BALANCING OF PARTIAL OPTIMA BY MEANS OF A LEARNING MONTE CARLO APPROACH. AN APPLICATION IN UNDERGROUND COAL MINING.
CARNEGIE-MELLON UNIV PITTSBURGH PA MANAGEMENT SCIENCES RESEARCH GROUP
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There are two important problems in underground hard coal mining which have recently been solved short run production planning in the faces and planning of the locomotive traffic in main haulage roads. Mutual independence has usually been assumed for these problems and optima have been computed accordingly. However, this independence is not usually valid so that the resulting optima are not global. Treating both problems simultaneously in all their interrelations is unwieldy and impractical. Here a method is proposed for balancing the partial optima by means of a learning Monte Carlo approach using sets of ratios. An algorithm and results of an application are given. Author
- Mining Engineering
- Operations Research