Genetic algorithms for sequencing problems in mixed model assembly lines

2003 ◽  
Vol 45 (4) ◽  
pp. 669-690 ◽  
Author(s):  
S.G Ponnambalam ◽  
P Aravindan ◽  
M Subba Rao
2013 ◽  
Vol 655-657 ◽  
pp. 1675-1681
Author(s):  
Shu Xu ◽  
Fu Ming Li

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.


1996 ◽  
Vol 30 (4) ◽  
pp. 1027-1036 ◽  
Author(s):  
Yow-Yuh Leu ◽  
Lance A. Matheson ◽  
Loren Paul Rees

2012 ◽  
Vol 566 ◽  
pp. 253-256
Author(s):  
Bing Gang Wang

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.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Francesco Costantino ◽  
Alberto Felice De Toni ◽  
Giulio Di Gravio ◽  
Fabio Nonino

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.


Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 201-206 ◽  
Author(s):  
Stefan Keckl ◽  
Wolfgang Kern ◽  
Antoin Abou-Haydar ◽  
Engelbert Westkämper

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