In the first edition of my book “Taking Sound Business Decisions: From Rich Data to Better Solutions”, I explain on pages 14 and 15 what the shadow price and reduced cost of a linear programming model really mean. I write the following:
Then, I continue on page 15 with a sentence as follows:
“It should be intuitively clear that the reduced cost is equal to the shadow price of the non-negativity constraint of the variable”.
In the book, I suggest the reader to think about this statement, but some people asked me to explain this statement in more detail. I guess it is less intuitive than I initially thought. Below you find some explanation. I still think, however, that it’s best not to read this and think about it for a minute. It’s not that difficult.
Let’s take a look at the model (I call it here model 1) that I use in the book. I assume that the decision variables x1 and x2 are amounts for production of ... whatever.
Model 1.
minimise cost (C) = 10 x1 + 7 x2.
subject to the following constraints:
x1 + x2 >= 10
x1 >= 0
x2 >= 0

The optimal solution is equal to x1 = 0 and x2 = 10 with an objective of 70.
In the book I explain that the reduced cost for x1 is equal to 3. Indeed, x1 is too expensive compared to x2, and therefore x1 = 0. Therefore, the cost should be reduced from 10 to 7 (or lower, so by minimum a value of 3) to make the production of x1 attractive, hence, the value of 3 for the reduced cost.
Now let’s go back to the statement: “The reduced cost of a decision variable (i.e. value 3 for variable x1) is equal to the shadow price of the non-negativity constraint of the variable (i.e. x1 >= 0)”
The shadow price for the constraint x1 >= 0 can be defined as follows: If you increase the right hand side of that constraint (currently 0) by one unit (i.e. the constraint changes to x1 >= 1), what is the impact on the objective. Hence, the model changes into (notice the small difference):
Model 2.
minimise cost (C) = 10 x1 + 7 x2.
subject to the following constraints:
x1 + x2 >= 10
x1 >= 1
x2 >= 0
The optimal solution is now equal to x1 = 1 and x2 = 9 with an objective of 73. This is exactly 3 more than the previous solution, and hence, the shadow price of the constraint x1 >= 0 in model 1 is equal to 3. This value 3 is equal to the reduced cost of x1 in model 1, which illustrates my statement.

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