Chapter 02 – Linear Programming: Basic Concepts
Chapter 2
Linear programming: basic concepts
True-False Questions
2-1 Linear programming problems may have multiple goals or objectives specified.
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2-2 Linear programming allows a manager to find the best mix of activities to pursue and
at what levels.
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2-3 Linear programming problems always involve either maximizing or minimizing an
objective function.
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2-4 All linear programming models have an objective function and at least two constraints.
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2-5 Constraints limit the alternatives available to a decision-maker.
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2-6 When formulating a linear programming problem on a spreadsheet, the data cells will
show the optimal solution.
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2-7 When formulating a linear programming problem on a spreadsheet, objective cells will
show the levels of activities for the decisions being made.
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2-8 When formulating a linear programming problem on a spreadsheet, the Excel equation
for each output cell can typically be expressed as a SUMPRODUCT function.
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2-9 One of the great strengths of spreadsheets is their flexibility for dealing with a wide
variety of problems.
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2-10 Linear programming problems can be formulated both algebraically and on
spreadsheets.
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2-11 The parameters of a model are the numbers in the data cells of a spreadsheet.
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2-12 An example of a decision variable in a linear programming problem is profit
maximization.
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2-13 A feasible solution is one that satisfies all the constraints of a linear programming
problem simultaneously.
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2-14 An infeasible solution violates all of the constraints of the problem.
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2-15 The best feasible solution is called the optimal solution.
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2-16 Since all linear programming models must contain nonnegativity constraints, Solver
will automatically include them and it is not necessary to add them to a formulation.
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2-17 The line forming the boundary of what is permitted by a constraint is referred to as a
parameter.
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2-18 The origin satisfies any constraint with a ≥ sign and a positive right-hand side.
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2-19 The feasible region only contains points that satisfy all constraints.
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2-20 A circle would be an example of a feasible region for a linear programming problem.
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2-21 The equation 5x + 7y = 10 is linear.
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2-22 The equation 3xy = 9 is linear.
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2-23 The graphical method can handle problems that involve any number of decision
variables.
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2-24 An objective function represents a family of parallel lines.
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2-25 When solving linear programming problems graphically, there are an infinite number
of possible objective function lines.
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2-26 For a graph where the horizontal axis represents the variable x and the vertical axis
represents the variable y, the slope of a line is the change in y when x is increased by 1.
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2-27 The value of the objective function decreases as the objective function line is moved
