Please use this identifier to cite or link to this item:
Title: A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs
Author: Ferrer Biosca, Albert
Guimarans Serrano, Daniel
Ramalhinho Lourenco, Helena  
Juan Pérez, Ángel Alejandro
Others: Universitat Politècnica de Catalunya
National ICT Australia
Universitat Pompeu Fabra
Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Keywords: biased randomization
heuristic algorithms
flow shop
iterated local search
Issue Date: 1-Feb-2016
Publisher: Expert Systems with Applications
Citation: Ferrer Biosca, A., Guimarans, D., Ramalhinho, H. & Juan, A.A. (2016). A BRILS metaheuristic for non-smooth flow-shop problems with failure-risk costs. Expert Systems with Applications, 44(), 177-186. doi: 10.1016/j.eswa.2015.09.011
Published in: Expert Systems with Applications, 2016, 44
Project identifier: info:eu-repo/grantAgreement/MTM2011-29064-C03-02
Also see:
Abstract: This paper analyzes a realistic variant of the Permutation Flow-Shop Problem (PFSP) by considering a non-smooth objective function that takes into account not only the traditional makespan cost but also failure-risk costs due to uninterrupted operation of machines. After completing a literature review on the issue, the paper formulates an original mathematical model to describe this new PFSP variant. Then, a Biased-Randomized Iterated Local Search (BRILS) algorithm is proposed as an efficient solving approach. An oriented (biased) random behavior is introduced in the well-known NEH heuristic to generate an initial solution. From this initial solution, the algorithm is able to generate a large number of alternative good solutions without requiring a complex setting of parameters. The relative simplicity of our approach is particularly useful in the presence of non-smooth objective functions, for which exact optimization methods may fail to reach their full potential. The gains of considering failure-risk costs during the exploration of the solution space are analyzed throughout a series of computational experiments. To promote reproducibility, these experiments are based on a set of traditional benchmark instances. Moreover, the performance of the proposed algorithm is compared against other state-of-the-art metaheuristic approaches, which have been conveniently adapted to consider failure-risk costs during the solving process. The proposed BRILS approach can be easily extended to other combinatorial optimization problems with similar non-smooth objective functions.
Language: English
ISSN: 0957-4174MIAR
Appears in Collections:Articles cientÍfics

Files in This Item:
File Description SizeFormat 
brils.pdf334,51 kBAdobe PDFThumbnail