A computational study of the general lot-sizing and scheduling model under demand uncertainty via robust and stochastic approaches

Author(s)

Alem, Douglas ^[1] ; Curcio, Eduardo ^[2] ; Amorim, Pedro ^[2] ; Almada-Lobo, Bernardo ^[2]

Affiliation

^[1] Univ Fed Sao Carlos, Rodovia Joao Leme dos Santos, SP-264, Km 110, Bairro Do Itinga, Sorocaba - Brazil

^[2] Univ Porto, INESC TEC, Fac Engn, Rua Dr Roberto Frias, P-4200465 Oporto - Portugal

• Document type: Journal article
• Source: Computers & Operations Research; v. 90, p. 125-141, FEB 2018.
• Web of Science Citations: 7

Abstract

This paper presents an empirical assessment of the General Lot-Sizing and Scheduling Problem (GLSP) under demand uncertainty by means of a budget-uncertainty set robust optimization and a two-stage stochastic programming with recourse model. We have also developed a systematic procedure based on Monte Carlo simulation to compare both models in terms of protection against uncertainty and computational tractability. The extensive computational experiments cover different instances characteristics, a considerable number of combinations between budgets of uncertainty and variability levels for the robust optimization model, as well as an increasing number of scenarios and probability distribution functions for the stochastic programming model. Furthermore, we have devised some guidelines for decision-makers to evaluate a priori the most suitable uncertainty modeling approach according to their preferences. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 15/26453-7 - Humanitarian supply chain: models and solution methods
• Grantee: Douglas José Alem Junior
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/08303-2 - Operations planning via stochastic programming and robust optimization
• Grantee: Douglas José Alem Junior
• Support Opportunities: Regular Research Grants