Work

Rolling horizon data driven robust optimization for supply chain planning

Year: 2025

Type: article

Abstract: This paper discusses the challenge of production planning in a dyadic supply chain, where uncertainties disrupt plans that are updated through a rolling horizon DRP process. These uncertainties, such as demand fluctuations, machine failures, and delivery delays, cause instability and worsen the bullwhip effect, which reduces the reliability and effectiveness of production plans. To address these challenges, we propose a data-driven robust optimization framework that uses historical data analysis and clustering techniques to create well-defined uncertainty sets. Numerical experiments, using simulated historical data, show that this approach improves supply chain planning by balancing precision and robustness in managing uncertainties.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.09.062

Authors: Walid Khellaf, Romain Guillaume

Institutions: IMT Mines Albi, Centre de Génie Industriel, Université de Toulouse, Institut de Recherche en Informatique de Toulouse, Université Toulouse - Jean Jaurès

Language: English

Topic: Supply Chain and Inventory Management

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Agence Nationale de la Recherche