Integer Programming Branch and Bound Calculator

Build integer models and inspect branching decisions. Test maximization or minimization cases with clear steps. Export results, node logs, and summaries easily today online.

Integer Programming Branch and Bound Form

Constraints

Use up to four constraints in the form ax + by (<=, >=, =) rhs.

Variable Bounds

Example Data Table

Item Example Value
Objective Maximize Z = 5x + 7y
Constraint 1 2x + y <= 14
Constraint 2 x + 3y <= 18
X Bounds 0 to 10
Y Bounds 0 to 10
Integer Rule x and y must be whole numbers

Formula Used

Objective function: Z = c1x + c2y

Constraint form: ax + by <= rhs, ax + by >= rhs, or ax + by = rhs

LP relaxation: Solve the same model without the integer rule first.

Branching rule: If x or y is fractional, split into two new nodes.

Example branch: if x = 4.6, create x <= 4 and x >= 5.

Bounding test: Prune any node that cannot beat the current best integer solution.

How to Use This Calculator

  1. Select maximize or minimize.
  2. Enter the objective coefficients for x and y.
  3. Add up to four linear constraints.
  4. Choose the correct operator for each constraint.
  5. Set lower and upper bounds for both variables.
  6. Press the solve button.
  7. Review the root LP result, best integer result, and node log.
  8. Use the CSV button for spreadsheet analysis.
  9. Use the PDF button to save the page as a PDF.

About This Integer Programming Branch and Bound Calculator

What this calculator does

This calculator solves small integer programming models with a branch and bound method. It works with two decision variables. It also accepts up to four linear constraints. You can test maximization and minimization cases. The page shows the LP relaxation, branching decisions, and final integer answer.

Why branch and bound matters

Many linear programming answers are fractional. Real planning problems often need whole numbers. You may need full machines, full workers, or full shipments. Branch and bound fixes that issue. It creates smaller subproblems. It then checks bounds and removes weak nodes. This reduces wasted search effort.

Useful outputs for analysis

The result section appears directly below the header and above the form after submission. That keeps the answer visible at once. You can inspect the best integer point and compare it with the root LP relaxation. The node log also helps you understand pruning, fathoming, and branching depth.

Where this method is used

Integer programming supports production planning, assignment models, budget allocation, scheduling, routing, and resource selection. A branch and bound calculator is useful in maths classes and operations research practice. It helps students see how a feasible region changes when new branch constraints are added to a node.

Why exports help

CSV export is useful for reports and audits. You can move node records into a spreadsheet and review each branch step. PDF export helps preserve a printable summary. Both options support classroom work, project documentation, and client reviews. The example data table also gives a quick starting point.

Best use case

This page is best for bounded two-variable models where you want a clear explanation of the search process. It is simple to use, but still shows important branch and bound logic. That makes it a practical learning tool and a helpful quick solver for small integer optimization tasks.

FAQs

1. What does branch and bound mean in integer programming?

Branch and bound solves an LP relaxation first. If the answer is fractional, it splits the model into smaller branches. It then uses bounds to remove nodes that cannot improve the best integer solution.

2. What kind of problems can this calculator solve?

This page solves bounded two-variable integer programming problems with up to four linear constraints. It supports maximization and minimization models and allows <=, >=, and equality constraints.

3. Why is the LP relaxation shown first?

The LP relaxation gives a bound for the original integer problem. It shows the best continuous solution before branching. That value helps decide whether a node is worth exploring.

4. Why do I need variable bounds?

Bounds keep the search region finite and practical. They also make enumeration and node generation more stable. Clear lower and upper limits improve the quality of the result and reduce unnecessary search.

5. What does pruning mean in the node log?

Pruning means a node is removed from further search. This happens when the node is infeasible or when its bound cannot beat the current best integer solution.

6. Can I use decimal coefficients in the objective or constraints?

Yes. The calculator accepts decimal values for coefficients, right-hand sides, and variable bounds. The final decision variables are still treated as integers during branch and bound.

7. Why might no integer solution appear?

No integer solution appears when all feasible points violate the whole-number rule or when the chosen bounds and constraints leave no valid integer combination inside the feasible region.

8. How do the CSV and PDF options help?

The CSV file stores the summary and node log for later analysis. The PDF option lets you save or print the current result page for notes, assignments, and reporting.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.