# How to formulate Linear Programming model

###### business uses of linear programming models and linear programming model for bank balance sheet management linear programming models characteristics
Dr.NaveenBansal,India,Teacher
Published Date:25-10-2017
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CHAPTER – 2 Linear Programming Models (Resource Allocation Models) 2.1. INTRODUCTION A model, which is used for optimum allocation of scarce or limited resources to competing products or activities under such assumptions as certainty, linearity, fixed technology, and constant profit per unit, is linear programming. Linear Programming is one of the most versatile, powerful and useful techniques for making managerial decisions. Linear programming technique may be used for solving broad range of problems arising in business, government, industry, hospitals, libraries, etc. Whenever we want to allocate the available limited resources for various competing activities for achieving our desired objective, the technique that helps us is LINEAR PROGRAMMING. As a decision making tool, it has demonstrated its value in various fields such as production, finance, marketing, research and development and personnel management. Determination of optimal product mix (a combination of products, which gives maximum profit), transportation schedules, Assignment problem and many more. In this chapter, let us discuss about various types of linear programming models. 2.2. PROPERTIES OF LINEAR PROGRAMMING MODEL Any linear programming model (problem) must have the following properties: (a) The relationship between variables and constraints must be linear. (b) The model must have an objective function. (c) The model must have structural constraints. (d) The model must have non-negativity constraint. Let us consider a product mix problem and see the applicability of the above properties. Example 2.1. A company manufactures two products X and Y, which require, the following resources. The resources are the capacities machine M , M , and M . The available capacities 1 2 3 are 50,25,and 15 hours respectively in the planning period. Product X requires 1 hour of machine M and 1 hour of machine M . Product Y requires 2 hours of machine M , 2 hours of 2 3 1 machine M and 1 hour of machine M . The profit contribution of products X and Y are Rs.5/- 2 3 and Rs.4/- respectively.Linear Programming Models (Resource Allocation Models) 23 23 23 23 23 The contents of the statement of the problem can be summarized as follows: Machines Products Availability in hours XY M 02 50 1 M 12 25 2 M 11 15 3 Profit in Rs. Per unit 5 4 In the above problem, Products X and Y are competing candidates or variables. Machine capacities are available resources. Profit contribution of products X and Y are given. Now let us formulate the model. Let the company manufactures x units of X and y units of Y. As the profit contributions of X and Y are Rs.5/- and Rs. 4/- respectively. The objective of the problem is to maximize the profit Z, hence objective function is: Maximize Z = 5x + 4y OBJECTIVE FUNCTION. This should be done so that the utilization of machine hours by products x and y should not exceed the available capacity. This can be shown as follows: For Machine M 0x + 2y 50 ≤ 1 For Machine M 1x + 2y 25 and LINEAR STRUCTURAL CONSTRAINTS. ≤ 2 For machine M 1x + 1y 15 ≤ 3 But the company can stop production of x and y or can manufacture any amount of x and y. It cannot manufacture negative quantities of x and y. Hence we have write, Both x and y are 0 . NON -NEGATIVITY CONSTRAINT. ≥ As the problem has got objective function, structural constraints, and non-negativity constraints and there exist a linear relationship between the variables and the constraints in the form of inequalities, the problem satisfies the properties of the Linear Programming Problem. 2.2.1. Basic Assumptions The following are some important assumptions made in formulating a linear programming model: 1. It is assumed that the decision maker here is completely certain (i.e., deterministic conditions) regarding all aspects of the situation, i.e., availability of resources, profit contribution of the products, technology, courses of action and their consequences etc. 2. It is assumed that the relationship between variables in the problem and the resources available i.e., constraints of the problem exhibits linearity. Here the term linearity implies proportionality and additivity. This assumption is very useful as it simplifies modeling of the problem. 3. We assume here fixed technology. Fixed technology refers to the fact that the production requirements are fixed during the planning period and will not change in the period. 4. It is assumed that the profit contribution of a product remains constant, irrespective of level of production and sales.24 24 24 Operations Research 24 24 5. It is assumed that the decision variables are continuous. It means that the companies manufacture products in fractional units. For example, company manufacture 2.5 vehicles, 3.2 barrels of oil etc. This is referred too as the assumption of divisibility. 6. It is assumed that only one decision is required for the planning period. This condition shows that the linear programming model is a static model, which implies that the linear programming problem is a single stage decision problem. (Note: Dynamic Programming problem is a multistage decision problem). 7. All variables are restricted to nonnegative values (i.e., their numerical value will be ≥ 0). 2.2.2. Terms Used in Linear Programming Problem Linear programming is a method of obtaining an optimal solution or programme (say, product mix in a production problem), when we have limited resources and a good number of competing candidates to consume the limited resources in certain proportion. The term linear implies the condition of proportionality and additivity. The programme is referred as a course of action covering a specified period of time, say planning period. The manager has to find out the best course of action in the interest of the organization. This best course of action is termed as optimal course of action or optimal solution to the problem. A programme is optimal, when it maximizes or minimizes some measure or criterion of effectiveness, such as profit, sales or costs. The term programming refers to a systematic procedure by which a particular program or plan of action is designed. Programming consists of a series of instructions and computational rules for solving a problem that can be worked out manually or can fed into the computer. In solving linear programming problem, we use a systematic method known as simplex method developed by American mathematician George B. Dantzig in the year 1947. The candidates or activity here refers to number of products or any such items, which need the utilization of available resources in a certain required proportion. The available resources may be of any nature, such as money, area of land, machine hours, and man-hours or materials. But they are limited in availability and which are desired by the activities / products for consumption. 2.2.3. General Linear Programming Problem A general mathematical way of representing a Linear Programming Problem (L.P.P.) is as given below: Z Z Z = = = c c c x x x + + + c c c x x x + … + … + … c c c x x x subjects to the conditions, OBJECTIVE FUNCTION subjects to the conditions, OBJECTIVE FUNCTION subjects to the conditions, OBJECTIVE FUNCTION Z Z = = c c x x + + c c x x + … + … c c x x subjects to the conditions, OBJECTIVE FUNCTION subjects to the conditions, OBJECTIVE FUNCTION 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 n n n n n n n n n n a a a a a x x x x x + + + + + a a a a a x x x x x + + + + + a a a a a x x x x x + …+ a + …+ a + …+ a + …+ a + …+ a x x x x x + …+.. + …+.. + …+.. + …+.. + …+..a a a a a x x x x x ( ( ( ( (≥ ≥ ≥ ≥ ≥, =, , =, , =, , =, , =, ≤ ≤ ≤ ≤ ≤) ) ) ) ) b b b b b 11 11 11 11 11 1 1 1 1 1 12 12 12 12 12 2 2 2 2 2 13 13 13 13 13 3 3 3 3 3 1j 1j 1j 1j 1j jjjjj 1 1 1 1 1n n n n n n n n n n 1 1 1 1 1 a a x x + + a a x x + + a a x x + …………. + + …………. + a a x x + ……+ + ……+ a a x x ( ( ≥ ≥, =, , =, ≤ ≤) ) b b a a a x x x + + + a a a x x x + + + a a a x x x + …………. + + …………. + + …………. + a a a x x x + ……+ + ……+ + ……+ a a a x x x ( ( ( ≥ ≥ ≥, =, , =, , =, ≤ ≤ ≤) ) ) b b b 21 21 21 21 21 1 1 1 1 1 22 22 22 22 22 2 2 2 2 2 23 23 23 23 23 3 3 3 3 3 2 2 2 2 2jjjjj jjjjj 2 2 2 2 2n n n n n n n n n n 2 2 2 2 2 Structural Structural Structural Structural Structural ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… ……………………………………………………………………………………… Constraints Constraints Constraints Constraints Constraints .......................................................................................................... .......................................................................................................... .......................................................................................................... .......................................................................................................... .......................................................................................................... a a a x x x + + + a a a x x x + + + a a a x x x +…+ +…+ +…+ a a a x x x… + … + … + a a a x x x ( ( (≥ ≥ ≥, =, , =, , =, ≤ ≤ ≤ ) ) ) b b b a a x x + + a a x x + + a a x x +…+ +…+ a a x x… + … + a a x x ( (≥ ≥, =, , =, ≤ ≤ ) ) b b m m m m m1 1 1 1 1 1 1 1 1 1 m m m m m2 2 2 2 2 2 2 2 2 2 m m m m m3 3 3 3 3 3 3 3 3 3 mj mj mj mj mj jjjjj mn mn mn mn mn n n n n n m m m m m and all and all and all and all and all x x x x x are = 0 NON NEGETIVITY CONSTRINT are = 0 NON NEGETIVITY CONSTRINT are = 0 NON NEGETIVITY CONSTRINT are = 0 NON NEGETIVITY CONSTRINT are = 0 NON NEGETIVITY CONSTRINT..... jjjjj Where Where jj = 1, 2, 3, … = 1, 2, 3, … n n Where Where Where jjj = 1, 2, 3, … = 1, 2, 3, … = 1, 2, 3, … n n n Where all c s, b s and a s are constants and x s are decision variables. j i ij j , = , are To show the relationship between left hand side and right hand side the symbols ≤ ≥ used. Any one of the signs may appear in real problems. Generally sign is used for maximization ≤Linear Programming Models (Resource Allocation Models) 25 25 25 25 25 problems and sign is used for minimization problems and in some problems, which are known as ≥ mixed problems we may have all the three signs. The word optimize in the above model indicates either maximise or minimize. The linear function, which is to be optimized, is the objective function. The inequality conditions shown are constraints of the problem. Finally all x s should be positive, hence the i non-negativity function. The steps for formulating the linear programming are: 1. Identify the unknown decision variables to be determined and assign symbols to them. 2. Identify all the restrictions or constraints in the problem and express them as linear equations or inequalities of decision variables. 3. Identify the objective or aim and represent it also as a linear function of decision variables. Construct linear programming model for the following problems: 2.3. MAXIMIZATION MODELS Example 2.2. A retail store stocks two types of shirts A and B. These are packed in attractive cardboard boxes. During a week the store can sell a maximum of 400 shirts of type A and a maximum of 300 shirts of type B. The storage capacity, however, is limited to a maximum of 600 of both types combined. Type A shirt fetches a profit of Rs. 2/- per unit and type B a profit of Rs. 5/- per unit. How many of each type the store should stock per week to maximize the total profit? Formulate a mathematical model of the problem. Solution: Here shirts A and B are problem variables. Let the store stock ‘a’ units of A and ‘b’ units of B. As the profit contribution of A and B are Rs.2/- and Rs.5/- respectively, objective function is: Maximize Z = 2a + 5b subjected to condition (s.t.) Structural constraints are, stores can sell 400 units of shirt A and 300 units of shirt B and the storage capacity of both put together is 600 units. Hence the structural constraints are: 1a + 0b ≥ 400 and 0a + 1b 300 for sales capacity and 1a + 1b 600 for storage capacity. ≤ ≤ And non-negativity constraint is both a and b are 0. Hence the model is: ≥ Maximize Z = 2a + 5b s.t. 1a + 0b 400 ≤ 0a + 1b 300 ≤ 1a + 1b 600 and ≤ Both a and b are 0. ≥ Problem 2.3. A ship has three cargo holds, forward, aft and center. The capacity limits are: Forward 2000 tons, 100,000 cubic meters Center 3000 tons, 135,000 cubic meters Aft 1500 tons, 30,000 cubic meters. The following cargoes are offered, the ship owners may accept all or any part of each commodity: Commodity Amount in tons. Volume/ton in cubic meters Profit per ton in Rs. A 6000 60 60 B 4000 50 80 C 2000 25 5026 26 26 Operations Research 26 26 In order to preserve the trim of the ship the weight in each hold must be proportional to the capacity in tons. How should the cargo be distributed so as to maximize profit? Formulate this as linear programming problem. Solution: Problem variables are commodities, A, B, and C. Let the shipping company ship ‘a’ units of A and ‘b’ units of B and ‘c’ units of C. Then Objective function is: Maximize Z = 60a + 80b + 50c s.t. Constraints are: Weight constraint: 6000a + 4000b +2000c ≤ 6,500 ( = 2000+3000+1500) The tonnage of commodity is 6000 and each ton occupies 60 cubic meters, hence there are 100 cubic meters capacity is available. Similarly, availability of commodities B and C, which are having 80 cubic meter capacities each. Hence capacity inequality will be: 100a +80b + 80c ≤ 2,65,000 (= 100,000+135,000+30,000). Hence the l.p.p. Model is: Maximise Z = 60a+80b+50c s.t. 100a = 6000/60 = 100 6000a + 4000b + 2000c ≤ 6,500 80b = 4000/50 = 80 100a+80b+80c ≤ 2,65,000 and 80c = 2000/25 = 80 etc. a,b,c all 0 ≥ 2.4. MINIMIZATION MODELS Problem 2.4. A patient consult a doctor to check up his ill health. Doctor examines him and advises him that he is having deficiency of two vitamins, vitamin A and vitamin D. Doctor advises him to consume vitamin A and D regularly for a period of time so that he can regain his health. Doctor prescribes tonic X and tonic Y, which are having vitamin A, and D in certain proportion. Also advises the patient to consume at least 40 units of vitamin A and 50 units of vitamin Daily. The cost of tonics X and Y and the proportion of vitamin A and D that present in X and Y are given in the table below. Formulate l.p.p. to minimize the cost of tonics. Vitamins Tonics Daily requirement in units. XY A 24 40 D 32 50 Cost in Rs. per unit. 5 3 Solution: Let patient purchase x units of X and y units of Y. Objective function: Minimize Z = 5x + 3y Inequality for vitamin A is 2x + 4y 40 (Here at least word indicates that the patient can ≥ consume more than 40 units but not less than 40 units of vitamin A daily). Similarly the inequality for vitamin D is 3x + 2y 50. ≥ For non–negativity constraint the patient cannot consume negative units. Hence both x and y must be 0. ≥ Now the l.p.p. model for the problem is:Linear Programming Models (Resource Allocation Models) 27 27 27 27 27 Minimize Z = 5x + 3y s.t. 2x + 4y 40 ≥ 3x + 2y 50 and ≥ Both x and y are 0. ≥ Problem 2.5. A machine tool company conducts a job-training programme at a ratio of one for every ten trainees. The training programme lasts for one month. From past experience it has been found that out of 10 trainees hired, only seven complete the programme successfully. (The unsuccessful trainees are released). Trained machinists are also needed for machining. The company's requirement for the next three months is as follows: January: 100 machinists, February: 150 machinists and March: 200 machinists. In addition, the company requires 250 trained machinists by April. There are 130 trained machinists available at the beginning of the year. Pay roll cost per month is: Each trainee Rs. 400/- per month. Each trained machinist (machining or teaching): Rs. 700/- p.m. Each trained machinist who is idle: Rs.500/- p.m. (Labour union forbids ousting trained machinists). Build a l.p.p. for produce the minimum cost hiring and training schedule and meet the company’s requirement. Solution: There are three options for trained machinists as per the data given. (i) A trained machinist can work on machine, (ii) he can teach or (iii) he can remain idle. It is given that the number of trained machinists available for machining is fixed. Hence the unknown decision variables are the number of machinists goes for teaching and those who remain idle for each month. Let, ‘a’ be the trained machinists teaching in the month of January. ‘b’ be the trained machinists idle in the month of January. ‘c’ be the trained machinists for teaching in the month of February. ‘d’ be the trained machinists remain idle in February. ‘e’ be the trained machinists for teaching in March. ‘f ’ be the trained machinists remain idle in the month of March. The constraints can be formulated by the rule that the number of machinists used for (machining + teaching + idle) = Number of trained machinists available at the beginning of the month. For January 100 + 1a + 1b 130 ≥ For February, 150 + 1c + 1d = 130 + 7a (Here 7a indicates that the number of machinist trained is 10 × a = 10a. But only 7 of them are successfully completed the training i.e. 7a). For the month of March, 200 + 1e + 1f 130 + 7a +7c ≥ The requirement of trained machinists in the month of April is 250, the constraints for this will be 130 + 7a + 7c + 7e 250 and the objective function is ≥ Minimize Z = 400 (10a + 10c + 10e) + 700 (1a +1c + 1e) + 400 (1b + 1d +1f) and the non- negativity constraint is a, b, c, d, e, f all 0. The required model is: ≥ Minimize Z = 400 (10a + 10c + 10e) + 700 (1a +1c + 1e) + 400 (1b + 1d + 1f) s.t. 100 + 1a + 1b 130 ≥ 150 + 1c + 1d 130 + 7a ≥28 28 28 Operations Research 28 28 200 + 1e + 1f ≥ 130 + 7a + 7c 130 + 7a + 7c + 7e ≥ 250 and a, b, c, d, e, f all 0. ≥ 2.5. METHODS FOR THE SOLUTION OF A LINEAR PROGRAMMING PROBLEM Linear Programming, is a method of solving the type of problem in which two or more candidates or activities are competing to utilize the available limited resources, with a view to optimize the objective function of the problem. The objective may be to maximize the returns or to minimize the costs. The various methods available to solve the problem are: 1. The Graphical Method when we have two decision variables in the problem. (To deal with more decision variables by graphical method will become complicated, because we have to deal with planes instead of straight lines. Hence in graphical method let us limit ourselves to two variable problems. 2. The Systematic Trial and Error method, where we go on giving various values to variables until we get optimal solution. This method takes too much of time and laborious, hence this method is not discussed here. 3. The Vector method. In this method each decision variable is considered as a vector and principles of vector algebra is used to get the optimal solution. This method is also time consuming, hence it is not discussed here. 4. The Simplex method. When the problem is having more than two decision variables, simplex method is the most powerful method to solve the problem. It has a systematic programme, which can be used to solve the problem. One problem with two variables is solved by using both graphical and simplex method, so as to enable the reader to understand the relationship between the two. 2.5.1. Graphical Method In graphical method, the inequalities (structural constraints) are considered to be equations. This is because; one cannot draw a graph for inequality. Only two variable problems are considered, because we can draw straight lines in two-dimensional plane (X- axis and Y-axis). More over as we have non- negativity constraint in the problem that is all the decision variables must have positive values always the solution to the problem lies in first quadrant of the graph. Some times the value of variables may fall in quadrants other than the first quadrant. In such cases, the line joining the values of the variables must be extended in to the first quadrant. The procedure of the method will be explained in detail while solving a numerical problem. The characteristics of Graphical method are: (i) Generally the method is used to solve the problem, when it involves two decision variables. (ii) For three or more decision variables, the graph deals with planes and requires high imagination to identify the solution area. (iii) Always, the solution to the problem lies in first quadrant. (iv) This method provides a basis for understanding the other methods of solution. Problem 2.6. A company manufactures two products, X and Y by using three machines A, B, and C. Machine A has 4 hours of capacity available during the coming week. Similarly, the available capacity of machines B and C during the coming week is 24 hours and 35 hours respectively. One unit ofLinear Programming Models (Resource Allocation Models) 29 29 29 29 29 product X requires one hour of Machine A, 3 hours of machine B and 10 hours of machine C. Similarly one unit of product Y requires 1 hour, 8 hour and 7 hours of machine A, B and C respectively. When one unit of X is sold in the market, it yields a profit of Rs. 5/- per product and that of Y is Rs. 7/- per unit. Solve the problem by using graphical method to find the optimal product mix. Solution: The details given in the problem is given in the table below: Machines Products Available capacity in hours. (Time required in hours). XY A 11 4 B 38 24 C 10 7 35 Profit per unit in Rs. 5 7 Let the company manufactures x units of X and y units of Y, and then the L.P. model is: Maximise Z = 5x + 7y s.t. 1x + 1y 4 ≤ 3x + 8y 24 ≤ 10x + 7y 35 and ≤ Both x and y are 0. ≥ As we cannot draw graph for inequalities, let us consider them as equations. Maximise Z = 5x + 7y s.t. 1x + 1y = 4 3x + 8y = 24 10x + 7y = 35 and both x and y are 0 ≥ Let us take machine A. and find the boundary conditions. If x = 0, machine A can manufacture 4/1 = 4 units of y. Figure 2.1 Graph for machine A30 30 30 Operations Research 30 30 Similarly, if y = 0, machine A can manufacture 4/1 = 4 units of x. For other machines: Machine B When x = 0 , y = 24/8 = 3 and when y = 0 x = 24/3 = 8 Machine C When x = 0, y = 35/10 = 3.5 and when y = 0, x = 35 / 7 = 5. These values we can plot on a graph, taking product X on x-axis and product Y on y- axis. First let us draw the graph for machine A. In figure 2. 1 we get line 1 which represents 1x + 1y = 4. The point P on Y axis shows that the company can manufacture 4 units of Y only when does not want to manufacture X. Similarly the point Q on X axis shows that the company can manufacture 4 units of X, when does not want to manufacture Y. In fact triangle POQ is the capacity of machine A and the line PQ is the boundary line for capacity of machine A. Similarly figure 2.2 show the Capacity line RS for machine B. and the triangle ROS shows the capacity of machine B i.e., the machine B can manufacture 3 units of product Y alone or 8 units of product X alone. Figure 2.2. Graph for machine B The graph 2.3 shows that the machine C has a capacity to manufacture 5 units of Y alone or 3.5 units of X alone. Line TU is the boundary line and the triangle TOU is the capacity of machine C. The graph is the combined graph for machine A and machine B. Lines PQ and RS intersect at M. The area covered by both the lines indicates the products (X and Y) that can be manufactured by using both machines. This area is the feasible area, which satisfies the conditions of inequalities of machine A and machine B. As X and Y are processed on A and B the number of units that can be manufactured will vary and the there will be some idle capacities on both machines. The idle capacities of machine A and machine B are shown in the figure 2.4.Linear Programming Models (Resource Allocation Models) 31 31 31 31 31 Figure 2.3. Graph for machine C Figure 2.4. Graph of Machines A and B32 32 32 Operations Research 32 32 Figure 2.5 shows the feasible area for all the three machines combined. This is the fact because a products X and Y are complete when they are processed on machine A, B, and C. The area covered by all the three lines PQ. RS, and TU form a closed polygon ROUVW. This polygon is the feasible area for the three machines. This means that all the points on the lines of polygon and any point within the polygon satisfies the inequality conditions of all the three machines. To find the optimal solution, we have two methods. Figure 2.5. Graph for machine A, B and C combined Method 1. Here we find the co-ordinates of corners of the closed polygon ROUVW and substitute the values in the objective function. In maximisaton problem, we select the co-ordinates giving maximum value. And in minimisaton problem, we select the co-ordinates, which gives minimum value. In the problem the co-ordinates of the corners are: R = (0, 3.5), O = (0,0), U = (3.5,0), V = (2.5, 1.5) and W = (1.6,2.4). Substituting these values in objective function: Z = 5 × 0 + 7 × 3.5 = Rs. 24.50, at point R ( 0,3.5) Z = 5 × 0 + 7 × 0 = Rs. 00.00, at point O (0,0) Z = 5 × 3.5 + 7 × 0 = Rs. 17.5 at point U (3.5,0) Z = 5 × 2.5 + 7 × 1.5 = Rs. 23.00 at point V (2.5, 1.5) Z = 5 × 1.6 + 7 × 2.4 = Rs. 24.80 at point W (1.6, 2.4) Hence the optimal solution for the problem is company has to manufacture 1.6 units of product X and 2.4 units of product Y, so that it can earn a maximum profit of Rs. 24.80 in the planning period. Method 2. Isoprofit Line Method: Isoprofit line, a line on the graph drawn as per the objective function, assuming certain profit. On this line any point showing the values of x and y will yield same profit. For example in the given problem, the objective function is Maximise Z = 5x + 7y. If we assume a profit of Rs. 35/-, to get Rs. 35, the company has to manufacture either 7 units of X or 5 units of Y.Linear Programming Models (Resource Allocation Models) 33 33 33 33 33 Hence, we draw line ZZ (preferably dotted line) for 5x + 7y = 35. Then draw parallel line to this line ZZ at origin. The line at origin indicates zero rupees profit. No company will be willing to earn zero rupees profit. Hence slowly move this line away from origin. Each movement shows a certain profit, which is greater than Rs.0.00. While moving it touches corners of the polygon showing certain higher profit. Finally, it touches the farthermost corner covering all the area of the closed polygon. This point where the line passes (farthermost point) is the OPTIMAL SOLUTION of the problem. In the figure 2.6. the line ZZ passing through point W covers the entire area of the polygon, hence it is the point that yields highest profit. Now point W has co-ordinates (1.6, 2.4). Now Optimal profit Z = 5 × 1.6 + 7 × 2.4 = Rs. 24.80. Points to be Noted: (i) In case lsoprofit line passes through more than one point, then it means that the problem has more than one optimal solution, i.e., alternate solutions all giving the same profit. This helps the manager to take a particular solution depending on the demand position in the market. He has options. (ii) If the lsoprofit line passes through single point, it means to say that the problem has unique solution. (iii) If the Isoprofit line coincides any one line of the polygon, then all the points on the line are solutions, yielding the same profit. Hence the problem has innumerable solutions. (iv) If the line do not pass through any point (in case of open polygons), then the problem do not have solution, and we say that the problem is UNBOUND. Figure 2.6. ISO profit line method.34 34 34 Operations Research 34 34 Now let us consider some problems, which are of mathematical interest. Such problems may not exist in real world situation, but they are of mathematical interest and the student can understand the mechanism of graphical solution. Problem 2.7. Solve graphically the given linear programming problem. (Minimization Problem). Minimize Z = 3a + 5b S.T –3a + 4b 12 ≤ 2a – 1b – 2 ≥ 2a + 3b 12 ≥ 1a + 0b 4 ≥ 0a + 1b 2 ≥ And both a and b are 0. ≥ Points to be Noted: (i) In inequality –3a + 4b 12, product/the candidate/activity requires –3 units of ≤ the resource. It does not give any meaning (or by manufacturing the product A the manufacturer can save 3 units of resource No.1 or one has to consume –3 units of A. (All these do not give any meaning as far as the practical problems or real world problems are concerned). (ii) In the second inequality, on the right hand side we have –2. This means that –2 units of resource is available. It is absolutely wrong. Hence in solving a l.p.p. problem, one must see that the right hand side we must have always a positive integer. Hence the inequality is to be multiplied by –1 so that the inequality sign also changes. In the present case it becomes: –2a + 1b 2. ≤ Solution: Now the problem can be written as: Minimize Z = 3a + 5b S.T. When converted into equations they can be written as Min. Z = 3a + 5b S.T. –3a + 4b 12 –3a + 4b = 12 ≤ –2a + 1b 2–2a + 1b = 2 ≤ 2a – 3b 12 2a – 3b = 12 ≥ 1a + 0b 4 1a + 0b = 4 ≤ 0a + 1b 2 and both a and b are ≥ = 0.0a + 1b ≥ 2 and both a and b are ≥ 0. ≥ The lines for inequalities –3a + 4b ≤ 12 and –2a + 1b ≤ 2 starts from quadrant 2 and they are to be extended into quadrant 1. Figure 2.7 shows the graph, with Isocost line. Isocost line is a line, the points on the line gives the same cost in Rupees. We write Isocost line at a far off place, away from the origin by assuming very high cost in objective function. Then we move line parallel towards the origin (in search of least cost) until it passes through a single corner of the closed polygon, which is nearer to the origin, (Unique Solution), or passes through more than one point, which are nearer to the origin (more than one solution) or coincides with a line nearer to the origin and the side of the polygon (innumerable solution). The solution for the problem is the point P (3,2,) and the Minimum cost is Rs. 3 × 3 + 2 × 5 = Rs. 19/-Linear Programming Models (Resource Allocation Models) 35 35 35 35 35 Problem 2.8. The cost of materials A and B is Re.1/- per unit respectively. We have to manufacture an alloy by mixing these to materials. The process of preparing the alloy is carried out on three facilities X, Y and Z. Facilities X and Z are machines, whose capacities are limited. Y is a furnace, where heat treatment takes place and the material must use a minimum given time (even if it uses more than the required, there is no harm). Material A requires 5 hours of machine X and it does not require processing on machine Z. Material B requires 10 hours of machine X and 1 hour of machine Z. Both A and B are to be heat treated at last one hour in furnace Y. The available capacities of X, Y and Z are 50 hours, 1 hour and 4 hours respectively. Find how much of A and B are mixed so as to minimize the cost. Figure 2.7. Graph for the problem 2.7 Solution: The l.p.p. model is: Figure 2.8. Graph for the problem 2.836 36 36 Operations Research 36 36 Minimize Z = 1a + 1b S.T. Equations are: Minimise Z = 1a + 1b S.T 5a + 10b 50, 5a + 10b = 50 ≤ 1a + 1b ≥ 11a + 1b = 1 0a + 1b 4 and both a and b are 0. 0a + 1b = 4 and both a and b are 0. ≤ ≥ ≥ Figure 2.8 shows the graph. Here Isocost line coincides with side of the polygon, i.e., the line MN. Hence the problem has innumerable solutions. Any value on line (1,1) will give same cost. Optimal cost is Re.1/- Problem 2.9. Maximise Z = 0.75 a + 1b S.T. 1a + 1b 0 ≥ –0.5 a + 1b 1 and both a and b are 0. ≤ ≥ Solution: Writing the inequalities as equations, 1a + 1b = 0 i.e., a = b = 1 which is a line passing through origin at 45° 0.5 a + 1 b = 1 and both a and b are 0. Referring to figure 2.9. ≥ The polygon is not closed one i.e., the feasible area is unbound. When Isoprofit line is drawn, it passes through open side of the polygon and it does not coincide with any corner or any line. Hence the line can be moved indefinitely, still containing a part of the feasible area. Thus there is no finite maximum value of Z. That the value of Z can be increased indefinitely. When the value of Z can be increased indefinitely, the problem is said to have an UNBOUND solution. Figure 2.9. Graph for the problem 2.9 Problem 2.10. A company manufactures two products X and Y on two facilities A and B. The data collected by the analyst is presented in the form of inequalities. Find the optimal product mix for maximising the profit. Maximise Z = 6x – 2y S.T. Writing in the equation form: Maximise Z = 6x – 2y S.T. 2x – 1y 22x – 1y = 2 ≤ 1x + 0y 3 and both x and y are 01x + 0y =3 and both x and y are ≥ 0 ≤ ≥Linear Programming Models (Resource Allocation Models) 37 37 37 37 37 th Solution: The straight line for 2x – 1y = 2 starts in 4 quadrant and is to be extended into first quadrant. The polygon is not a closed one and the feasible area is unbound. But when an Isoprofit line is drawn it passes through a corner of the feasible area that is the corner M of the open polygon. The (figure 2.10) coordinates of M are (3, 4) and the maximum Z = Rs. 10/- Figure 2.10. Graph for the problem 2.10 Problem 2.11. A company manufactures two products X and Y. The profit contribution of X and Y are Rs.3/- and Rs. 4/- respectively. The products X and Y require the services of four facilities. The capacities of the four facilities A, B, C, and D are limited and the available capacities in hours are 200 Hrs, 150 Hrs, and 100 Hrs. and 80 hours respectively. Product X requires 5, 3, 5 and 8 hours of facilities A, B, C and D respectively. Similarly the requirement of product Y is 4, 5, 5, and 4 hours respectively on A, B, C and D. Find the optimal product mix to maximise the profit. Solution: Enter the given data in the table below: products Machines X Y Availability in hours. (Time in hours) A 5 4 200 B 3 5 150 C 5 4 100 D 84 80 Profit in Rs. Per unit: 3 4 The inequalities and equations for the above data will be as follows. Let the company manufactures x units of X and y units of Y. (Refer figure 2.11)38 38 38 Operations Research 38 38 Maximise Z 3x + 4y S.T. Maximise Z = 3x + 4y S.T. 5x + 4y 200 5x + 4y = 200 ≤ 3x + 5y 150 3x + 5y = 150 ≤ 5x + 4y 100 5x + 4y = 100 ≤ 8x + 4y 80 8x + 4y = 80 ≤ And both x and y are 0 And both x and y are 0 ≥ ≥ In the graph the line representing the equation 8x + 4y is out side the feasible area and hence it is a redundant equation. It does not affect the solution. The Isoprofit line passes through corner T of the polygon and is the point of maximum profit. Therefore Z = Z = 3 × 32 + 4 × 10 = Rs. 136/. T (32,10) Problem 2.12. This problem is of mathematical interest. Maximise Z = 3a + 4b S.T. Converting and writing in the form of equations, 1a – 1b –1. Maximise Z = 3a + 4b S.T ≤ – 1a + 1b 01a – 1b = 0 ≤ And both a and b are 0–1a + 1b = 0 ≥ And both a and b are 0 ≥ Referring to figure 2.11, the straight line for equation 1 starts in second quadrant and extended in to first quadrant. The line for equation 2 passes through the origin. We see that there is no point, which satisfies both the constraints simultaneously. Hence there is no feasible solution. Given l.p.p. has no feasible solution. Figure 2.11. Graph for the problem 2.11Linear Programming Models (Resource Allocation Models) 39 39 39 39 39 Figure 2.12. Graph for the problem 2.12 Problem 2.13. Solve the l.p.p. by graphical method. Maximise Z = 3a + 2b S.T. 1a + 1b 4 ≤ 1a – 1b 2 and both a and b are 0. ≤ ≥ Solution: The figure 2.13 shows the graph of the equations. Equations are: Maximise Z = 3a + 2b S.T. 1a + 1b = 4 1a – 1b = 2 and both a and b are ≥ 0. In the figure the Isoprofit line passes through the point N (3,1). Hence optimal Profit Z = 3 × 3 + 2 × 1 = Rs.11 /- Figure 2.13. Graph for the problem 2.1340 40 40 Operations Research 40 40 Problem 2.14: Formulate the l.p.p. and solve the below given problem graphically. Old hens can be bought for Rs.2.00 each but young ones costs Rs. 5.00 each. The old hens lay 3 eggs per week and the young ones lay 5 eggs per week. Each egg costs Rs. 0.30. A hen costs Rs.1.00 per week to feed. If the financial constraint is to spend Rs.80.00 per week for hens and the capacity constraint is that total number of hens cannot exceed 20 hens and the objective is to earn a profit more than Rs.6.00 per week, find the optimal combination of hens. Solution: Let x be the number of old hens and y be the number of young hens to be bought. Now the old hens lay 3 eggs and the young one lays 5 eggs per week. Hence total number of eggs one get is 3x + 5y. Total revenues from the sale of eggs per week is Rs. 0.30 (3x + 5y) i.e., 0.90 x + 1.5 y Now the total expenses per week for feeding hens is Re.1 (1x + 1y) i.e., 1x + 1y. Hence the net income = Revenue – Cost = (0.90 x + 1.5 y) – (1 x + 1 y) = – 0.1 x + 0.5 y or 0.5y – 0.1 x. Hence the desired l.p.p. is Maximise Z = 0.5 y – 0.1 × S.T. 2 x + 5 y 80 ≤ 1x + 1y 20 and both x and y are 0 ≤ ≥ The equations are: Maximise Z = 0.5 y – 0.1 × S.T. 2 x + 5 y = 80 1x + 1y = 20 and both x and y are 0 ≥ In the figure 2.13, which shows the graph for the problem, the isoprofit line passes through the point C. Hence Zc = Z (0,16) = Rs.8.00. Hence, one has to buy 16 young hens and his weekly profit will be Rs.8.00 Figure 2.14. Graph for the problem 2.14Linear Programming Models (Resource Allocation Models) 41 41 41 41 41 Point to Note: In case in a graphical solution, after getting the optimal solution, one more constraint is added, we may come across following situations. (i) The feasible area may reduce or increase and the optimal solution point may be shifted depending the shape of the polygon leading to decrease or increase in optimal value of the objective function. (ii) Some times the new line for the new constraint may remain as redundant and imposes no extra restrictions on the feasible area and hence the optimal value will not change. (iii) Depending on the position of line for the new constraint, there may not be any point in the feasible area and hence there may not be a solution. OR the isoprofit line may coincide with a line and the problem may have innumerable number of solutions. SUMMARY 1. The graphical method for solution is used when the problem deals with 2 variables. 2. The inequalities are assumed to be equations. As the problem deals with 2 variables, it is easy to write straight lines as the relationship between the variables and constraints are linear. In case the problem deals with three variables, then instead of lines we have to draw planes and it will become very difficult to visualize the feasible area. 3. If at all there is a feasible solution (feasible area of polygon) exists then, the feasible area region has an important property known as convexity Property in geometry. (Convexity means: Convex polygon consists of a set points having the property that the segment joining any two points in the set is entirely in the convex set. There is a mathematical theorem, which states, “The points which are simulations solutions of a system of inequalities of the type form a polygonal convex set”. ≤ The regions will not have any holes in them, i.e., they are solids and the boundary will not have any breaks. This can be clearly stated that joining any two points in the region also lies in the region. 4. The boundaries of the regions are lines or planes. 5. There will be corners or extreme points on the boundary and there will be edges joining the various corners. The closed figure is known as polygon. 6. The objective function of a maximisation is represented by assuming suitable outcome (revenue) and is known as Isoprofit line. In case of minimisation problem, assuming suitable cost, a line, known as Isocost line, represents the objective function. 7. If isoprofit or isocost line coincides with one corner, then the problem has unique solution. In case it coincides with more than one point, then the problem has alternate solutions. If

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