Multi-Objective Optimization of Order Oriented Mixed-Model Assembly Line Sequencing Problem

2013 ◽  
Vol 717 ◽  
pp. 460-465 ◽  
Author(s):  
Zhi Li ◽  
Zhao Liang Jiang ◽  
Yu Mei Liu
Keyword(s):  
Assembly Line ◽  
Mixed Model ◽  
Multi Objective Optimization ◽  
Assembly Lines ◽  
Setup Cost ◽  
Multi Objective ◽  
Large Product ◽  
Product Inventory ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Mixed-model assembly lines are widely used in many manufacturing firms to meet diversified demands of consumers without possessing large product inventories. In this paper, we posed order oriented assembly line sequencing as a multiple-objective optimization problem with the objectives to minimize material consumption waviness, the total setup cost, and finished product inventory cost. The multi-objective optimization algorithm based on non-dominated sorting particle swarm optimization (NSPSO) is designed. Computational experiment has been demonstrated to the applicability of using NSPSO to solve the problem and effectiveness of the proposed approach. By means of this research, the valid solutions for order oriented mixed-model assembly line sequence can be offered to the decision makers effectively.

Multi-Objective Optimization of Multi-Mixed-Model Assembly Lines Sequencing Problem

2013 ◽  
Vol 321-324 ◽  
pp. 2110-2115
Author(s):  
Zhi Li ◽  
Zhao Liang Jiang
Keyword(s):  
Mixed Model ◽  
Decision Makers ◽  
Multi Objective Optimization ◽  
Assembly Lines ◽  
Setup Cost ◽  
Inventory Cost ◽  
Multi Objective ◽  
Mixed Model Assembly Lines ◽  
Product Inventory ◽  
Mixed Model Assembly

Multi-mixed model assembly lines (MMMALs) sequencing cooperative optimization is a typical problem that a variety of products models are assembled in multiple assembly lines. It extends the traditional products sequencing from MMAL to MMMALs. In this paper, we pose products assembly sequencing in multi-mixed model assembly lines (MMMALs) as a multiple-objective optimization problem with the objectives to minimize consumption waviness of each material in the lines, total setup cost and finished product inventory cost. The multi-objective optimization algorithm based on NSGAII is designed. Computational experiment has been demonstrated to the applicability of using NSGAII to solve the problem and effectiveness of the proposed approach. By means of this research, the valid solutions for products assembly sequence can be offered to the decision makers effectively.

scholarly journals Multi-objective optimization of mixed model assembly line balancing in an assemble-to-order industry with stochastic environment

2020 ◽  
Vol 12 (3) ◽  
pp. 90-107
Author(s):  
Ayele Legesse ◽  
Ermias Tesfaye ◽  
Eshetie Berhan
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Mixed Model ◽  
Assembly Line Balancing ◽  
Stochastic Environment ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

The objective of this research is to propose a methodology for multi-objective optimization of a mixed-model assembly line balancing problem with the stochastic environment. To do this a mathematical model representing the problems at hand is developed with objectives of minimizing cycle time and minimization of the number of workstations (which is of Type-E ALB problem). And two optimization meta-heuristics are considered to solve it, namely, Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) and Multi-Objective Genetic Algorithm (MOGA). To test the performance of the algorithms three different size standard problems in Assemble-to-order types of industry are taken and five demand arrival scenarios are considered to incorporate the stochastic nature of the demand arrival for each model in all problems. Both the algorithms are coded and run using MATLAB® 2013a and are compared based on different performance measures. The results indicated that MOGA outperformed NSGA-II in most of the test problems. Nevertheless, both algorithms have resulted in significant improvements in the performance measures in Assemble-to-order types of industry dataset compared to the existing line configuration. Keywords: Assembly Line, Multi-objective optimization, Single model, mixed-model, stochastic environment, Genetic Algorithm

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