reviewers for their useful suggestions and Dong-Ming Yan for his valuable assistance in preparing the simulation framework. This work was partly supported by an Anita Borg Google PhD scholarship award.

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Correspondence to Sawsan AlHalawani .

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AlHalawani, S., Mitra, N.J. (2015). Congestion-Aware Warehouse Flow Analysis and Optimization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_66

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  • DOI: https://doi.org/10.1007/978-3-319-27863-6_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27862-9

  • Online ISBN: 978-3-319-27863-6

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