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README.md

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@@ -2069,38 +2069,44 @@ Entering variable: $x_{13}$.
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#### [4.3](). Update Basic Variables
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🏄🏄🏄🏄🏄
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<br>
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| Variable | Adjustment | New Value |
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|--------------|------------|-----------|
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| $x_{13}$ | $+100$ | $100$ |
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| $x_{33}$ | $-100$ | $50$ |
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| $x_{31}$ | $+100$ | $110$ |
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| $ x_{11} $ | $-100$ | $0$ |
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| $x_{11}$ | $-100$ | $0$ |
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<br>
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**New Basic Variables:**
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- $ x_{13} = 100 $
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- $ x_{21} = 10 $
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- $ x_{22} = 130 $
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- $ x_{31} = 110 $
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- $ x_{33} = 50 $
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[**New Basic Variables**]():
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- $x_{13} = 100$
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- $x_{21} = 10$
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- $x_{22} = 130$
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- $x_{31} = 110$
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- $x_{33} = 50$
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<br>
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#### [4.4](). Verify Feasibility
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- **Supplies**:
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- Supplier 1: $ 100 $ ✔️
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- Supplier 2: $ 10 + 130 = 140 $ ✔️
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- Supplier 3: $ 110 + 50 = 160 $ ✔️
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<br>
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- **Demands**:
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- Consumer 1: $ 10 + 110 = 120 $ ✔️
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- Consumer 2: $ 130 $ ✔️
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- Consumer 3: $ 100 + 50 = 150 $ ✔️
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- [**Supplies**]():
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- [Supplier 1](): $100$ ✔️
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- [Supplier 2](): $10 + 130 = 140$ ✔️
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- [Supplier 3](): $110 + 50 = 160$ ✔️
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<br>
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- [**Demands**]():
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- [Consumer 1](): $10 + 110 = 120$ ✔️
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- [Consumer 2](): $130$ ✔️
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- [Consumer 3](): $100 + 50 = 150$ ✔️
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<br>
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#### [4.5](). Calculate Final Total Cost
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<br>
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### [4.6](). Final Optimality Check
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#### [4.6](). Final Optimality Check
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Recalculating reduced costs confirms all $\bar{c}_{ij} \geq 0$. [**Optimal solution reached**]().
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Recalculating reduced costs confirms all $ \bar{c}_{ij} \geq 0 $. **Optimal solution reached**.
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<br>
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## [Step 5](): Final Solution
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<br>
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## Final Solution
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| Variable | Value |
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|------------|-------|
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| $ x_{13} $ | 100 |
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| $ x_{21} $ | 10 |
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| $ x_{22} $ | 130 |
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| $ x_{31} $ | 110 |
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| $ x_{33} $ | 50 |
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| $x_{13}$ | 100 |
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| $x_{21}$ | 10 |
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| $x_{22}$ | 130 |
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| $x_{31}$ | 110 |
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| $x_{33}$ | 50 |
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<br>
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**Total Cost:** $\boxed{10460}$.
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[**Total Cost:**](): $\boxed{10460}$.
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This is the optimal solution with all reduced costs non-negative.
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***This is the optimal solution with all reduced costs non-negative.***
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<br>
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### Theoretical Explanation
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The **Assignment Problem** aims to allocate *n* tasks to *n* agents (machines, workers) at minimum cost, ensuring each task and agent is assigned exactly once.**
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The [**Assignment Problem**]() aims to allocate *n* tasks to *n* agents (machines, workers) at minimum cost, ensuring each task and agent is assigned exactly once.
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### [Problem Statement]():
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<br>
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🏄🏄🏄🏄🏄
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## 1. [Hungarian Method]() (Step by Step):
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### [**Step 1](): Subtract Row Minimums**
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### [Step 1](): Subtract Row Minimums
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#### Subtract the minimum value in each row from all elements in that row.
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#### [Subtract the minimum value in each row from all elements in that row]().
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- Row 1 min: 2 → [0, 2, 1]
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- Row 2 min: 1 → [0, 2, 1]
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- Row 3 min: 2 → [3, 0, 2]
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<br>
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- [Row 1 min: 2]()[0, 2, 1]
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- [Row 2 min: 1]()[0, 2, 1]
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- [Row 3 min: 2]()[3, 0, 2]
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<br>
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#### [**Matrix after row subtraction:**]()
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#### [Matrix after row subtraction]():
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<br>
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| | M1 | M2 | M3 |
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|---------|----|----|----|
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<br>
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### [**Step 2](): Subtract Column Minimums**
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### [Step 2](): Subtract Column Minimums
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### Problem Recap
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### [Problem Recap]():
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<br>
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- **3 tasks** must be assigned to **3 machines**.
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- Each task can be done by any machine, but with different costs.
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- Each task must be assigned to exactly one machine, and each machine to exactly one task.
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- **Goal:** Minimize total assignment cost.
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### Cost Table
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<br>
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### [Cost Table]():
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<br>
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| | Machine 1 | Machine 2 | Machine 3 |
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|---------|-----------|-----------|-----------|

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