Chapter 2. Linear Programming. By Mohammad Shahid Khan M.Eco, MBA, B.Cs, B.Ed. Lecturer in Economics & Business Administration Department of Economics Kardan Institute of Higher Education, Kabul. Introduction to Linear Programming:.
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Min C=3x + 4y
5x + 8y24 ,
x 0 ,
Feasible Solution:A set of values of the variables x1, x2, x3,….,xn which satisfy all the constraints is called the feasible solution of the LPP.
Optimal Solution. A feasible solution that makes the value of the objective function an optimum (maximum or minimum) is called an optimal solution.
Find model appropriate for objective.
Identify the objective function and express it as a linear function of decision variables. It might take the form of maximizing profit or production or minimizing cost.
The last step is to practically implement the solution to the problem for which the model was constructed and optimal value of the decision variable is derived.
i. The linear programming technique helps to make the best possible use of available productive resources (such as time, labour, machines etc.)
Limitations of Linear Programming
(a). Linear programming is applicable only to problems where the constraints and objective function are linear i.e., where they can be expressed as equations which represent straight lines. In real life situations, when constraints or objective functions are not linear, this technique cannot be used.
(b). Factors such as uncertainty, weather conditions etc. are not taken into consideration.
The Regal China Company produces two products daily plates and mugs. The company has limited amounts of two resources used in the production of these products clay and labor. Given these limited resources, the company desires to know how many plates and Mugs to produce each day, in order to Maximize profit. The two products have the following resource requirements for production and profit per item produced (i.e., the model parameters).
Product Labor Clay Profit
(hours/unit) (lbs./unit) (Rs./unit)
Plate 1 4 4
Mug 2 3 5
There are 40 hours of labour and 120 pounds of clay available each day for production.
The objective of the company is to Maximize total profit.
The company's profit is the sum of the individual profits gained from each plate and mug. As such, profits from plates is determine by multiplying the unit profit for each plate, 4, by the number of plates produced, X1. Likewise, profit derived from mugs is the unit profit of a mug, 5, multiplied by the number of mugs produced, X2.
Thus, total profit, Z, can be expressed mathematically as
Maximize Z = 4X1 + 5X2
Z = total profit per day
4X1 = profit from plates ;
5X2 = profit from mugs
By placing the term Maximize in front of the profit function, the relationship expresses the objective of the firm to Maximize total profit.
This problem has two resources used for production, which are limited, labor and clay. Production of plates and mugs require both labor and clay.
For each plate produce, one hour of labor is required. Therefore, the labor used for the production of plates is 1X1 hours. Similarly, each mug requires two hours of labor; the labor used for the production of mugs is 2X2 hours.
Thus, the labor used by the company is the sum of the individual amounts of labor used for each product.
1X1 + 2X2
However, the amount of labor represented "1X1 + 2X2" is limited to 40 hrs per day, thus, the complete labor constraint is
1X1 + 2X2 < 40 hours
The constraint for clay is formulated in the same way as the labor constraint. Since each plate requires four pounds of clay, the amount of clay used daily for the production of plates is 4X1 pounds, and since each mug requires three pounds of clay, the amount of clay used for mugs daily is 3X2. Given that amount of clay available for production each day is 120 pounds, the material constraint can be formulated as
4X1 + 3X2 < 120 pounds
A final restriction is that the number of plates and mugs produced be either zero or a positive value, since it would be impossible to produce negative items. These restrictions are referred to as nonnegative constraints and are expressed mathematically as
X1 > 0, X2 > 0
The complete linear programming model for this problem can now be summarized as
Maximize Z = 4X1 + 5X2
1X1 + 2X2 < 40
4X1 + 3X2 < 120
X1, X2 > 0
Every unit of P1 uses 3 units of resource(R1) and 1 unit of resource(R2) and earns a profit of 5 Afs.
Similarly every unit of P2 uses 2 units of resources (R1) and 2 units of resource (R2) and earns a profit of 4 Afs.
Resources R1 and R2 are available in quantities as 60, 40 respectively.
The firm wants to determine how many of the two products must be manufactured each week in order to maximize profit subject to resource availability.
The Bata Shoe Company has contracted with an advertising firm to determine the types and amount of advertising it should have for its stores. The three types of advertising available are radio and television commercials and newspaper ads. The retail store desires to know the number of each type of advertisement it should purchase in order to Maximize exposure. It is estimated that each ad and commercial will reach the following potential audience
and cost the following amount.
Type of Advertisement Exposure Cost
(people/ad or commercial)
Television commercial 20, 000 Rs. 15, 000
Radio commercial 12, 000 8, 000
Newspaper ad 9, 000 4, 000
The following resource constraints exist:
1. There is a budget limit of Rs. 100,000 available for advertising.
2. The television station has enough time available for four commercials.
3. The radio station has enough time available for ten radio commercials.
4. The newspaper has enough space available for seven ads.
5. The advertising agency has time and staff to produce at most a total of fifteen commercials ads.
This model consists of three decision variables representing the number of each type of advertising
X1 = the number of television commercials
X2 = the number of radio commercials
X3 = the number of newspaper ads
The Objective Function
The objective of this problem is different from the objectives in the previous examples in which only profit was Maximized (or cost minimized). In this problem profit is not Maximized, but rather the audience exposure is Maximized.
This objective function demonstrates that although a linear programming model must either Maximize or Minimize some objective, the objective itself can be in terms of any type of activity or valuation.
For this problem the objective of audience exposure is determined by summing the audience exposure gained from each type of advertising
Maximize Z = 20, 000 X1 + 12, 000 X2 + 9, 000 X3
Z = the total number of audience exposures
20, 000 X1 = the estimated number of exposures from television commercials
12, 000 X2 = the estimated number of exposures from radio commercials
9, 000 X3 = the estimated number of exposures from newspaper ads
The first constraint in this model reflects the limited budget of Rs. 100, 000 allocated for advertisement,
15, 000 X1 + 6, 000 X2 + 4, 000 X3 < 100, 000
15, 000 X1 = the amount spent for television advertising
6, 000 X2 = the amount spent for radio advertising
4, 000 X3 = the amount spent for newspaper advertising
The next three constraints represent the fact that television and radio commercials are limited to four and ten, respectively, while newspaper ads are limited to seven.
X1 < 4 (TV commercials)
X2 < 10 (Radio commercials)
X3 < 7 (Newspaper ads)
The final constraint specifies that the total number of commercials and ads cannot exceed fifteen due to the limitations of the advertising firm:
X1 + X2 + X3 < 15 commercials and ads
Maximize Z = 20, 000 X1 + 12, 000 X2 + 9, 000 X3
15, 000 X1 + 6, 000 X2 + 4, 000 X3 < Rs. 100, 000
X1 < 4
X2 < 10
X3 < 7
X1 + X2 + X3 < 15
X1, X2, X3 > 0
Max π = 24g1 + 8g2
Answer: g1=4, g2=4, π=128
Min C = 120X1 + 60X2
Answer: X1=2, X2=9, C=780