Cost models for application of Monte Carlo scheduling algorithms of n jobs through m mathines

The n-job, m-machine sequencing problem is a long standing problem in industry. Many methods have been employed in practice, but none of them is generally accepted as the best solution to all problems of this kind. Heuristic methods can be applied when a computer is not available. This research has concentrated on the Monte Carlo methods, Crude and Chain, and cost models which can be minimized during the applications of these methods to actual situations. These applications require a computer except for very small problems. The primary result of this research has been to determine at what point in a series of applications of Monte Carlo calculations the expected gain from obtaining a better sequence is offset by the cost of the calculations. The methods used are general The Rayleigh density function is used to illustrate the optimization method.