Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation

TL;DR

This paper demonstrates that stochastic optimization within a rolling horizon framework, evaluated through simulation, improves production planning under demand uncertainty compared to deterministic methods and traditional MRP.

Contribution

It introduces a scenario-based stochastic programming approach integrated with simulation for improved production planning under demand uncertainty.

Findings

Stochastic optimization outperforms deterministic models and MRP in cost efficiency.

Stochastic approach is especially advantageous under tight resource constraints and high demand uncertainty.

Simulation confirms the robustness of stochastic optimization in dynamic production environments.

Abstract

Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts. Therefore, this article focuses on the integration of updated customer demand into the rolling horizon planning cycle. We use scenario-based stochastic programming to solve capacitated lot sizing problems under stochastic demand in a rolling horizon environment. This environment is replicated using a discrete event simulation-optimization framework, where the optimization problem is periodically solved, leveraging the latest demand information to continually adjust the production plan. We evaluate the stochastic optimization approach and compare its performance to solving a deterministic lot sizing model, using expected demand figures as input, as well as to standard Material Requirements Planning (MRP). In the simulation study, we analyze three different customer behaviors related to forecasting, along with four levels of shop load, within a multi-item and multi-stage production system. We test a range of significant parameter values for the three planning methods and compute the overall costs to benchmark them. The results show that the production plans obtained by MRP are outperformed by deterministic and stochastic optimization. Particularly, when facing tight resource restrictions and rising uncertainty in customer demand, the use of stochastic optimization becomes preferable compared to deterministic optimization.