Improve Warehouse Productivity using Order Batching with Python 📦

In a Distribution Center (DC), walking time from one location to another during picking route can account for 60% to 70% of the operator’s working time. Reducing this walking time is the most effective way to increase your DC overall productivity.

I have published a series of articles that propose an approach to design a model to simulate the impact of several picking processes and routing methods to find optimal order picking by using the Single Picker Routing Problem (SPRP) for a two-dimensional warehouse model (axis-x, axis-y).

SPRP is a specific application of the general Traveling Salesman Problem (TSP) answering the question:

“Given a list of storage locations and the distances between each pair of locations, what is the shortest possible route that visits each storage location and returns to the depot ?”

SPRP is used to determine the minimum route in the picking process to prepare one or several orders.

I have designed this Streamlit App to provide a tool to Logistics Engineers for testing these different strategies by only uplooading their own dataset of order line records.

Understand the theory behind 📜

• Improve Warehouse Productivity using Order Batching with Python - Medium Article
• Improve Warehouse Productivity using Spatial Clustering with Python Scipy - Medium Article
• Design Pathfinding Algorithm using Google AI to Improve Warehouse Productivity - Medium Article

Picking Route Optimization to reduce Walking Distance 🚶‍♂️

Initial Step: Prepare order lines datasets with Warehouse Layout Information

Based on your actual warehouse layout, storage locations are mapped with 2-D (x, y) coordinates that will be used to measure walking distance.

Every storage location must be linked to a Reference using Master Data. (For instance, reference #123129 is located in coordinate (xi, yi)). You can then associate every order line to a geographical location for picking.

Order lines can be extracted from your WMS Database, this table should be joined with the Master Data table to link every order line to a storage location and its (x, y) coordinate in your warehouse. Extra tables can be added to include more parameters in your model like (Destination, Delivery lead time, Special Packing, ..).

Experiment 1: What is the impact of wave picking in the total walking distance?

For more information and details about calculation: Medium Article

Problem Statement

For this study, we will use the example of E-Commerce type DC where items are stored in 4 level shelves. These shelves are organized in multiple rows (Row#: 1 … n) and aisles (Aisle#: A1 … A_n).

1. Items Dimensions: Small and light dimensions items
2. Picking Cart: lightweight picking cart with a capacity of 10 orders
3. Picking Route: Picking Route starts and ends at the same location

Scenario 1, the worst in terms of productivity, can be easily optimized because of

• Locations: Orders #1 and #2 have common picking locations
• Zones: orders have picking locations in a common zone
• Single-line Orders: items_picked/walking_distance efficiency is very low

Simulation

In the article I have built a set of functions needed to run different scenarios and simulate the pickers walking distance.

Function: Calculate distance between two picking locations

This function will be used to calculate the walking distance from a point i (xi, yi) and j (xj, yj).

Objective: return the shortest walking distance between the two potential routes from point i to point j.

Parameters

• y_low : lowest point of your alley (y-axis)
• y_high : highest point of your alley (y-axis)

Function: the Next Closest Location

This function will be used to choose the next location among several candidates to continue your picking route.

Objective: return the closest location as the best candidate

Function: Create your picking route and calculate the total walking distance

This function will be used to create your picking route from a set of orders to prepare.

• Input: a list of (x, y) locations based on items to be picked for this route
• Output: an ordered sequence of locations covered and total walking distance

Function: Create batches of n orders to be picked at the same time

• Input: order lines data frame (df_orderlines), number of orders per wave (orders_number)
• Output: data frame mapped with wave number (Column: WaveID), the total number of waves (waves_number)

Function: listing picking locations of wave_ID picking route

• Input: order lines data frame (df_orderlines) and wave number (waveID)
• Output: list of locations i(xi, yi) included in your picking route

Results and Next Steps

After setting up all necessary functions to measure picking distance, we can now test our picking route strategy with picking order lines.

Here, we first decided to start with a very simple approach

• Orders Waves: orders are grouped by chronological order of receiving time from OMS ( TimeStamp)
• Picking Route: picking route strategy is following the Next Closest Location logic

To estimate the impact of wave picking strategy on your productivity, we will run several simulations with a gradual number of orders per wave:

1. Measure Total Walking Distance: how much walking distance is reduced when the number of orders per route is increased?
2. Record Picking Route per Wave: recording the sequence of locations per route for further analysis

Experiment 2 - What is the impact of grouping orders by spatial clusters of picking locations?

For more information and details about calculation: Medium Article

Idea: Picking Locations Clusters

Group picking locations by clusters to reduce the walking distance for each picking route. (Example: the maximum walking distance between two locations is <15 m)

Spatial clustering is the task of grouping together a set of points in a way that objects in the same cluster are more similar to each other than to objects in other clusters.

For this part we will split the orders in two categories:

• Mono-line orders: they can be associated to a unique picking locations
• Multi-line orders: that are associated with several picking locations

Mono-line orders

Grouping orders in cluster within n meters of walking distance

Multi-line orders

Grouping multi-line orders in cluster (using centroids of picking locations) within n meters of walking distance

Model Simulation

Methodology

To sum up, our model construction, see the chart below, we have several steps before Picking Routes Creation using Wave Processing.

At each step, we have a collection of parameters that can be tuned to improve performance:

Comparing three methods of wave creation

We’ll start first by assessing the impact of Order Wave processing by clusters of picking locations on total walking distance.

We’ll be testing three different methods:

• Method 1: we do not apply clustering (i.e Initial Scenario)
• Method 2: we apply clustering on single-line orders only
• Method 3: we apply clustering to single-line orders and centroids of multiline orders

Parameters of Simulation

• Order lines: 20,000 Lines
• Distance Threshold: Maximum distance between two picking locations (distance_threshold = 35 m)
• Orders per Wave: orders_number in [1, 9]

Final Results

- Best Performance: Method 3 for 9 orders/Wave with 83% reduction of walking distance - Method 2 vs. Method 1: Clustering for mono-line orders reduce the walking distance by 34% - Method 3 vs. Method 2: Clustering for mono-line orders reduce the walking distance by 10%

Build the application locally 🏗️

Because the ressources provided by Streamlit cloud or Heroku are limited, I would suggest to run this application locally.

Build a python local environment (recommanded)

Then install virtualenv using pip3

sudo pip3 install virtualenv 

Now create a virtual environment

virtualenv venv 

Active your virtual environment

source venv/bin/activate

Launch Streamlit 🚀

Install all dependencies needed using requirements.txt

 pip install -r requirements.txt 

Run the application

streamlit run segmentation.py 

Click on the Network URL in the shell

-> Enjoy!

Use the application 🖥️

Access it here:

Why should you use it?

This Streamlit Web Application has been designed for Supply Chain Engineers to help them simulating the impact on picking route optimization in the total distance of their picking operators.

Load the data

• You can use the dataset located in the folder In/df_lines.csv
• You can build your own dataset following the step of ('Initial Step') above

Experiment 1

Step 1: Scope

As the computation time can increase exponentially with the size of the dataset (optimization can be done) you can ask the model to take only the n thousands first lines for analysis.

Step 2: Fix the range of orders/wave to simulate

In the picture below we ask the model to run a loop testing scenarios with the number of orders per wave varying between 1 to 10

Step 3: SIMULATION 1: START CALCULATION

Click the button to start the calculations

Final Results

💡 This is the same graph with the one presented in the article

Experiment 2

Step 1: Scope

As the computation time can increase exponentially with the size of the dataset (optimization can be done) you can ask the model to take only the n thousands first lines for analysis.

Step 2: SIMULATION 1: START CALCULATION

Click the button to start the calculations

Final Results

💡 This is the same graph with the one presented in the article

About me 🤓

Senior Supply Chain Engineer with an international experience working on Logistics and Transportation operations.
Have a look at my portfolio: Data Science for Supply Chain Portfolio
Data Science for Warehousing📦, Transportation 🚚 and Demand Forecasting 📈