Genetic algorithms for sequencing problems in mixed model assembly lines

On the base of summarizing and contrasting the objectives of sequencing problem in mixed model assembly lines (MMAL) , and in consideration of the influence sequence-dependent setup times , a objective is proposed to minimize the total unfinished works and idle times over all jobs and stations . And the corresponding model is presented. To solve this model, a modified genetic algorithm is proposed to determine suitable sequences. Comparing with the Lingo 9 software, the proposed GA turns out to have a good ability to solve the sequencing problems.

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Simultaneous solving of balancing and sequencing problems with station-dependent assembly times for mixed-model assembly lines

Applied Soft Computing ◽

10.1016/j.asoc.2011.11.027 ◽

2012 ◽

Vol 12 (4) ◽

pp. 1359-1370 ◽

Cited By ~ 42

Author(s):

H. Mosadegh ◽

M. Zandieh ◽

S.M.T. Fatemi Ghomi

Keyword(s):

Mixed Model ◽

Assembly Lines ◽

Mixed Model Assembly Lines ◽

Sequencing Problems ◽

Mixed Model Assembly

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Sequencing mixed-model assembly lines with genetic algorithms

Computers & Industrial Engineering ◽

10.1016/0360-8352(96)00050-2 ◽

1996 ◽

Vol 30 (4) ◽

pp. 1027-1036 ◽

Cited By ~ 39

Author(s):

Yow-Yuh Leu ◽

Lance A. Matheson ◽

Loren Paul Rees

Keyword(s):

Genetic Algorithms ◽

Mixed Model ◽

Assembly Lines ◽

Mixed Model Assembly Lines ◽

Mixed Model Assembly

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Sequencing Mixed-Model Assembly Lines to Minimize the Variation of Parts Consumption by Hybrid Genetic Algorithms

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.566.253 ◽

2012 ◽

Vol 566 ◽

pp. 253-256

Author(s):

Bing Gang Wang

Keyword(s):

Genetic Algorithm ◽

Mixed Model ◽

Hybrid Genetic Algorithm ◽

Initial Population ◽

Assembly Lines ◽

Mixed Model Assembly Lines ◽

Mutation Operators ◽

Sequencing Problems ◽

Crossover And Mutation ◽

Mixed Model Assembly

This paper is concerned about the sequencing problems in mixed-model assembly lines. The optimization objective is to minimizing the variation of parts consumption. The mathematical models are put forward. Since the problem is NP-hard, a hybrid genetic algorithm is newly-designed for solving the models. In this algorithm, the new method of forming the initial population is presented, the hybrid crossover and mutation operators are adopted, and moreover, the adaptive probability values for performing the crossover and mutation operations are used. The optimization performance is compared between the hybrid genetic algorithm and a genetic algorithm proposed in early published literature. The computational results show that satisfactory solutions can be obtained by the hybrid genetic algorithm and it performs better in terms of solution’s quality.

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Scheduling Mixed-Model Production on Multiple Assembly Lines with Shared Resources Using Genetic Algorithms: The Case Study of a Motorbike Company

Advances in Decision Sciences ◽

10.1155/2014/874031 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Francesco Costantino ◽

Alberto Felice De Toni ◽

Giulio Di Gravio ◽

Fabio Nonino

Keyword(s):

Genetic Algorithms ◽

Manufacturing Systems ◽

Mixed Model ◽

Complex Model ◽

Scheduling Problems ◽

Assembly Lines ◽

Shared Resources ◽

Final Assembly ◽

Mixed Model Assembly Lines ◽

Mixed Model Assembly

The authors deal with the topic of the final assembly scheduling realized by the use of genetic algorithms (GAs). The objective of the research was to study in depth the use of GA for scheduling mixed-model assembly lines and to propose a model able to produce feasible solutions also according to the particular requirements of an important Italian motorbike company, as well as to capture the results of this change in terms of better operational performances. The “chessboard shifting” of work teams among the mixed-model assembly lines of the selected company makes the scheduling problem more complex. Therefore, a complex model for scheduling is required. We propose an application of the GAs in order to test their effectiveness to real scheduling problems. The high quality of the final assembly plans with high adherence to the delivery date, obtained in a short elaboration time, confirms that the choice was right and suggests the use of GAs in other complex manufacturing systems.

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