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Integrated lot sizing and blending problems under demand uncertainty

Grant number: 23/02210-4
Support Opportunities:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): June 01, 2023
Field of knowledge:Engineering - Production Engineering - Operational Research
Principal Investigator:Silvio Alexandre de Araujo
Grantee:Maurício Rocha Gonçalves
Host Institution: Instituto de Biociências, Letras e Ciências Exatas (IBILCE). Universidade Estadual Paulista (UNESP). Campus de São José do Rio Preto. São José do Rio Preto , SP, Brazil
Associated scholarship(s):23/09205-6 - Integrated lot sizing and blending problems under demand uncertainty, BE.EP.DD


This research project deals with problems that appear in different industrial contexts. An important part of industrial production planning comprises solving the lot sizing problem, which consists of determining the decisions regarding the quantities of end products to be produced in each period over a planning horizon of finite and discrete time. In several manufacturing systems, the production process consists of obtaining end products by blending components and, in this context, the blending problem stands, in which, given a set of end products, the aim is to minimize the total cost of obtaining them from the blending of components in order to satisfy all the relevant qualities in the final composition. In this ongoing research project, we address a two-stage stochastic formulation for the lot sizing problem of end products under demand uncertainty, integrated with the blending problem in which the proportion of different components used in the end products can vary. We propose to solve the stochastic problem using approximations based on the \textit{Sample Average Approximation} (SAA) scheme and use the Benders decomposition relying on an optimization package to solve the mixed integer programming problems resulting from the SAA approach. We performed initial computational experiments and in the course of this project we should improve the proposed solution approach, run new computational tests, as well as test other stochastic formulation approaches. (AU)

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