The decision-making process associated with the scheduling of burley tobacco harvesting operations was formulated as a multi-stage decision process, and solved using a procedure called dynamic programming. The solution of a stochastic dynamic programming model provides a set of optimal decision rules, that is, a strategy. When certain user-specified parameters are provided, the decision model provides information concerning the optimal date to start harvesting, the optimal number of hours to harvest on each day, the optimal date to introduce hired labor, and the optimal number of workers which should be hired.

The solution of the dynamic programming model makes it possible to compute a timeliness cost which is defined as the amount of the expected total return which is lost because of delaying harvest initiation beyond the optimal starting day. Thus, a decision-maker can consult tabulated strategy solutions in any situation during the harvesting season and make decisions with the aid of timeliness cost information.

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Published in Transactions of the ASAE, v. 22, issue 2, p. 251-259.

© 1979 American Society of Agricultural Engineers

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The research reported in this paper (77-2-39) is in connection with a project of the University of Kentucky Agricultural Experiment Station and is published with the approval of the Director.