dynamic-lot-sizing

Dynamic lot-sizing uses dynamic programming to optimize inventory replenishment for non-stationary systems with time-varying demand, setup costs, and holding costs. It calculates optimal order quantities across a finite horizon to minimize total inventory costs.

Dynamic lot sizing solves finite-horizon inventory problems using dynamic programming when parameters are non-stationary. You need it when facing seasonal demand, fluctuating supplier prices, or varying holding and setup costs that make static models inadequate.

Yes, price speculation analysis identifies opportunities to buy inventory ahead of anticipated price increases. It evaluates future cost changes against holding expenses to determine if提前 purchasing reduces total replenishment costs.

Rolling horizon simulation models inventory planning with regular replanning intervals to adapt to changing forecasts. It applies dynamic programming repeatedly across moving time windows to update optimal replenishment decisions as new demand information arrives.

Yes, the dynamic lot-sizing implementation depends on numpy and pandas for data handling, scipy for optimization computations, and matplotlib for visualizing inventory results. These libraries provide the computational framework for dynamic programming and rolling horizon simulation.

Dynamic programming for finite-horizon lot-sizing requires known or forecasted time-varying parameters across the planning horizon. It may not handle highly stochastic demand well, and rolling horizon simulation helps adapt to forecast updates but adds computational overhead.