The two-dimensional cutting stock problem with usable leftovers and demand uncertainty

• Grant number: 19/25041-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: January 04, 2022
• End date: April 03, 2022
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Adriana Cristina Cherri
• Grantee: Douglas Nogueira Do Nascimento
• Supervisor: José Fernando da Costa Oliveira
• Host Institution: Faculdade de Engenharia (FE). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Institution abroad: Universidade do Porto (UP), Portugal
• Associated to the scholarship: 18/16600-0 - The two-dimensional cutting stock problem with usable leftovers and demand uncertainty, BP.DD

Abstract

In this research project we address the two-dimensional cutting stock with usable leftovers (2D-CSPUL) and demand uncertainty. This problem has great practical importance for many companies, with a strong economic and environmental impact, due to the reduction of waste of raw material. Producing items and leftovers for stock in order to meet a variable and random demand is a very common real situation that involves a complex planning process, in addition to being a situation little explored in the literature. In this context, the objective of this research project is to keep the proposal of developing a matheuristic to solve the 2D-CSPUL with demand uncertainty. It also aims to investigate uncertainty with a focus on the size of the generated usable leftovers. Implementations, computational tests and a scientific paper have been developed. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DO NASCIMENTO, DOUGLAS NOGUEIRA; CHERRI, ADRIANA CRISTINA; OLIVEIRA, JOSE FERNANDO. The two-dimensional cutting stock problem with usable leftovers: mathematical modelling and heuristic approaches. OPERATIONAL RESEARCH, v. 22, n. 5, p. 41-pg., 2022-08-06. (19/25041-8, 18/07240-0, 18/16600-0, 16/01860-1)