A Genetic Algorithm for solving two-sided assembly line balancing problems

Taha, Raghda B.; El-Kharbotly, Amin K.; Sadek, Yomna Mahmoud; Afia, Nahid

doi:10.1016/j.asej.2011.10.003

Cited by 26 publications

(8 citation statements)

References 14 publications

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“…In addition, five heuristic methods are used in the initial population with solutions obtained by population generation method introduced by [13].…”

Section: Enhanced Genetic Algorithm (Ega)mentioning

confidence: 99%

Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm

Anwar

Ali

Awad

2019

SJET

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Assembly line balancing (ALB) is used in many industries to minimize the number of stations, improve the efficiency and work load balance among stations. Enhanced Genetic Algorithm (EGA) is proposed using precedence preservative crossover and scramble mutation techniques, aiming to minimize two objectives; number of stations as a primary objective and smoothing index as a secondary objective. Benchmark problems were selected from the literature, used to test the efficacy of the algorithm and to compare the results with the well-known algorithm SALOME. The results showed high efficacy while handling two objectives. It outperformed SALOME in work load balance and efficiency. On the other, EGA outperformed the Genetic Algorithm (GA) developed by [1] in minimizing the number of stations.

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“…In addition, five heuristic methods are used in the initial population with solutions obtained by population generation method introduced by [13].…”

Section: Enhanced Genetic Algorithm (Ega)mentioning

confidence: 99%

Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm

Anwar

Ali

Awad

2019

SJET

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“…Meta-heuristics have also been presented by Baykasoglu and Dereli (2008), Simaria and Vilarinho (2009), , Ozbakir and Tapkan (2010), Ozcan (2010), Ozbakir and Tapkan (2011), Chutima and Chimklai (2012), and Khorasanian et al (2013). Among these meta-heuristics, studies by Kim et al (2000), Kim et al (2009), Taha et al (2011), Rabbani et al (2012) and Purnomo et al (2013) employed Genetic Algorithm (GA) based approaches to balance two-sided lines. As can be comprehended from these studies, there exist numerous successfully implemented GA based approaches in the literature for two-sided assembly line balancing problems.…”

Section: Etc)mentioning

confidence: 99%

“…Leu et al (1994), Rubinovitz (1995), Kim et al (2000), Rekiek et al (2001), Goncalves (2002), Simaria and Vilarinho (2004), Zhang et al (2005), Haq et al (2006), Levitin et al (2006), Suwannarongsri et al (2007), Zhang et al (2008, Hwang andKatayama (2009), Yu andYin (2010), Chica et al (2011), Akpinar and Bayhan (2011), and . In particular, Kim et al (2000), Kim et al (2009), Taha et al (2011), Rabbani et al (2012, and Purnomo et al PTALBP has not been addressed using any GA based technique. Therefore, there is neither GA based nor evolutionary approach published concerning parallel two-sided assembly lines.…”

Section: Etc)mentioning

confidence: 99%

A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem

Küçükkoç

Zhang

2015

Production Planning & Control

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“…Baykasoglu and Dereli (2008), Simaria and Vilarinho (2009), Ozcan and Toklu (2009b), Ozbakir and Tapkan (2010), Ozcan (2010), Ozbakir and Tapkan (2011), Chutima and Chimklai (2012). Among these meta-heuristics, the studies of Kim et al (2000Kim et al ( , 2009, Taha et al (2011), Purnomo et al (2013), and Rabbani et al (2012) employ different variations of genetic algorithm to balance two-sided lines. As can be comprehended from these studies, there exist numerous genetic algorithm approaches in the literature for two-sided assembly line balancing problems.…”

Section: International Journal Of Production Researchmentioning

confidence: 99%

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Küçükkoç

Zhang

2014

International Journal of Production Research

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Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed by Ozcan et al. (Balancing parallel two-sided assembly lines, International Journal of Production Research, 48 (16), 4767-4784, 2010). The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles explained and discussed.Keywords: mixed-model parallel two-sided assembly lines; assembly line balancing; agent based ant colony optimisation; meta-heuristics; artificial intelligence

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A Genetic Algorithm for solving two-sided assembly line balancing problems

Cited by 26 publications

References 14 publications

Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm

Single Model Assembly Line Balancing Using Enhanced Genetic Algorithm

A mathematical model and genetic algorithm-based approach for parallel two-sided assembly line balancing problem

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

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