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

Manuel Schlenkrich, Wolfgang Seiringer, Klaus Altendorfer and Sophie N. Parragh

Papers from arXiv.org

Abstract: Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts and execution-level friction. This article integrates scenario-based stochastic programming into a rolling horizon framework for capacitated lot sizing, evaluated via discrete-event simulation. We compare this stochastic approach against deterministic optimization and standard Material Requirements Planning (MRP) across varying customer update behaviors, shop loads, and diverse multi-stage topologies (divergent, convergent, and mixed). To accurately capture shop-floor dynamics, the framework introduces a non-anticipativity parameter controlling schedule flexibility, alongside probabilistic setup-time feedback and soft overtime constraints. Results indicate that optimization consistently outperforms MRP. In unbuffered, highly congested settings, stochastic optimization natively smooths workloads and reduces costs by up to 68%. However, introducing explicit safety stocks fundamentally shifts system dynamics: physical buffers effectively absorb shop-floor noise, diminishing the stochastic model's anticipative advantage and enabling deterministic optimization to dominate. Ultimately, this study offers critical managerial insights for aligning planning algorithms, inventory buffering, and schedule flexibility.

Date: 2024-02, Revised 2026-08
New Economics Papers: this item is included in nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2402.14506 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2402.14506

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().