# Management Science And Linear Programming

Management technology is the use of a scientific approach to handling management problems to be able to help managers make smarter decisions. "1 In this article, I will describe how optimization techniques adding to the management decision making progress, and address advantages and disadvantages of using these techniques and provide some examples of applications of linear encoding to illustrating my conversation.

Management Science can be used in a number of organizations to solve a great deal of problems. The applications of management technology techniques are common, and they have been acknowledged with increasing the efficiency and output of business businesses frequently. Matching to various studies of business, many show that they use management technology techniques, & most are content with the results. Typically for management technology, the situation is studied when we create a model for this, such a model is often a mathematical model. It could be by means of chart or graph, but most frequently a management science model involves a set of mathematical romantic relationships. These relationships are made up of figures and symbols. Once model is made, algorithms are being used to resolve problem. Various techniques are devised to model problem and solve it for possible alternatives.

Linear development is one of the trusted modeling techniques that can solve decision issues with many thousands of factors. Linear encoding models include an objective function and model constraints, which consist of decision variables and parameters. The objective function is a linear numerical relationship that identifies the aim of the firm in conditions of your choice variables. The target function always involves a certain volume of variables, (e. g. , increase the earnings or minimize the price of producing radios). The model constraints are also linear connections of the decision variables found in the objective function. This technique is closely related to linear algebra and uses inequalities in the condition statement alternatively than equalities. A linear encoding problem can fall season in three categories: problems with more than one optimum solution, infeasible problems, and problems with unbounded solutions. Within an optimal solution, the objective function has a distinctive maximum or bare minimum value. An infeasible problem has no feasible solution area; every possible solution point violates one or more constrain. A problem is unbounded if the constraints do not sufficiently restrain the objective function so that for any given possible solution, another feasible solution are available which makes further improvement to the target function. Linear programming problems can be fixed using graphical examination method.

Probabilistic techniques are another school of modeling way for problem solving. These techniques are distinguished from mathematical encoding techniques for the reason that the results are probabilistic. In this system risk means uncertainty for which the probability of distribution is know. Therefore risk diagnosis involves review of the outcomes of decisions along with their probabilities. Probability evaluation tries to load gap between what's know and what need to be know for an maximum solution. Therefore, probabilistic models are being used to prevent occurrences happening because of the adverse uncertainty. Decision analysis and queuing systems are exemplory case of probabilistic techniques. The modeling techniques use to solve physical problems such as vehicles or move of commodities is Network modeling. Network problems are an abstract representation of processes and activities for a given problem and illustrated by using network branches and nodes. This technique uses most affordable way to transport the goods, also to determine maximum or minimum amount possible flow from source to destination and to find shortest critical course in large assignments. 4

By means of one example I am going to gradually check advantages and negatives of linear coding. To begin with it is known that you of the primary advantages of linear programming is that it fits totally with truth, as I am going to see, the example displays this property. Imagine we are running a soccer team and launching a new merchandising plan and we have to decide the amount of scarves and t shirts produced, considering current constraints. The deal prices of t-shirts and scarves are 35 and 10 respectively, also we know the maximum annual production capacity is 2000 units, subsequently four times more time is needed to sew a shirt weighed against a scarf having at most 2300 hours yearly and lastly space is limited up to 2500 square inches wide, requiring shirts and scarves, 3 and 2 square inches respectively. 5 The first gain is the computation facility, as can be checked in the first step where we must model or formulate the challenge. This is an activity where verbal declaration is translated to mathematical statement. The incomes must be maximized knowing different prices of scarves and tops but some restrictions have been established that are called constraints, in cases like this limitations are related to capacity, time and sales space.

Objective function: 35X+10Y

Constraints:

1. capacity x+y2000

2. time 4x+y2300

3. sales space 3x+2y2500

4. nonnegativity constraints x, y0

The maximum profit is in the main point where the second and third constraints intersect one another. Because of this is known that X=420 and Y=620.

To draw the objective function to help make the maximum profit add up to 3000, but this means nothing at all, because we can choose any number, the slope of the lines is the matter that concerns us. If we move parallel the function toward much larger objective function worth, the maximum earnings point will be found when the range will be completely beyond the possible region. It could be ascertained that in frequent occasions some factors are ignored in this sort of problems. Hence the situation is less rigorous and it loses correctness and certainly, that becomes a downside. This example has been resolved by visual method since it has only two variables. It is impossible to solve issues with the graphical method if there tend to be more than two factors. Therefore this is another drawback of linear development: graphical method can be utilized only under motivated conditions. But there still will be more advantages, linear encoding analysis can help both with determining whether management's strategies are possible and in unbounded conditions where in fact the value of the answer is infinitely large, without violating any of the constraints, caution us that the problem is improperly produced. The next step to analyze is what happens whenever we change the beliefs of the objective function or in the constraints. This is another advantage of using linear coding, when can check easily the way the results differ if we change the old coefficients for others. This is called sensitivity analysis, which establishes how changes influence the optimal way to the initial linear development problem.

Linear development is the most used program in the management area and there are several reasons which take us to choose this method solving management problems due to the complexity of the issues that may be handled.

The management technology modeling process helps businesses to improve their operations through the use of scientific methods and the development of specialized techniques. It is the procedure for re searching for an maximum solution to the prevailing problem. Management science modeling process provides organized, analytical and general approaches to the condition handling for decision-making, regardless of the nature of the machine, product, or service. Models are aimed at supporting the decision-maker in decision-making process.

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