A Stochastic Approach to Lot-Sizing Decisions Under Demand Uncertainty

• Grant number: 25/00277-0
• Support Opportunities: Scholarships abroad - Research
• Start date: July 15, 2026
• End date: July 14, 2027
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Diego Jacinto Fiorotto
• Grantee: Diego Jacinto Fiorotto
• Host Investigator: Raf Jans
• Host Institution: Faculdade de Ciências Aplicadas (FCA). Universidade Estadual de Campinas (UNICAMP). Limeira , SP, Brazil
• Institution abroad: École des Hautes Études Commerciales (HEC Montréal), Canada
• Associated research grant: 22/05803-3 - Cutting, packing, lot-sizing, scheduling, routing and location problems and their integration in industrial and logistics settings, AP.TEM

Abstract

This project addresses issues that arise in the context of industrial production planning, involving extensions of the lot sizing problem. In general, within the industrial context, the lot sizing problem involves determining the quantity of products to be produced in each period over a finite time horizon, in order to meet a certain demand and optimize an objective function, such as minimizing costs. With the natural evolution of industrial and logistics decision-making processes, driven, among other factors, by the increasing competitiveness imposed by the globalized market, various strategies have been employed to improve decision-making, making it more complex and aligned with real-world practices. This research aims to develop and implement stochastic approaches for solving the lot-sizing problem in flexible manufacturing systems. Traditional lot-sizing models often assume deterministic demand, limiting their applicability in real-world environments where demand uncertainty is a significant factor. This study introduces probabilistic modeling and two-stage stochastic programming to optimize production planning under uncertainty, focusing on balancing flexibility and cost-efficiency. The research also integrates process flexibility as a decision variable, allowing for more dynamic production configurations. Heuristic methods will be developed to address the computational challenges posed by large-scale problems, ensuring the models are practical for real-world applications. Computational experiments using real-world data will be conducted to assess the performance of the proposed framework, with the ultimate goal of providing manufacturing systems with robust solutions that minimize costs while adapting to variable demand. We note that the activities proposed here are directly related to the activities of the Thematic Project (Process 2022/05803-3), where we are involved as associate researchers, specifically regarding the lot sizing problems (C) and problem integration (G).