Mixed-model assembly line balancing in the make-to-order and stochastic environment using multi-objective evolutionary algorithms

Authors:
Neda Manavizadeh;Masoud Rabbani;Davoud Moshtaghi;Fariborz Jolai
Affiliations:
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 4
Cited 0

Mixed model assembly line design in a make-to-order environment

Computers and Industrial Engineering
Articulating user preferences in many-objective problems by sampling the weighted hypervolume

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Computers and Industrial Engineering
An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	12.05

Visualization

Abstract

The present study introduces a multi-objective genetic algorithm (MOGA) to solve a mixed-model assembly line problem (MMALBP), considering cycle time (CT) and the number of stations simultaneously. A mixed-model assembly line is one capable of producing different types of products to respond to different market demands, while minimizing on capital costs of designing multiple assembly lines. In this research, according to the stochastic environment of production systems, a mixed-model assembly line has been put forth in a make-to-order (MTO) environment. Furthermore, a MOGA approach is presented to solve the corresponding balancing problem and the decision maker is provided with the subsequent answers to pick one based on the specific situation. Finally, a comparison is carried out between six multi-objective evolutionary algorithms (MOEA) so as to determine the best method to solve this specific problem.