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

• Type: Article in International Scientific Journal
• Date: 2018-09-06

Authors

Douglas Alem (Author) Other; Eduardo Curcio (Author) Other; Pedro Amorim (Author) FEUP; Bernardo Almada Lobo (Author) FEUP

Journal

Title: Computers and Operations Research

Vol. 90

Pages: 125-141

ISSN: 0305-0548

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 18 Citations

INSPEC

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17