In tropical countries, such as Thailand, the variation of tree moisture content can be significant based on seasonal variations in rainfall. Pulp mill operation optimization accounting for wood moisture variation was used to determine optimal operation conditions and minimize production cost. The optimization models were built using empirical modeling techniques with simulated data from the IDEAS software package. Three case studies were performed. First, a base case of nominal annual operation at a fixed production rate was used to calculate production cost that varies with wood moisture content. The second case is annual optimization where production was allowed to vary monthly over an annual cycle to minimize production cost. For the third case, real-time optimization (RTO) was used to determine optimal production rate with the wood moisture content varying every 3 days. The rolling horizon approach was used to schedule production to keep inventory levels within bounds and with a penalty applied to deviations from the annual expected values of inventory. The advantage of RTO in accounting for moisture content variation was confirmed by annual production costs results simulated for 20 years. These results statistically demonstrated that the overall cost was reduced compared to the second case of monthly production targets.