Multiple Optimal Solutions
Imagine climbing to the highest point of a mountain range and finding two peaks of exactly the same height, joined by a flat ridge. You have not found one best spot — you have found many, all equally high. That is the picture behind multiple optimal solutions: a problem where more than one choice gives the same best value of the objective.
A concrete example
Maximise Z=2x+2y subject to x+y≤10, x,y≥0.
Since Z=2x+2y=2(x+y) and x+y≤10, the largest value is Z=20. But which point achieves it? Every point on the line x+y=10 in the first quadrant — (10,0), (0,10), (5,5), (3,7) — gives Z=20. There is not one optimal point but a whole edge of optimal points.
Why it happens
In graphical linear programming, multiple optimal solutions occur when the objective line is parallel to one of the boundary edges of the feasible region. As you slide the objective line outward, its final contact with the region is that whole edge, not a single corner — so every point on the edge (including both its corner endpoints) is optimal.
How to recognise it
Using the graphical method:
- Draw the feasible region.
- Draw the objective line ax+by=constant for any value.
- Slide it parallel to itself in the direction of improvement.
- If the last contact is a line segment rather than a single corner, the problem has multiple optimal solutions.
A quick algebraic hint: if the objective Z=ax+by gives the same optimal value at two adjacent corners, then every point on the edge joining them is also optimal.
Why it matters
Multiple optimal solutions are a feature, not a mistake — they mean the problem has flexibility. A company might have several production plans yielding the same maximum profit, and can then choose between them on secondary grounds (ease, risk, preference) without losing any profit.
Do not confuse this with unboundedness (no finite optimum at all) or infeasibility (no feasible point at all). Multiple optimal solutions are a well-defined set of equally-good answers — a tie for first place, not a breakdown of the problem.
Multiple Optimal Solutions is a nuanced idea within the CBSE Class 12 Linear Programming chapter, tested when the objective line runs parallel to a boundary edge of the feasible region — a scenario NCERT explicitly includes in its solved examples and exercises. "When does an LPP have infinite optimal solutions" is a frequent student query, since recognising this case rather than assuming a single unique answer is often the difference-maker in board exam marks.