This lecture covers Exponential smoothing methods that capture level, trend, and seasonal components of time series data. The models decompose time series into level, trend, and seasonal components using multiplicative or additive formulations. Key theoretical principles include recursive updating equations and smoothing parameters that balance historical data with recent observations. Table of Contents: - Aggregate planning problem definition and planning horizon - Demand aggregation across products and time periods - Decision variables: production rate, workforce, inventory, backorders - Constraint formulation: demand satisfaction, capacity, inventory balance - Cost components: production, inventory holding, workforce hiring/firing, backordering - Objective function: total cost minimization
This lecture covers Exponential smoothing methods that capture level, trend, and seasonal components of time series data. The models decompose time series into level, trend, and seasonal components u…