facility-layout-optimizer

What this skill does

# facility-layout-optimizer

You are **facility-layout-optimizer** - a specialized skill for optimizing facility layouts to minimize material flow and maximize space utilization.

## Overview

This skill enables AI-powered facility layout optimization including:
- From-To chart analysis
- Activity relationship diagramming
- CRAFT and ALDEP algorithm implementation
- Block layout generation
- Aisle design and dimensioning
- Material flow visualization
- Space requirement calculation
- Layout alternative evaluation

## Capabilities

### 1. From-To Chart Analysis

```python
import numpy as np
import pandas as pd

def create_from_to_chart(flow_data: list):
    """
    Create From-To chart from material flow data

    flow_data: list of (from_dept, to_dept, flow_volume, cost_per_unit)
    """
    # Get unique departments
    depts = set()
    for from_d, to_d, _, _ in flow_data:
        depts.add(from_d)
        depts.add(to_d)
    depts = sorted(list(depts))

    # Create matrix
    n = len(depts)
    flow_matrix = np.zeros((n, n))
    cost_matrix = np.zeros((n, n))

    dept_idx = {d: i for i, d in enumerate(depts)}

    for from_d, to_d, flow, cost in flow_data:
        i, j = dept_idx[from_d], dept_idx[to_d]
        flow_matrix[i, j] = flow
        cost_matrix[i, j] = cost

    # Calculate weighted flow
    weighted_flow = flow_matrix * cost_matrix

    return {
        "departments": depts,
        "flow_matrix": pd.DataFrame(flow_matrix, index=depts, columns=depts),
        "cost_matrix": pd.DataFrame(cost_matrix, index=depts, columns=depts),
        "weighted_flow": pd.DataFrame(weighted_flow, index=depts, columns=depts),
        "total_flow": flow_matrix.sum(),
        "total_weighted_flow": weighted_flow.sum()
    }
```

### 2. Activity Relationship Diagram

```python
from dataclasses import dataclass
from enum import Enum

class Closeness(Enum):
    A = "Absolutely necessary"
    E = "Especially important"
    I = "Important"
    O = "Ordinary"
    U = "Unimportant"
    X = "Undesirable"

@dataclass
class RelationshipEntry:
    dept1: str
    dept2: str
    closeness: Closeness
    reason: str

def create_relationship_chart(relationships: list):
    """
    Create Activity Relationship Chart (REL chart)
    """
    # Extract departments
    depts = set()
    for r in relationships:
        depts.add(r.dept1)
        depts.add(r.dept2)
    depts = sorted(list(depts))

    # Create relationship matrix
    n = len(depts)
    rel_matrix = {}

    for r in relationships:
        key = (r.dept1, r.dept2) if r.dept1 < r.dept2 else (r.dept2, r.dept1)
        rel_matrix[key] = {
            "closeness": r.closeness.name,
            "reason": r.reason
        }

    # Closeness score for layout optimization
    closeness_scores = {
        'A': 64, 'E': 16, 'I': 4, 'O': 1, 'U': 0, 'X': -64
    }

    # Create numeric matrix for algorithms
    score_matrix = np.zeros((n, n))
    dept_idx = {d: i for i, d in enumerate(depts)}

    for (d1, d2), rel in rel_matrix.items():
        i, j = dept_idx[d1], dept_idx[d2]
        score = closeness_scores[rel['closeness']]
        score_matrix[i, j] = score
        score_matrix[j, i] = score

    return {
        "departments": depts,
        "relationships": rel_matrix,
        "score_matrix": pd.DataFrame(score_matrix, index=depts, columns=depts),
        "summary": {
            "total_relationships": len(relationships),
            "A_count": sum(1 for r in relationships if r.closeness == Closeness.A),
            "X_count": sum(1 for r in relationships if r.closeness == Closeness.X)
        }
    }
```

### 3. CRAFT Algorithm

```python
def craft_algorithm(initial_layout: np.ndarray, flow_matrix: np.ndarray,
                   distance_matrix_func, max_iterations: int = 100):
    """
    CRAFT (Computerized Relative Allocation of Facilities Technique)

    Improvement algorithm - starts with initial layout and iteratively improves
    """
    n = len(flow_matrix)
    current_layout = initial_layout.copy()

    def calculate_cost(layout, flow, dist_func):
        total_cost = 0
        for i in range(n):
            for j in range(n):
                if i != j:
                    loc_i = np.argwhere(layout == i)[0]
                    loc_j = np.argwhere(layout == j)[0]
                    dist = dist_func(loc_i, loc_j)
                    total_cost += flow[i, j] * dist
        return total_cost

    current_cost = calculate_cost(current_layout, flow_matrix, distance_matrix_func)
    iteration = 0
    improvement_history = [{"iteration": 0, "cost": current_cost}]

    while iteration < max_iterations:
        best_swap = None
        best_cost = current_cost

        # Try all pairwise exchanges
        for i in range(n):
            for j in range(i + 1, n):
                # Swap departments i and j
                test_layout = current_layout.copy()
                pos_i = np.argwhere(test_layout == i)[0]
                pos_j = np.argwhere(test_layout == j)[0]
                test_layout[tuple(pos_i)] = j
                test_layout[tuple(pos_j)] = i

                test_cost = calculate_cost(test_layout, flow_matrix, distance_matrix_func)

                if test_cost < best_cost:
                    best_cost = test_cost
                    best_swap = (i, j)

        if best_swap is None:
            break  # No improvement found

        # Apply best swap
        i, j = best_swap
        pos_i = np.argwhere(current_layout == i)[0]
        pos_j = np.argwhere(current_layout == j)[0]
        current_layout[tuple(pos_i)] = j
        current_layout[tuple(pos_j)] = i
        current_cost = best_cost
        iteration += 1

        improvement_history.append({
            "iteration": iteration,
            "swap": best_swap,
            "cost": current_cost
        })

    return {
        "final_layout": current_layout,
        "final_cost": current_cost,
        "iterations": iteration,
        "improvement_history": improvement_history,
        "improvement_percent": (improvement_history[0]['cost'] - current_cost) /
                              improvement_history[0]['cost'] * 100
    }
```

### 4. Block Layout Generation

```python
def generate_block_layout(departments: list, space_requirements: dict,
                         facility_dimensions: tuple, rel_chart: dict):
    """
    Generate block layout from space requirements
    """
    width, height = facility_dimensions
    total_space = width * height

    # Calculate space allocation
    total_required = sum(space_requirements.values())

    layouts = []

    # Simple strip-based layout
    x_pos = 0
    y_pos = 0
    max_height_in_row = 0

    for dept in departments:
        required = space_requirements.get(dept, 100)
        # Calculate block dimensions (roughly square)
        block_width = np.sqrt(required)
        block_height = required / block_width

        if x_pos + block_width > width:
            # Move to next row
            x_pos = 0
            y_pos += max_height_in_row
            max_height_in_row = 0

        layouts.append({
            "department": dept,
            "x": x_pos,
            "y": y_pos,
            "width": block_width,
            "height": block_height,
            "area": required
        })

        x_pos += block_width
        max_height_in_row = max(max_height_in_row, block_height)

    return {
        "blocks": layouts,
        "facility_dimensions": facility_dimensions,
        "total_space_used": sum(b['area'] for b in layouts),
        "utilization": sum(b['area'] for b in layouts) / total_space * 100
    }
```

### 5. Layout Evaluation

```python
def evaluate_layout(layout: list, flow_data: dict, rel_chart: dict):
    """
    Evaluate layout quality
    """
    # Calculate centroids
    centroids = {}
    for block in layout:
        centroids[block['department']] = (
            block['x'] + block['width'] / 2,
            block['y'] + block['height'] / 2
        )

    # Calculate total ma