Update README.md

Author: vinit714

README.md

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-# Optimizing-Warehouse-Efficiency-via-Python-Based-Order-Batching
+# Optimizing-Warehouse-Efficiency-via-Python-Based-Order-Batching
+
+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.
+
+<p align="center">
+  <img align="center" src="static/img/intro_1.gif" width=75%>
+</p>
+<p align="center"><b>Scenario 1:</b> Picking routes with 1 order picked per wave</p>
+
+I've been exploring an approach to design a model that simulates the impact of different picking processes and routing methods, aiming to find the optimal order picking strategy using the **Single Picker Routing Problem (SPRP)** in a two-dimensional warehouse layout (axis-x and 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 ?”
+
+This repo is containing a ready-to-use **Streamlit App** designed for **Logistics Engineers** to test these different strategies by only uplooading their own dataset of order lines records.
+-
+
+# Picking Route Optimization 🚶‍♂️ 
+
+## 💾 **Initial: prepare order lines datasets with picking locations**
+
+Based on your **actual warehouse layout**, storage locations are mapped with **2-D (x, y) coordinates** that will be used to measure walking distance.
+
+<p align="center">
+  <img align="center" src="static/img/warehouse_layout.png" width=75%>
+</p>
+<p align="center">Warehouse Layout with 2D Coordinates</p>
+
+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.
+
+<p align="center">
+  <img align="center" src="static/img/processing_layout.png" width=75%>
+   
+</p>
+<p align="center">Database Schema</p>
+
+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: Impacts of wave picking on the pickers walking distance?**
+
+### ✔️ 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).
+
+<p align="center">
+  <img align="center" src="static/img/trolley.jpeg" width=35%>
+  
+</p>
+<p align="center">Different routes between two storage locations in the warehouse</p>
+
+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
+
+<p align="center">
+  <img align="center" src="static/img/wave_picking.gif" width=75%>
+  
+</p>
+<p align="center"><b>Scenario 2:</b> Wave Picking applied to Scenario 1</p>
+
+The first intuitive way to optimize this process is to combine these three orders in one picking route — this strategy is commonly called Wave Picking.
+
+We are going to build a model to simulate the impact of several Wave Picking strategies in the total walking distance for a specific set of orders to prepare.
+
+
+### 📊 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
+<p align="center">
+  <img align="center" src="static/img/batch_function_1.png" width=75%>
+  
+</p>
+<p align="center"><b>Function:</b> Different routes between two storage locations in the warehouse</p>
+
+
+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
+<p align="center">
+  <img align="center" src="static/img/batch_function_2.png" width=75%>
+  
+</p>
+<p align="center"><b>Function:</b> Next Storage Location Scenario</p>
+
+
+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
+
+
+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
+
+<p align="center">
+  <img align="center" src="static/img/batch_final.png" width=100%>
+  
+</p>
+<p align="center"><b>Experiment 1:</b> Results for 5,000 order lines with a ratio from 1 to 9 orders per route</p>
+
+
+## 🧮**Experiment 2: Impacts of orders batching using spatial clusters of picking locations?**
+
+<p align="center">
+  <img align="center" src="static/img/cluster_process.png" width=100%>
+  
+</p>
+<p align="center"><b>Order Lines Processing</b> for Order Wave Picking using Clustering by Picking Location</p>
+
+### 💡**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** 
+<p align="center">
+  <img align="center" src="static/img/cluster_walking_distance.png" width=100%>
+   
+</p>
+<p align="center">Left [Clustering using Walking Distance] / Right [Clustering using Euclidian Distance]</p>
+
+_Grouping orders in cluster within n meters of walking distance_
+
+#### **Multi-line orders** 
+<p align="center">
+  <img align="center" src="static/img/cluster_centroids.png" width=75%>
+  
+</p>
+<p align="center"><b>Example: </b>Centroid of three Picking Locations</p>
+
+_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:
+<p align="center">
+  <img align="center" src="static/img/cluster_analysis.png" width=100%>
+  
+</p>
+<p align="center"><b>Methodology: </b>Model Construction with Parameters</p>
+
+#### **Comparing three methods of wave creation**
+<p align="center">
+  <img align="center" src="static/img/wave_creation.png" width=75%>
+  
+</p>
+<p align="center"><b>Methodology: </b>Three Methods for Wave Processing</p>
+
+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**
+<p align="center">
+  <img align="center" src="static/img/cluster_final_results.png" width=100%>
+  
+</p>
+<p align="center"><b>Test 1:</b> 20,000 Order Lines / 35 m distance Threshold</p>
+
+- 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 app.py --server.address 0.0.0.0 
+
+### Click on the URL   
+  <p align="center">
+    <img align="center" src="static/img/launch_streamlit.png" width=50%>
+      
+  </p>
+<p align="center"><b>Instructions:</b> Click on the URL</p>
+  
+> -> Enjoy!
+
+# Use the application 🖥️ 
+> This app has not been deployed, you need to use it locally/.
+
+## **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
+<p align="center">
+  <img align="center" src="static/img/params_1.PNG" width=75%>
+  
+</p>
+<p align="center"><b>Experiment 1:</b> Parameters</p>
+
+### **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:** START CALCULATION
+
+Click the button to start the calculations
+
+### **Final Results**
+<p align="center">
+  <img align="center" src="static/img/batch_results.png" width=75%>
+    
+</p>
+<p align="center"><b>Experiment 1:</b> Final Resultss</p>
+
+
+## 🧪 Experiment 2
+<p align="center">
+  <img align="center" src="static/img/params_2.PNG" width=75%>
+  
+</p>
+<p align="center"><b>Experiment 2:</b> Parameters</p>
+
+### **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:** START CALCULATION
+
+Click the button to start the calculations
+
+### **Final Results**
+<p align="center">
+  <img align="center" src="static/img/streamlit_picking_route.png" width=75%>
+</p>
+<p align="center"><b>Experiment 2:</b> Final Results</p>