A bi-level optimisation approach for assembly line design using a nested genetic algorithm

Simple genetic algorithm has shortcomings of poor local search ability and premature convergence. To overcome these disadvantages, simulated annealing algorithm which has good local search ability was combined with genetic algorithm to form simulated annealing genetic algorithm. The tests by two commonly used test functions of Shaffer’s F6 and Rosenbrock show that simulated annealing genetic algorithm outperforms the simple genetic algorithm both in convergence rate and convergence quality. Finally, the simulated annealing genetic algorithm was firstly applied in a practical problem of balancing and sequencing design of mixed-model assembly line, once again, the solution results show that simulated annealing genetic algorithm outperforms the simple genetic algorithm. Meanwhile, it provides a new algorithm for solving the design problem of mixed-model assembly line.

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Concurrent Optimization of Product Module Selection and Assembly Line Configuration: A Multi-Objective Approach

Journal of Manufacturing Science and Engineering ◽

10.1115/1.2034513 ◽

2005 ◽

Vol 127 (4) ◽

pp. 875-884 ◽

Cited By ~ 19

Author(s):

Zhonghui Xu ◽

Ming Liang

Keyword(s):

Genetic Algorithm ◽

Assembly Line ◽

Solution Procedure ◽

Programming Approach ◽

Reconfigurable Manufacturing ◽

Multi Objective ◽

Modular Product ◽

Conflicting Objectives ◽

Module Selection ◽

Line Design

Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

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The Industry Assembly Line Layout’s Modeling and Simulation in Industry Manufacturing Based on Genetic Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.675.3 ◽

2013 ◽

Vol 675 ◽

pp. 3-7

Author(s):

Fang Guo ◽

Zhi Hong Huang

Keyword(s):

Genetic Algorithm ◽

Modeling And Simulation ◽

Equilibrium Problem ◽

Assembly Line ◽

Simulation Software ◽

Heuristic Procedure ◽

Plant Simulation ◽

Line Design

The equilibrium problem is one important aspect of industry assembly line design. This paper puts forward the method to solve the industry assembly line’s equilibrium problem based on the genetic algorithm’s heuristic procedure and on this basis it also optimizes the industry assembly line’s layout and synthetically considers the material carrying cost, plant area’s use ratio and other factors in industry manufacturing. Then it optimizes by eM-Plant simulation software and combining with genetic algorithm to efficiently acquire visual and satisfying layout effects. At last, it uses examples of industry assembly line to verify this method’s feasibility.

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