A Discrete State, Continuous Parameter Markov Process Approach to Timber Harvesting Systems Analysis

Abstract

Abstract The introduction of simulation into timber harvesting systems analysis has spawned a proliferation of such models. As a result, the potential for applying stochastic process theory has been largely overlooked. This paper develops the theoretical aspects of a discrete state, continuous parameter Markov process model for application to timber harvesting systems analysis. Erlang and mixed Erlang probability distributions are introduced as models for time-based timber harvesting activities. These families of distributions are used because they offer flexibility in modeling the variety of distributions encountered in the field, yet maintain the integrity of the Markov assumptions. General formulation and solution of the Markov process model is discussed. An application of the theory to the analysis of timber harvesting system interaction is presented. The interaction between a grapple skidder and a mechanical slasher is formulated and solved as a discrete state, continuous parameter Markov process. The analytic solution showed that the system was severely unbalanced, with the skidder delayed 84.5% of the time, the slasher never idle, and both simultaneously performing productive work 15.5% of the time. For. Sci. 34(2):276-291.