Title
Machine scheduling in custom furniture industry through neuro-evolutionary hybridization
Abstract
Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks.
Year
DOI
Venue
2011
10.1016/j.asoc.2010.04.020
Appl. Soft Comput.
Keywords
Field
DocType
critical problem,evolutionary computing,machine scheduling,hybrid approach,machine scheduling optimization,neural network,neural networks,custom manufacturing,manufacturing step,processing time,nsga-ii,multi-objective evolutionary algorithms,custom furniture industry,genetic algorithm ii,inherent problem,neuro-evolutionary hybridization,huge search space,multilayer perceptron neural network,multilayer perceptron,search space,multi objective optimization
Mathematical optimization,Machine scheduling,Evolutionary computation,Sorting,Schedule,Multilayer perceptron neural network,Artificial intelligence,Artificial neural network,Furniture industry,Mathematics,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
11
2
Applied Soft Computing Journal
Citations 
PageRank 
References 
6
0.49
37
Authors
4
Name
Order
Citations
PageRank
Juan C. Vidal110211.58
Manuel Mucientes237835.05
Alberto Bugarín341448.75
Manuel Lama438334.84