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Linear programming is a subject under mathematics that explains optimising operations related to constraints. It is an important technique for finding the optimum resource utilisation. You learn how to maximise or minimise the numerical value of any objective under this subject. This subject consists of linear functions that explain conditions related to linear equations and inequalities. Pay attention to the word carefully: “linear programming” consists of two words: first is linear, and second is programming. Word “linear” means a relationship between degree one and multiple variables is a time. “programming” elaborates on the process of choosing the best solutions from multiple alternatives. Other than mathematics, linear programming is also used in other fields like business, economics, manufacturing and telecommunication.

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**Linear programming**, also known as linear optimisation, maximises or minimises the linear function subject to linear constraints. It deals with a different format of an optimised and feasible region that further solves equations related to the function’s lowest or highest value. Students who learn linear programming often encounter problems involving calculations related to profit and loss. Under linear programming, students got to learn about problems related to optimisation.

A linear programming problem’s key role is finding the optimal solution. Here is some hypothesis that is considered while working with linear programming:

- The number of constraints should be expressed in quantitative terms.
- The linear function, which is also an objective function, needs to be optimised. The relationship between the constraints and the objective function is supposed to be linear.

This subject often demands students’ attention and focus; when students get assigned linear programming assignments, they often seek **programming assignment help**** **because of the toughness of the subject. It is very normal and helpful at the same time to have an expert who can assist you with all your doubts, which can help you to gain good grades.

**Integer programming:**

Programming is a huge field, narrowing it down to integer programming, which is used widely in multiple other sectors than engineering. Under integer programming, both linear and constraint functions act linearly. It is widely used in telecommunications, product scheduling, and cellular networking. Students are asked to make an assignment on this topic to test their knowledge regarding the subject. Due to the lack of proper interpretation of the subject, students are usually unable to finish their assignments on time, which causes a decrease in grades.

**Duality:**

Duality is a unifying theory that helps to lay down a relation between the linear program with the concern of all other linear programs. Indeed, it is a project which includes topics that require a sound knowledge of topics. Professors assign students assignments regarding the subject to sharpen their knowledge related to the subject. Sometimes, students feel like getting assistance from Assignment experts due to the difficulties.

**Pivot operation:**

Pivot plays a vital role in linear programming; it is the element of the matrix that gets selected first by the algorithm, like the simplex algorithm, gaussian elimination, etc. The algorithm picks up the pivot element in the matrix to solve the statistical problems. In matrix algorithms, a pivot entry is usually mandated to be slightly distinguishable from zero in the case of matrix algorithms. The entire process of finding the most distant element is known as pivoting. The pivot is the first element chosen by an algorithm to solve an optimisation problem. Students are given the task of completing the assignment on this subject.

**Activity analysis:**

Activity analysis is one of the popular topics, and students often get an assignment. By definition, the activity of identifying, systematising and expressing the understanding of characteristics and Documentation comes under activity analysis. An activity analysis is accomplished through interviews, group sessions, questionnaires, observations, and reviews of physical records of work. It has multiple uses in real life, making this subject so popular, which is why students often get topics over this subject.

Linear programming is all about finding optimal solutions related to operations research. Linear programming helps researchers when it comes to finding economical solutions to problems. They further get to evaluate the limitations of the subject too. Linear programming techniques have been used in multiple fields for the advancement and efficiency of the process. Let’s see some other fields, including engineering, where linear programming is used.

**Applications in Engineering: **

Linear programming plays a huge role in engineering, as engineers use it to solve manufacturing and design problems. For instance, if you look at airfoil meshes, they use it for aerodynamic shape optimisation, which helps with the reduction of the drag coefficient. From this, you can have an idea of how much linear programming helps engineers with its tools when it comes to shaping optimisation.

**Efficient Manufacturing:**

Under manufacturing, raw materials need to be converted into products to increase the company’s revenue. Every step of the manufacturing process needs to work smoothly to fulfil the production goal. Let’s take the example of getting the desired product; the raw material must go through multiple machines, which will take a certain time. The number of products manufactured could differ because the production will increase. Now to increase productivity and maximise profit, the company uses the linear expression related to the number of raw materials required.

**Energy Industry:**

When it comes to the energy industry, the grid system incorporates renewables and traditional electrical systems, like wind and solar photovoltaics. Generators, distribution lines, transmission, etc., are required to optimise the electric load. Meanwhile, the total distance and electric load between the generation of electricity will be demanded as per time. The costs of sustainability for profits are always observed timely. To optimise the electric power while system designing, linear programming plays a vital role. Also, linear programming takes into consideration optimising the load-matching delivery to the energy industry.

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**Mapping Out The Process: **Mapping your steps regarding your assignment is essential. As we talked about earlier, research work took a lot of time. All you need is to write down how you wanna proceed with your assignment. Start with choosing the topic, place heading and sub-headings, and select the statements you wanna include inside bullet points. Involve data and statistics, which help you prove your statements per your chosen topic. Assign time to each process/part of the assignment; this way, you will not get lost between your project. It also will help in time management.

**Q.1: ****What is a linear Programming Technique? Explain its assumptions related to Linear Programming and accounting data.**

**Answer: **To select an optimum plan, the quantitative selection technique under linear programming is necessary. It’s one of the methods to find the best solution, which contains multiple interactive variables. The subject to various constraints/restrictions is determined with an optimum objective consisting of possible alternatives. The goal is to increase functions like contribution margin to minimise some functions like costs.

Cost factors or contribution margins are used to determine the objective under accounting data to minimise cost and maximise the contribution margin. Here are these key assumptions that persist in two parts. First, one product unit or activity is assumed to be the same for all identical units with the associated contribution margin or cost. Second, for every included unit, the resource input per activity is supposed to be the same. Apart from these two, one more assumption about linear programming is that all relationships and factors are deterministic. The graph of the same could be solved if there are less than two variables involved by the simplex method. A computer’s needs might be essential if there are multiple variables and constraints.

**Q.2: ** **Discuss the components of a simplex tableau. **

**Answer:** Here are some simplex tableau components:

- The coefficients of the objective function are included under the objective row.
- Slack variables will be included inside the variable row containing such variables’ problems.
- There is one row for each constraint, and the problem row contains the coefficient of the variables under constraints. Variables that are not included are assigned zero coefficients.
- The objective column contains the contribution margin to each solution variable and receives further entries at each iteration.
- The first tableau is about slack. The variable column includes the variables that are used to find the solution. The artificial variables are joined in this column since a no-production concern is supposed to have at this point of the iteration process.
- Under the first tableau, the quantity column is included, which contains the values for constraints; it contains the solution mix.
- The objective column includes the total contribution margin. The index row contains values regarding optimum solutions that indicate whether an optimum solution has been reached. The optimum solution is not yet achieved if the table contains any negative number.

**Q.3: ****What is a shadow price? Explain its significance.**

**Answer:** A shadow price is a financial value assigned to an abstract or intangible commodity not traded in the marketplace. Most of the time, it acts as an externality. A shadow price is a result of relaxing a constraint in a contribution margin maximisation problem or cost minimisation problem. A shadow price shows the contribution margin change per unit or cost if a constraint is relaxed. It is an actual economic cost assigned to services and goods, adjusted accordingly after removing distortionary market incorporating an instrumental impact per societal impact. A shadow price is usually estimated on a group of hypotheses and calculations because it lacks steadfast data, so it is impressionistic and somewhat approximate.

The shadow price is the maximum unit variable cost, which increases along with constraining factors. Shadow prices also indicate the possibility of utilising a resource for another purpose. Shadow prices are only useful over a suitable range.

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