Loading in 5 sec....

Linear-Programming ApplicationsPowerPoint Presentation

Linear-Programming Applications

- 246 Views
- Updated On :
- Presentation posted in: General

Linear-Programming Applications. Linear-Programming Applications. Constrained Optimization problems occur frequently in economics: maximizing output from a given budget; or minimizing cost of a set of required outputs. Lagrangian multiplier problems required binding constraints.

**linear programming**real world examples**linear programming**examples in business**linear programming**examples and solutions**linear programming**sample problems**linear programming**real world**applications****linear programming applications**in marketing- companies using
**linear programming** **linear programming**example

Linear-Programming Applications

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Linear-Programming Applications

Constrained Optimization problems occur frequently in economics:

- maximizing output from a given budget;
- or minimizing cost of a set of required outputs.
Lagrangian multiplier problems required binding constraints.

A number of business problems have inequality constraints.

- Constraints of production capacity, time, money, raw materials, budget, space, and other restrictions on choices. These constraints can be viewed as inequality constraints
- A "linear" programming problem assumes a linear objective function, and a series of linear inequality constraints

1. constant prices for outputs (as in a perfectly competitive market).

2.constant returns to scale for production processes.

3.Typically, each decision variable also has a non-negativity constraint. For example, the time spent using a machine cannot be negative.

- Linear programming problems can be solved using graphical techniques, SIMPLEX algorithms using matrices, or using software, such as ForeProfit software.
- In the graphical technique, each inequality constraint is graphed as an equality constraint. The Feasible Solution Space is the area which satisfies all of the inequality constraints.
- The Optimal Feasible Solution occurs along the boundary of the Feasible Solution Space, at the extreme points or corner points.

- The corner point that maximize the objective function is the Optimal Feasible Solution.
- There may be several optimal solutions. Examination of the slope of the objective function and the slopes of the constraints is useful in determining which is the optimal corner point.
- One or more of the constraints may be slack, which means it is not binding.
- Each constraint has an implicit price, the shadow price of the constraint. If a constraint is slack, its shadow price is zero.
- Each shadow price has much the same meaning as a Lagrangian multiplier.

GRAPHICAL

Corner Points

A, B, and C

X1

CONSTRAINT # 1

A

B

Feasible

Region OABC

CONSTRAINT

# 2

C

O

X2

GRAPHICAL

X1

CONSTRAINT # 1

Optimal Feasible

Solution at

Point B

Highest

Profit

Line

A

B

CONSTRAINT

# 2

C

O

X2

- Each linear programming problem (the primal problem) has an associated dual problem.
- EXAMPLE: A maximization of profit objective function, subject to resource constraints has an associated dual problem
- The dual is a minimization of the total costs of the resources subject to constraints that the value of the resources used in producing one unit of each output be at least as great as the profit received from the sale of that output.

- THEOREM: the maximum value of the primal (profit max problem) equals the minimum value of the dual (cost minimization) problem.
- The resource constraints of the primal problem appear in the objective function of the dual problem

Maximize p = P1·Q1 + P2·Q2 subject to:

c·Q1 + d·Q2<R1The budget constraint, for example.

e·Q1 + f·Q2< R2The machine scheduling time constraint.

where Q1 and Q2 > 0 Nonnegativity constraint.

MinimizeC= R1·w1 + R2·w2 subject to:

c·W1 + e·W2>P1Profit Contribution of Product 1

d·W1 + f·W2> P2Profit Contribution of Product 2

where W1 and W2 > 0 Nonnegativity constraint.

- The solutions to primal and dual problems may be solved graphically, so long as this involves two dimensions.
- With many products, the solution involves the SIMPLEX algorithm, or software available in FOREPROFIT

- Multi-plant firms want to produce with the lowest cost across their disparate facilities. Sometimes, the relative efficiencies of the different plants can be exploited to reduce costs.
- A firm may have two mines that produces different qualities of ore. The firm has output requirements in each ore quality.
- Scheduling of hours per week in each mine has the objective of minimizing cost, but achieving the required outputs.

- If one mine is more efficient in all categories of ore, and is less costly to operate, the optimal solution may involve shutting one mine down.
- The dual of this problem involves the shadow prices of the ore constraints. It tells the implicit value of each quality of ore.

- Financial decisions sometimes may be viewed as a linear programming problem.
- EXAMPLE: A financial officer may want to maximize the return on investments available, given a limited amount of money to invest.
- The usual problem in finance is to accept all projects with positive net present values, but sometimes the capital budgets are fixed or limited to create "capital rationing" among projects.

- The solution involves determining what fraction of money allotted should be invested in each of the possible projects or investments.
- In some problems, projects cannot be broken into small parts.
- When this is the case, integer programming can be added to the problem.