Optimizing multi-objective sequencing problem in mixed-model assembly line on just-in-time: particle swarm optimization algorithm

2017 ◽  
Vol 12 (4) ◽  
pp. 288-298 ◽  
Author(s):  
Mohammad Alaghebandha ◽  
Vahid Hajipour ◽  
Mojtaba Hemmati
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Particle Swarm Optimization Algorithm ◽  
Just In Time ◽  
Swarm Optimization ◽  
Sequencing Problem ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

A Non-Dominated Sorting Particle Swarm Optimization Algorithm For Mixed-Model Assembly Line Sequencing Problem

2012 ◽  
Vol 628 ◽  
pp. 451-457
Author(s):  
Zhi Li ◽  
Zhao Liang Jiang ◽  
Wen Ping Liu ◽  
Yu Mei Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Particle Swarm ◽  
Setup Cost ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Mixed-model assembly line (MMAL) sequencing is a typical problem that various models of a common base product are assembled on the same line. In this paper, we proposed products assembly sequencing in mixed-model assembly lines as a multiple-objective optimization problem with the objectives to minimize material consumption waviness, the total setup cost, and total task overlapped time. These three objectives are typically inversely correlated with each other, and simultaneously optimization of the three objectives is challenging. The multi-objective optimization algorithm based on non-dominated sorting particle swarm optimization (NSPSO) is designed. We conduct an extensive experiment study in which the performance of the proposed NSPSO is compared against non-dominated genetic algorithm (NSGA II). The computational results show that the proposed NSPSO outperforms NSGA II, significantly in large-sized problems.

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