Linear programming manzoor nabi
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The document discusses linear programming, a mathematical modeling technique for optimizing resource allocation in various business contexts. Key elements include limited resources, objectives, linearity, homogeneity, and the necessity of constraints, alongside the formulation of the decision variables. An example problem illustrates the application of linear programming to maximize profits while adhering to constraints related to manufacturing processes.
Linear programming manzoor nabi
- 1. By Mr. Vikram Singh Department Forestry and Agriculture Linear Programming
- 2. Topic :- Linear programming Name :- Manzoor Nabi Roll No:- 04 Course:- Forestry 4th sem Year:- 2014-15
- 3. Introduction Linear programming is a widely used mathematical modeling technique to determine the optimum allocation of scarce resources among competing demands. Resources typically include raw materials, manpower, machinery, time, money and space. The technique is very powerful and found especially useful because of its application to many different types of real business problems in areas like finance, production, sales and distribution, personnel, marketing and many more areas of management. As its name implies, the linear programming model consists of linear objectives and linear constraints, which means that the variables in a model have a proportionate relationship. For example, an increase in manpower resource will result in an increase in work output.
- 4. ESSENTIALS OF LINEAR PROGRAMMING MODEL For a given problem situation, there are certain essential conditions that need to be solved by using linear programming. 1. Limited resources : limited number of labour, material equipment and finance 2. Objective : refers to the aim to optimize (maximize the profits or minimize the costs). 3. Linearity : increase in labour input will have a proportionate increase in output. 4. Homogeneity : the products, workers' efficiency, and machines are assumed to be identical. 5. Divisibility : it is assumed that resources and products can be divided into fractions. (in case the fractions are not possible, like production of one-third of a computer, a modification of linear programming called integer programming can be used).
- 5. PROPERTIES OF LINEAR PROGRAMMING MODEL The following properties form the linear programming model: 1. Relationship among decision variables must be linear in nature. 2. A model must have an objective function. 3. Resource constraints are essential. 4. A model must have a non-negativity constraint.
- 6. FORMULATION OF LINEAR PROGRAMMING Formulation of linear programming is the representation of problem situation in a mathematical form. It involves well defined decision variables, with an objective function and set of constraints. Objective function: The objective of the problem is identified and converted into a suitable objective function. The objective function represents the aim or goal of the system (i.e., decision variables) which has to be determined from the problem. Generally, the objective in most cases will be either to maximize resources or profits or, to minimize the cost or time. Constraints: When the availability of resources are in surplus, there will be no problem in making decisions. But in real life, organizations normally have scarce resources within which the job has to be performed in the most effective way. Therefore, problem situations are within confined limits in which the optimal solution to the problem must be found. Non-negativity constraint Negative values of physical quantities are impossible, like producing negative number of chairs, tables, etc., so it is necessary to include the element of non-negativity as a constraint
- 7. GENERAL LINEAR PROGRAMMING MODEL A general representation of LP model is given as follows: Maximize or Minimize, Z = p1 x1 + p2 x2 ………………pn xn Subject to constraints, w11 x1 + w12 x2 + ………………w1n xn ≤ or = or ≥ w1 ……………(i) w21 x1 + w22 x2 ………………w2n xn ≤ or = or ≥ w2 …………… (ii) . . . . . . . . . . . . wm1 x1 + wm2 x2 +………………wmn xn ≤ or = ≥ wm …………(iii) Non-negativity constraint, xi ≥ o (where i = 1,2,3 …..n)
- 8. Example A company manufactures two types of boxes, corrugated and ordinary cartons. The boxes undergo two major processes: cutting and pinning operations. The profits per unit are Rs. 6 and Rs. 4 respectively. Each corrugated box requires 2 minutes for cutting and 3 minutes for pinning operation, whereas each carton box requires 2 minutes for cutting and 1 minute for pinning. The available operating time is 120 minutes and 60 minutes for cutting and pinning machines. The manager has to determine the optimum quantities to be manufacture the two boxes to maximize the profits.
- 9. Solution Decision variables completely describe the decisions to be made (in this case, by Manager). Manager must decide how many corrugated and ordinary cartons should be manufactured each week. With this in mind, he has to define: xl be the number of corrugated boxes to be manufactured. x2 be the number of carton boxes to be manufactured Objective function is the function of the decision variables that the decision maker wants to maximize (revenue or profit) or minimize (costs). Manager can concentrate on maximizing the total weekly profit (z). Here profit equals to (weekly revenues) – (raw material purchase cost) – (other variable costs). Hence Manager’s objective function is: Maximize z = 6X2 + 4x2 Constraints show the restrictions on the values of the decision variables. Without constraints manager could make a large profit by choosing decision variables to be very large. Here there are three constraints: Available machine-hours for each machine Time consumed by each product Sign restrictions are added if the decision variables can only assume nonnegative values (Manager can not use negative negative number machine and time never negative number )
- 10. All these characteristics explored above give the following Linear Programming (LP) problem max z = 6x1 + 4x2 (The Objective function) s.t. 2x1 + 3x2 ≤ 120 (cutting timeconstraint) 2x1 + x2 ≤ 60 (pinning constraint) x1, x2 ≥ 0 (Sign restrictions) A value of (x1,x2) is in the feasible region if it satisfies all the constraints and sign restrictions. This type of linear programming can be solve by two methods 1) Graphical method 2) Simplex algorithm method
- 11. Example Previous the packaging product mix problem is solved using simplex method. Maximize Z = 6x1 + 4x2 Subject to constraints, 2x1+3x2≤120 (Cutting machine) .....................(i) 2x1+ x2≤ 60 (Pinning machine) ......................(ii) where x1, x2 ≥ 0 Considering the constraint for cutting machine, 2x1+ 3x2 ≤ 120 To convert this inequality constraint into an equation, introduce a slack variable, S3 which represents the unused resources. Introducing the slack variable, we have the equation 2x1+ 3x2 + S3 = 120 Similarly for pinning machine, the equation is 2x1+ x2 + S4 = 60
- 12. Example cont…. If variables x1 and x2 are equated to zero, i.e., x1 = 0 and x2 = 0, then S3 = 120 S4 = 60 This is the basic solution of the system, and variables S3 and S4 are known as Basic Variables, SB while x1 and x2 known as Non-Basic Variables. If all the variables are non negative, a basic feasible solution of a linear programming problem is called a Basic Feasible Solution.
- 13. Cont…. Rewriting the constraints with slack variables gives us, Zmax = 6x1 + 4x2 + 0S3 + 0S4 Subject to constraints, 2x1 + 3x2 + S3 = 120 ....................(i) 2x1 + x2 + S4 = 60 ....................(ii) where x1, x2 ≥ 0 Which can shown in following simplex table form
- 14. Cont… If the objective of the given problem is a maximization one, enter the co-efficient of the objective function Zj with opposite sign as shown in table. Take the most negative coefficient of the objective function and that is the key column Kc. In this case, it is -6. Find the ratio between the solution value and the key column coefficient and enter it in the minimum ratio column. The intersecting coefficients of the key column and key row are called the pivotal element i.e. 2. The variable corresponding to the key column is the entering element of the next iteration table and the corresponding variable of the key row is the leaving element of the next iteration table (In other words, x1 replaces S4 in the next iteration table. Given indicates the key column, key row and the pivotal element.)
- 15. Cont.. In the next iteration, enter the basic variables by eliminating the leaving variable (i.e., key row) and introducing the entering variable (i.e., key column). Make the pivotal element as 1 and enter the values of other elements in that row accordingly. In this case, convert the pivotal element value 2 as 1 in the next iteration table. For this, divide the pivotal element by 2. Similarly divide the other elements in that row by 2. The equation is S4/2. This row is called as Pivotal Equation Row Pe. The other co-efficients of the key column in iteration Table 5.4 must be made as zero in the iteration Table 5.5. For this, a solver, Q, is formed for easy calculation.
- 16. Cont… Solver, Q = SB + (–Kc * Pe) The equations for the variables in the iteration number 1 of table 8 are, For S3 Q = SB + (– Kc * Pe) = S3 + (–2x Pe) = S3 – 2Pe …………………………(i) For – Z , Q = SB + (– Kc * Pe) = – Z + ((– 6) * Pe) = – Z + 6Pe …………………………(ii) Using the equations (i) and (ii) the values of S3 and –Z for the values of Table 1 are found as shown in Table 5.4
- 17. Cont…. Using these equations, enter the values of basic variables SB and objective function Z. If all the values in the objective function are non-negative, the solution is optimal. Here, we have one negative value – 1. Repeat the steps to find the key row and pivotal equation values for the iteration 2 and check for optimality. We get New Table as below:
- 18. Cont…. The solution is, x1 = 15 corrugated boxes are to be produced and x2 = 30 carton boxes are to be produced to yield a Profit, Zmax = Rs. 210.00
- 19. Cont…. It can be solve in different kinds of software as Tora, EXCEL, Lindo, etc. In Tora As follows, Steps for shoving linear programming by graphic method using Tora shoftware Step 1 Start Tora select linear programming