Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach

Al-Hawari, Tarek; Ali, Marwan; Al-Araidah, Omar; Mumani, Ahmad

doi:10.1007/s00170-014-6545-5

Cited by 24 publications

(13 citation statements)

References 9 publications

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“…However, the solutions for the second and the third objective were unsatisfactory. Al-Hawari et al (2015) used a multiple assignment genetic algorithm (MA-GA) to solve multi-objective SALBP. This algorithm assigns tasks to the workstations in three ways: forward, backward and bidirectional assignment.…”

Section: Related Workmentioning

confidence: 99%

“…The mathematical model for SALBP is composed of three objective functions as follow (Al-Hawari et al 2015):…”

Section: Mathematical Modelmentioning

confidence: 99%

“…In this section, we use the proposed MACO to solve some famous test problems (Scholl 1993;Scholl and Klein 2007) with some different cycle times in order to test its performance for multi-objective SALBP, and compare with MOGA (Hwang et al 2008) and MA-GA (Al-Hawari et al 2015).…”

Section: Pheromone Updating Rulementioning

confidence: 99%

“…The proposed MACO has been compared with multi-objective genetic algorithm (MOGA) (Hwang et al 2008) and multiple assignment genetic algorithm (MA-GA) (Al-Hawari et al 2015). All the three algorithms can get optimal solution on the first objective.…”

Section: Introductionmentioning

confidence: 99%

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A modified ant colony optimization algorithm for multi-objective assembly line balancing

Zhong

2016

Soft Comput

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In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.

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Section: Related Workmentioning

confidence: 99%

“…The mathematical model for SALBP is composed of three objective functions as follow (Al-Hawari et al 2015):…”

Section: Mathematical Modelmentioning

confidence: 99%

Section: Pheromone Updating Rulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A modified ant colony optimization algorithm for multi-objective assembly line balancing

Zhong

2016

Soft Comput

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“…In these algorithms, GA is very desirable to solve multi-objective ALBPs because they deal simultaneously with a set of possible solutions which allows to find an entire set of Pareto-optimal solutions in a single run of the algorithm (Atiya et al, 2014). Al-Havari et al (2015) proposed a multiple-assignment GA which presents a new approach of tasks assignment for solving multi-objective single-model ALBPs. Zacharia and Nearchou (2012) introduced a new multi-objective GA for solving the fuzzy simple ALBP-Type 2.…”

Section: Introductionmentioning

confidence: 99%

Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach

Zhong

2016

Concurrent Engineering

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Hull assembly line balancing has significant impact on performance of shipbuilding system and is usually a multi-objective optimization problem. In this article, the primary objectives of the hull assembly line balancing are to minimize the number of workstations, to minimize the static load balancing index, to minimize the dynamic load balancing index between workstations, and to minimize the multi-station-associated complexity. Because this problem comes under combinatorial optimization category and is non-deterministic polynomial-time hard, an improved genetic algorithm simulated annealing is presented. In genetic algorithm simulated annealing, the task sequence numbers are used as chromosomes, and selection, crossover, and mutation operators only deal with the elements of task set instead of the ones of the problem space. In order to prevent the algorithm appearing early convergence or getting local optimal result, the simulated annealing algorithm is used to deal with the individuals. Meanwhile, the algorithm is embedded with the hierarchical scheduling tactics in order to solve the selection problem on optimal solution in the Pareto-optimal set. A number of benchmark problems are solved to prove the superior efficiency of the proposed algorithm. Finally, a case study of the optimization of a hull assembly line was given to illustrate the feasibility and effectiveness of the method.

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Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques

Mirzaei

Nejad²

2022

Engineering Applications of Modern Metaheuristics

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Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach

Cited by 24 publications

References 9 publications

A modified ant colony optimization algorithm for multi-objective assembly line balancing

A modified ant colony optimization algorithm for multi-objective assembly line balancing

Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach

Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques

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