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Guest Post: Making LP Relevant to Students

March 15, 2013

steve harrodDr. Steven Harrod is Assistant Professor of Operations Management at the University of Dayton. He shares a tip on teaching LP today.

It takes some creativity to make linear programming (see Module B in the Heizer/Render text) relevant to students. Here is an activity that offers a discussion of energy, transportation, and air pollution. The topic is coal-burning electric power plants, and it is an example of the blending problem.

Nearly half of all electricity in the U.S. is produced by burning coal, and nearly all of this coal moves by rail. Coal is an organic material that varies considerably in cost, power, and pollution content. Power plants frequently blend different coals to achieve their desired performance. Trains magazine published a detailed article on the movement of coal and its consumption by electric power plants in 2010. The readings and class materials may be downloaded here.

The documents package includes a quiz you may assign to motivate the reading assignment, a longer version of this Guest Post, and a sample spreadsheet model. Start the class discussion by drawing the class’s attention to the power plant at Monroe, Michigan. If you have an overhead projector with internet access, use Google maps to display a satellite photo of the plant. The lakeside plant has a prominent railroad loop and coal storage facility. You may also wish to explain how a power plant converts coal into electricity, and the environment concerns (sulfur causes acid rain and ash must be disposed of).

The challenge question for the students is: what coal should this plant purchase to satisfy energy and pollution limits at minimum cost? The formulated and solved LP leads to an optimal blend of three of the five coal sources. Ask the students, “is this intuitive?” Would you have been able to reach this conclusion without LP? Discuss at length and experiment with reducing or eliminating the pollution limits. This exercise may lead to a lengthy discussion of energy policy, environmental policy, and their joint effect on transportation demand.

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