Optimizing your warehouse means examining every corner of your infrastructure and every facet of your workflows and processes to identify and correct inefficiencies. Not only does warehouse optimization result in a healthier bottom line, but it also improves key warehouse metrics like accurate orders and on-time delivery.

Our solution for this optimization problem attempts to use Machine Learning techniques to provide a scalable and fast solution to this problem. We used several models like Decision tree, Random Forest Classifier, XGBoost classifier, k-Nearest Neighbours, etc to solve this issue.

The highest accuracy we obtained so far is 89% in Random Forest Classifier. The following graph shows our confusion matrix plot for RFC at various values of n_estimators

As you can see in the graph, the classification of item into -1th bin is where our error lies. When the bins are full and the algorithm is supposed to not place the item, our algorithm is placing it into a bin. That is where the algorithm is malperforming,

1. Amazon Bin Image Dataset

2. Tabularised Amazon Bin Image Dataset

3. CTGAN Sythetic Data

1. Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.

2. Optimizing Warehouse Operations with Machine Learning on GPUs, C. Seward

3. Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta,and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD.

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6. https://mlli.mit.edu/projects/machine-learning/predicting-warehouse-storage

7. https://machinelearningmastery.com/how-to-define-your-machine-learning-problem/

8. https://developer.nvidia.com/blog/optimizing-warehouse-operations-machine-learning-gpus/

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12. https://automl.github.io/auto-sklearn/master/