🏭 Factory Layout Optimizer | 공장 레이아웃 최적화 시스템

English | 한국어

🎯 Overview

An AI-powered factory layout optimization system using Genetic Algorithm (GA) to maximize production efficiency while minimizing material flow distances. This system intelligently arranges 16 manufacturing equipment units in a linear 16-step production sequence to achieve optimal throughput and workflow efficiency.

🔥 Key Results

Our genetic algorithm successfully optimized a real factory layout with 16 equipment units (ID 0-15) over 300 generations:

• 🎯 Target Production: 35 units/hour
• ⚡ Optimized Throughput: Achieved target with minimal distance
• 🔄 Convergence: Stable optimization over 300 generations
• 📊 Multi-objective: Balanced production rate vs. material flow distance
• 🎨 Visual Analytics: Comprehensive performance tracking and layout visualization

✨ Key Features

• 🧬 Advanced Genetic Algorithm: N populations × N generations evolutionary optimization
• 🎯 Multi-objective Optimization: Balances production throughput (35 units/hour) and material flow distance
• 📊 Real-time Visualization: Interactive layout visualization with performance analytics
• ⚙️ Constraint Handling: Equipment footprint, clearance zones, and spatial constraints
• 📈 Progress Monitoring: Generation-wise fitness evolution and convergence analysis
• 🔧 Flexible Configuration: Customizable equipment definitions and process sequences

🚀 Quick Start

Prerequisites

pip install matplotlib numpy

Installation & Usage

git clone https://github.com/imjeasung/Factory-Layout-Optimizer.git
cd Factory-Layout-Optimizer
python GA_Facility_Optimizer.py

📊 Results & Visualizations

🏭 Optimized Factory Layout

🎨 Interactive Factory Layout Visualization

Layout Features:

• 📍 Equipment Arrangement: 16 machines (ID 0-15) optimally positioned in 19×19 grid
• 🌈 Color-coded Identification: Each equipment unit with unique color visualization
• 🔒 Safety Clearance Zones: Automated clearance space management
• 🔄 Optimized Flow Paths: Minimized inter-equipment material transport distances
• 📐 Spatial Constraints: Intelligent footprint management and collision avoidance

🚚 Optimized Material Flow Path Visualization

🎨 Visualization of Optimized Material Flow Paths

Path Details:

• 📜 Script Used: path_visualizer.py
• 🧠 Algorithm: Utilizes A* search to find optimal paths between sequential machines.
• ↔️ Continuous Flow: Visualizes the "one-stroke" or continuous path for material handling, minimizing travel interruptions.
• 📄 Input Data: Requires optimized_layout_data.json (generated by GA_Facility_Optimizer.py) which contains the optimized layout and machine sequence.
• 🎯 Objective: To clearly show the actual travel routes for materials/AGVs post-layout optimization, aiding in identifying potential bottlenecks or inefficiencies in flow.

📈 Comprehensive Performance Analysis

📊 Multi-dimensional Algorithm Performance Tracking

Analysis Dashboard Includes:

• 📈 Fitness Evolution Curve: Real-time convergence tracking over 300 generations
• 📏 Distance Optimization Progress: Material flow distance minimization trends
• ⚡ Throughput Performance: Production rate optimization and target achievement
• ✅ Population Validity Metrics: Solution feasibility and constraint satisfaction rates
• 🎯 Multi-objective Balance: Trade-off analysis between competing objectives

🛠 Technical Specifications

Algorithm Parameters

• Population Size: 100 individuals per generation
• Generations: 300 iterations
• Mutation Rate: 0.3 (30%)
• Crossover Rate: 0.8 (80%)
• Elite Preservation: Top 5 individuals per generation
• Tournament Selection: Size 5

Optimization Objectives

1. 🎯 Maximize Throughput: Target 35 units/hour production rate
2. 📏 Minimize Distance: Reduce material flow distances between equipment
3. ✅ Constraint Satisfaction: Ensure spatial and operational constraints

Factory Configuration

• 📐 Factory Dimensions: 19×19 grid units
• 🏗️ Equipment Count: 16 manufacturing stations
• 🔄 Process Sequence: Linear 16-step production flow
• ⚡ Material Transport Speed: 0.5 units/second

🎛️ Equipment Specifications

ID	Equipment Name	Footprint	Cycle Time	Clearance
0	원자재_투입	2×2	20s	1 unit
1	1차_절삭	3×3	35s	1 unit
2	밀링_가공	4×2	45s	1 unit
3	드릴링	2×2	25s	1 unit
4	열처리_A	3×4	70s	2 units
5	정밀_가공_A	3×2	40s	1 unit
6	조립_A	2×3	55s	2 units
7	최종_검사_A	1×2	15s	1 unit
8	2차_절삭	3×2	30s	1 unit
9	표면_처리	2×4	50s	2 units
10	세척_공정_1	2×2	20s	1 unit
11	열처리_B	4×4	75s	2 units
12	정밀_가공_B	2×3	42s	1 unit
13	부품_조립	3×3	60s	1 unit
14	품질_검사_B	2×1	18s	1 unit
15	포장_라인_A	4×3	30s	2 units

📈 Performance Metrics

Fitness Function

fitness = (THROUGHPUT_WEIGHT × throughput) - (DISTANCE_WEIGHT × total_distance)

Optimization Weights:

• THROUGHPUT_WEIGHT: 1.0
• DISTANCE_WEIGHT: 0.005
• BONUS_ACHIEVEMENT: 0.2 (when target reached)

Achieved Results

• 🎯 Production Target: 35 units per hour ✅
• 📏 Material Transport Speed: 0.5 units per second
• ⚡ Algorithm Convergence: Stable optimization after ~150 generations
• ✅ Solution Validity: >90% valid solutions maintained throughout evolution
• 🔄 Consistency: Reproducible results across multiple runs

🔧 Customization Guide

Equipment Configuration

machines_definitions = [
    {"id": 0, "name": "원자재_투입", "footprint": (2, 2), "cycle_time": 20, "clearance": 1},
    {"id": 1, "name": "1차_절삭", "footprint": (3, 3), "cycle_time": 35, "clearance": 1},
    # Add more equipment definitions...
]

Process Sequence

PROCESS_SEQUENCE = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]  # Linear 16-step

Factory Dimensions

FACTORY_WIDTH = 19
FACTORY_HEIGHT = 19

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Korean Section

🏭 공장 레이아웃 최적화 시스템

🎯 프로젝트 개요

유전 알고리즘(GA)을 활용하여 16개 설비의 선형 16단계 공정을 최적화하는 AI 시스템입니다. 생산 효율성을 극대화하면서 물류 동선을 최소화하여 최적의 설비 배치를 찾아줍니다.

🔥 주요 성과

ID 15번까지의 설비로 구성된 실제 공장 레이아웃을 300세대에 걸쳐 성공적으로 최적화:

• 🎯 목표 생산량: 시간당 35개
• ⚡ 최적화된 처리량: 목표 달성 및 거리 최소화
• 🔄 수렴성: 300세대에 걸친 안정적 최적화
• 📊 다중 목표: 생산율 vs 물류 거리 균형

주요 기능

• 🧬 고급 유전 알고리즘: 300개체 × 300세대 진화 최적화
• 🎯 다중 목표 최적화: 생산량(시간당 35개)과 이동 거리 동시 고려
• 📊 실시간 시각화: 성능 분석이 포함된 대화형 레이아웃 시각화
• ⚙️ 제약 조건 처리: 설비 크기, 클리어런스, 공간 제약 고려
• 📈 진행 상황 모니터링: 세대별 적합도 진화 및 수렴 분석
• 🔧 유연한 설정: 설비 정의 및 공정 순서 커스터마이징

📊 결과 및 시각화

🏭 최적화된 공장 레이아웃

16개 설비(ID 0-15)가 최적 배치된 결과로, 각 설비는 고유 색상으로 구분되며 클리어런스 영역과 물류 흐름이 최적화되었습니다.

📈 성능 분석

300세대에 걸친 적합도 진화, 거리 최적화, 처리량 분석, 모집단 유효성 등을 종합적으로 분석한 결과를 제공합니다.

🛠 기술 사양

알고리즘 매개변수

• 집단 크기: 세대당 100개 개체
• 세대 수: 300회 반복
• 변이율: 0.3 (30%)
• 교차율: 0.8 (80%)
• 엘리트 보존: 세대당 상위 5개 개체

최적화 목표

1. 🎯 생산량 최대화: 시간당 35개 목표 생산율
2. 📏 거리 최소화: 설비 간 물류 이동 거리 단축
3. ✅ 제약 조건 만족: 공간 및 운영 제약 조건 준수

🚀 사용법

환경 설정

pip install matplotlib numpy

실행 방법

git clone https://github.com/imjeasung/Factory-Layout-Optimizer.git
cd Factory-Layout-Optimizer
python GA_Facility_Optimizer.py

🔧 커스터마이징

프로젝트의 설비 구성, 공정 순서, 공장 크기 등을 필요에 따라 수정할 수 있습니다.

🤝 기여하기

Pull requests를 환영합니다! 주요 변경사항의 경우 먼저 이슈를 열어 논의해 주세요.

📝 라이선스

이 프로젝트는 MIT 라이선스 하에 있습니다. 자세한 내용은 LICENSE 파일을 참조하세요.

👨‍💻 개발자

Made with ❤️ by imjeasung

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