Topics: Problem solving, Inequality, Computer, Binary relation, Supply and demand, Algorithm / Pages: 3 (533 words) / Published: Mar 5th, 2013

The Transportation Problem
(Demand Does Not Equal Supply and Vice Versa)

Linear programming is a mathematical method that is used to apportion resources in as efficient a way as possible. It is used to conserve valuable assets and save money. It is sometimes referred to as LP.

The transportation method of linear programming is the specific application of LP that addresses transportation problems. Solving transportation problems is one of the most common uses of linear programming. It is also one of the most important, particularly in business settings. It often deals with the efficient and cost-effective physical allocation of products. However, they can also deal with things like employee placement. Such problems are sometimes referred to as assignment problems.

The function of the transportation method is not only to ensure that products are shipped cost-effectively. By making sure that products are shipping efficiently, the transportation method also ensures that customer needs are being met and shipping locations are operating at peak productivity.

To this point the transportation method has required that the supply and demand be equal: the rim requirements for the columns. This is unlikely; most real problems are of the so called unbalanced type, where supply and demand are unequal. In such cases there is the inequality.


To be able to produce an Excel Program that can solve problem about Transportation Method in which demand does not equal supply and vice versa.

Scope and Limitation:

The Excel program that is made is not fully automatic operated. This program needs some manual operation in the beginning of the program and some parts upon replying the queries. The program can only calculate with a maximum of; three variance of supply and a dummy, and three variance of demand and a dummy. The program shows the total cost and solution for improvement.

The Program:

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