Linear Programming Simplex Method Online Calculator

Build simplex tableaux quickly for optimization models. Compare constraints, pivots, slack, and objective changes clearly. Download organized results for study, planning, and reports today.

Calculator Inputs

Objective Function Coefficients

Enter coefficients for Z = c1x1 + c2x2 + c3x3 + c4x4.

Constraints

Constraint 1

Constraint 2

Constraint 3

Constraint 4

Constraint 5

Constraint 6

Example Data Table

Item x1 x2 Relation RHS
Objective Max Z 3 5
Constraint 1 2 3 8
Constraint 2 2 1 6
Constraint 3 1 2 5

Formula Used

The calculator uses a linear objective function and linear constraints.

Objective: Z = c1x1 + c2x2 + ... + cnxn.

Constraint: a1x1 + a2x2 + ... + anxn ≤, ≥, or = b.

Reduced cost: Cj - Zj. A positive value can enter a maximization tableau.

Ratio test: RHS divided by the positive pivot column value. The smallest valid ratio leaves the basis.

For minimization, the calculator changes the objective sign, solves the equivalent maximization model, then reports the original objective value.

How To Use This Calculator

  1. Select maximization or minimization.
  2. Choose the number of decision variables and constraints.
  3. Enter objective coefficients for each active variable.
  4. Enter each constraint coefficient, relation, and right side.
  5. Use a larger Big M value for models with artificial variables.
  6. Press the calculate button to view the result above the form.
  7. Download the CSV or PDF report when needed.

Linear Programming Simplex Method Calculator

This calculator helps solve linear programming models with the simplex method. It is useful when a goal must be maximized or minimized. Common goals include profit, cost, output, distance, time, or resource use. The method works with linear objective functions and linear constraints.

What This Tool Does

The tool builds a simplex tableau from your objective function and constraints. It adds slack variables for less than or equal limits. It adds surplus and artificial variables when needed. Then it chooses pivot columns and pivot rows. Each pivot improves the current solution. The process stops when no improving column remains, or when the model cannot be solved.

Why Simplex Is Important

Simplex is a classic optimization method. It checks corner points of a feasible region without drawing every graph. This makes it powerful for larger problems. A business can compare product mixes. A student can verify homework steps. An analyst can test resource allocation plans. The calculator also shows iterations, so the path is easier to audit.

Inputs You Can Enter

You may choose maximization or minimization. You may enter two to four decision variables. You may enter up to six constraints. Each constraint accepts coefficients, a relation symbol, and a right hand side value. The calculator assumes decision variables are non negative. This is the common form for simplex models.

Reading The Results

The answer shows the final objective value and each decision variable. Slack values show unused capacity. Surplus values show excess over a minimum requirement. Artificial values should finish near zero. If an artificial value remains positive, the original model is infeasible.

Best Use Cases

Use this calculator for classroom learning, operations planning, production scheduling, blending problems, diet models, shipping decisions, and resource allocation. It is not meant for nonlinear models. It also does not replace expert review for financial or engineering decisions.

Accuracy Tips

Check every sign before solving. Make sure all coefficients use the same units. Avoid mixing hours, minutes, dollars, and percentages without conversion. If a result seems strange, review the constraints first. Small input mistakes can change the final corner point completely.

They also help compare manual work against answers. Use exported notes for assignments and reports during review.

FAQs

What is the simplex method?

The simplex method is an optimization process for linear programming. It moves between feasible corner solutions using tableau pivots. The best corner gives the final objective value when the model is bounded and feasible.

Can this calculator solve minimization problems?

Yes. Select minimization before solving. The calculator converts the objective internally, runs the simplex process, and then reports the objective value using your original coefficients.

What does a slack variable mean?

A slack variable measures unused capacity in a less than or equal constraint. A zero slack value means the resource limit is fully used at the final solution.

What does a surplus variable mean?

A surplus variable measures how much a greater than or equal constraint exceeds its required minimum. It appears when the model includes lower bound type restrictions.

Why are artificial variables used?

Artificial variables create a starting basis for equality and greater than or equal constraints. They should become zero in a feasible final solution.

What means unbounded model?

An unbounded model has no finite best value in the selected direction. The objective can keep improving without violating the listed constraints.

What means infeasible model?

An infeasible model has constraints that cannot be satisfied together. The calculator may show this when artificial variables remain positive after the simplex iterations.

Can I download the result?

Yes. After calculation, use the CSV or PDF button. The CSV includes tableau rows. The PDF gives a compact report with status, objective value, and variables.

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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.