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https://github.com/abdelrahmanelsafty75/construction-cost-optimization

"Cost minimization tool for construction using Linear Programming and AI sensitivity analysis."
https://github.com/abdelrahmanelsafty75/construction-cost-optimization

construction-tech gui linear-programming operations-research python

Last synced: 8 days ago
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"Cost minimization tool for construction using Linear Programming and AI sensitivity analysis."

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README 

          
# šŸ—ļø Construction Cost Optimizer



![Python Version](https://img.shields.io/badge/python-3.8%2B-blue?style=for-the-badge&logo=python)

![License](https://img.shields.io/badge/license-MIT-green?style=for-the-badge)

![Status](https://img.shields.io/badge/status-stable-success?style=for-the-badge)

![Topic](https://img.shields.io/badge/topic-Operations%20Research-orange?style=for-the-badge)



Linear Programming solution for minimizing construction material costs while meeting engineering specifications.



---



##  Problem Statement



A construction company needs to produce a concrete mix using two raw materials with specific cost and property characteristics:



### Materials Overview



| Material | Cost/Unit | Strength | Durability | Water Resistance |

|----------|-----------|----------|------------|------------------|

| **Cement** | $20 | 2 units | 1 unit | 1 unit |

| **Sand** | $10 | 1 unit | 1 unit | 3 units |



### Requirements

- āœ… Minimum **Strength**: 30 units

- āœ… Minimum **Durability**: 20 units  

- āœ… Minimum **Water Resistance**: 40 units



### Objective

**Minimize** the total cost while meeting all engineering specifications.



---



## āœ… Solution



Using Linear Programming optimization:



```

šŸŽÆ Optimal Mix:

   ā€¢ Cement: 10 units

   ā€¢ Sand: 10 units

   ā€¢ Total Cost: $300

```



All constraints satisfied:

- Strength: 2(10) + 10 = **30 units** āœ“

- Durability: 10 + 10 = **20 units** āœ“

- Water Resistance: 10 + 3(10) = **40 units** āœ“



---



## šŸ“ Mathematical Model



### Decision Variables

- `xā‚` = Units of Cement

- `xā‚‚` = Units of Sand



### Objective Function

```

Minimize: Z = 20xā‚ + 10xā‚‚

```



### Constraints

```

Subject to:

   2xā‚ + xā‚‚  ā‰„ 30    (Strength)

   xā‚  + xā‚‚  ā‰„ 20    (Durability)

   xā‚  + 3xā‚‚ ā‰„ 40    (Water Resistance)

   xā‚, xā‚‚    ā‰„ 0     (Non-negativity)

```



---



## šŸ“‚ Project Structure



```

construction-cost-optimization/

ā”œā”€ā”€ src/

ā”‚   ā”œā”€ā”€ solver.py          # Custom LP Algorithm Implementation

ā”‚   ā”œā”€ā”€ visualizer.py      # Matplotlib Visualization Tools

ā”‚   ā”œā”€ā”€ gui.py             # Tkinter User Interface

ā”‚   ā””ā”€ā”€ main.py            # Application Entry Point

ā”‚

ā”œā”€ā”€ docs/

ā”‚   ā”œā”€ā”€ problem_statement.md      # Detailed Problem Description

ā”‚   ā”œā”€ā”€ mathematical_model.md     # Complete LP Formulation

ā”‚   ā”œā”€ā”€ user_guide.md             # Usage Instructions

ā”‚   ā””ā”€ā”€ presentation.pdf          # Project Presentation

ā”‚

ā”œā”€ā”€ screenshots/           # Application Screenshots & Plots

ā”œā”€ā”€ requirements.txt       # Python Dependencies

ā””ā”€ā”€ README.md             # This File

```



---



##  Installation



### Prerequisites

- Python 3.8 or higher

- pip package manager



### Quick Setup



```bash

# Clone the repository

git clone https://github.com/abdelrahmanelsafty75/construction-cost-optimization.git

cd construction-cost-optimization



# Install dependencies

pip install -r requirements.txt



# Verify installation

python src/main.py

```



---



##  Usage



### Option 1: Interactive Menu

```bash

python src/main.py

```



Launches an interactive menu with options:

1. CLI Mode - Command-line interface

2. GUI Mode - Graphical user interface

3. Run Examples - Pre-configured scenarios

4. Generate Visualizations - Create plots



### Option 2: Direct GUI Launch

```bash

python src/gui.py

```



Opens the graphical interface immediately with:

- Input fields for costs and requirements

- Real-time optimization solving

- Interactive result visualization

- Parameter adjustment controls



---



## āœØ Features



### Core Capabilities

- **Custom Manual Solver** - Corner point method implementation from scratch

- **PuLP Integration** - Verification using industry-standard solver

- **Interactive GUI** - User-friendly Tkinter interface

- **Advanced Visualizations** - Professional matplotlib charts

- **Sensitivity Analysis** - Test parameter variations

- **Batch Processing** - Run multiple scenarios



### Visualization Tools

- **Feasible Region Plot** - Shows constraints and optimal solution

- **Cost Comparison** - Compare different scenarios

- **Sensitivity Analysis** - Price variation impact

- **Property Breakdown** - Material contribution charts



---



## šŸ–¼ļø Screenshots



### GUI Interface

![GUI Interface](screenshots/gui_input_window_01.png)



### Feasible Region Visualization

![Feasible Region](screenshots/gui_output_graph_00.png)



### Optimization Results

![Results Output](screenshots/gui_recommended_02.png)



---



## šŸ“š Documentation



Comprehensive documentation available in the `docs/` folder:



- **[Problem Statement](docs/problem_statement.md)** - Business context and problem description

- **[Mathematical Model](docs/mathematical_model.md)** - Complete LP formulation and solution methods

- **[User Guide](docs/user_guide.md)** - Installation, usage, and troubleshooting

- **[Presentation](docs/presentation.pdf)** - Original project presentation



---



## šŸ”¬ Technical Details



### Algorithm Implementation

- **Method**: Corner Point (Graphical) Method

- **Approach**: Find constraint intersections, evaluate objective at each

- **Verification**: Compare with PuLP Simplex solver

- **Complexity**: O(nĀ²) for n constraints



### Technologies Used

- **Python 3.8+** - Core programming language

- **NumPy** - Numerical computations

- **Matplotlib** - Data visualization and plotting

- **Tkinter** - GUI framework (built-in with Python)

- **PuLP** - LP solver for verification (optional)



---



## šŸŽ“ Academic Context



**Course:** Operations Research  

**Institution:** October 6 University  

**Semester:** Fall 2025 



This project demonstrates practical applications of Linear Programming in:

- Construction management

- Resource allocation

- Cost optimization

- Constraint satisfaction problems



---



## šŸ¤ Contributing



Contributions are welcome! Please:



1. Fork the repository

2. Create a feature branch (`git checkout -b feature/AmazingFeature`)

3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)

4. Push to the branch (`git push origin feature/AmazingFeature`)

5. Open a Pull Request



---



##  Contact



For questions, suggestions, or collaboration:

-  Email: [abdelrhmanelsafty74gmail.com]

-  LinkedIn: [www.linkedin.com/in/abdelrahmanelsafty75]



---





  Made with ā¤ļø by the Construction Optimization Team

  


  Operations Research Project - Fall 2025