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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018-09-06

Autores

Douglas Alem (Autor) Outra; Eduardo Curcio (Autor) Outra; Pedro Amorim (Autor) FEUP; Bernardo Almada Lobo (Autor) FEUP

Revista

Título: Computers and Operations Research

Vol. 90

Páginas: 125-141

ISSN: 0305-0548

Editora: Elsevier

Indexação

ISI Web of Knowledge - 13 Citações

Scopus - 18 Citações

INSPEC

Outras Informações

ID Authenticus: P-00N-1MX

DOI: 10.1016/j.cor.2017.09.005

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17