OPTIMAL LOCATION OF AND PRODUCTION SCHEDULING FOR A CENTRALIZED COAL PREPARATION PLANT
Special Research Report SR-116 This research applies linear mixed integer programming to optimize the location of centralized preparation plants. The model was developed to aid the small coal producers in overcoming some of their marketing problems. Specifically, an individual small coal producer has marketing difficulties because of his inability to meet quality and quantity speci- fications of a typical long-term contract. It is hypothesized that by pooling the resources of several operators these marketing problems can be overcome. A mechanism to pool these resources is to market the total production through a centralized preparation plant. In this report, a mathematical model of a centralized preparation plant system is developed which considers production capacities, demand requirements, transportation, blending, and preparation plant perform- ance. This model determines (1) the best location of the preparation plant based on minimizing the total discounted cost, (2) the production levels for each mine, (3) the clean coal quality, and (4) the total cost of the system. In addition, the model can be used to aid in the general design and operation of the preparation plant. To simplify the model's use, a matrix generator and report writer are developed. The report writer takes the raw data and generates a file of the data in the format required for input to IBM's MPSX MIP com- puter code, which in turn solves the problem. Since the output from MPSX is very detailed, a report writer is used to extract the important information and prepare a summary report of the optimal solution. To validate the model's logic and its ability to predict trends, the model has been applied to five hypothetical but realistic situations. iv The results of each case study yielded the expected trends for the respective changes.
|Work Title||OPTIMAL LOCATION OF AND PRODUCTION SCHEDULING FOR A CENTRALIZED COAL PREPARATION PLANT|
|License||Attribution-NonCommercial-NoDerivs 3.0 United States|
|Deposited||July 27, 2016|
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