Heuristic approaches to solve the U-shaped line balancing problem augmented by genetic algorithms

Martinez, U.; Duff, William S.

doi:10.1109/sieds.2004.239976

Cited by 14 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A year later, [20] applied the genetic algorithm (GA), which had been used for solving the SALBP, for solving the UALBP-1. Then, the optimal solutions from previous studies were compared.…”

Section: U-shape Assembly Line Balancing By Using Other Metaheuristicmentioning

confidence: 99%

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

Sresracoo

Kriengkorakot

et al. 2018

MCA

View full text Add to dashboard Cite

The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

show abstract

“…A year later, [20] applied the genetic algorithm (GA), which had been used for solving the SALBP, for solving the UALBP-1. Then, the optimal solutions from previous studies were compared.…”

Section: U-shape Assembly Line Balancing By Using Other Metaheuristicmentioning

confidence: 99%

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

Sresracoo

Kriengkorakot

et al. 2018

MCA

View full text Add to dashboard Cite

show abstract

“…Erel, Sabuncuoglu, and Aksu (2001) proposed a simulated annealing method for solving a UALB problem. Martinez and Duff (2004) proposed heuristic approaches to solve the UALB augmented by GAs. They used 10 task assignment rules.…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic U-shaped assembly line balancing using particle swarm optimization

Nilakantan

Ponnambalam

2015

Engineering Optimization

View full text Add to dashboard Cite

Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

show abstract

“…The proposed hybrid genetic algorithm was found to take a longer computation time, with respect to solution quality. Martinez and Duff (2004) addressed the U-shaped SMALB Type-1 problem. They first solved this problem using 10 heuristic rules adapted from the simple line balancing problem, such as maximum ranked positional weight, maximum total number of follower tasks or precedence tasks and maximum processing time and compared these heuristic solutions with the optimal solutions obtained from previous researches.…”

Section: Genetic Algorithm In Assembly Line Balancingmentioning

confidence: 99%

“…Tests were conducted on a set of randomlygenerated problems to determine the most effective genetic algorithm procedure, based on the best combination of parameters. GALB (SMALB Type-1) Genetic algorithm Ponnambalam et al (2000) SMALB Type-1 Multi-objective genetic algorithm Sabuncuoglu et al (2000) SMALB Genetic algorithm with dynamic partitioning Carnahan et al (2001) SALB Type-2 Ranking heuristic Combinatorial of genetic algorithm Problem space of genetic algorithm (better than the others) Simaria and Vilarinho (2001a;2001b) GALB (MMALB Type-2) Genetic algorithm SMALB Type-2 Simulated annealing Chen et al (2002) GALB (assembly planning Type-2) Genetic algorithm Goncalves and De Almeida 2002SALB Type-1 Hybrid genetic algorithm (combination of heuristic priority rules with genetic algorithm) Miltenburg (2002) GALB (MMALB and sequencing Genetic algorithm simultaneously Type-1) Valente et al (2002) GALB (SMALB Type-2) Genetic algorithm Hui et al (2002) SALBP Fuzzy logic-based system Zha and Lim 2002Intelligent design and planning of manual Neuro-fuzzy assembly workstation Brudaru and Valmar (2004) GALB (SMALB Type-1) Hybrid genetic algorithm (combined branch and bound with genetic algorithm) Martinez and Duff (2004) GALB (U-shape SMALB Type-1) 10 heuristic rules with genetic algorithm Simaria and Vilarinho (2004) GALB (MMALB Type-2) Iterative genetic algorithm based search procedure SALB Type-1 Genetic algorithm SMALB Type-1…”

Section: Genetic Algorithm In Assembly Line Balancingmentioning

confidence: 99%

Soft Computing in Optimizing Assembly Lines Balancing

Matondang¹,

Jambak²

2010

Journal of Computer Science

View full text Add to dashboard Cite

As part of manufacturing systems, the assembly line has become one of the most valuable researches to accomplish the real world problems related to them. Many efforts have been made to seek the best techniques in optimizing assembly lines. Problem statement: Since it was published by Salveson in 1955, some methods and techniques have been developed based on mathematical modeling. In recent years, some researches in Assembly Line Balancing (ALB) have been conducted using Soft Computing (SC) approaches. However, there is no comprehensive survey studies conducted regarding the use of SC in ALB problems, which is became the aim of this study. Approach: This study reviewed published literatures and previous related works that applied SC in solving ALB problems. Main outcomes: This study looks into the suitability of SC approaches in several types of ALB problems. Furthermore, this study provides the classification of ALB problems that can facilitate distinguishing those problems as fields of research. Result: This study found that Genetic Algorithms (GAs) are predominantly applied to solve ALB problems compared to other SC approaches. This high suitability in ALB refers to GAs' main characteristics that include its robustness and flexibility. These SC approaches have mostly been applied to simple ALB problems, which are not problems that are covered in a real complex manufacturing environment. Conclusion/Recommendations: This study recommends that future researches in ALB should be conducted with regard to other issues, beyond the simple ALB problems and more practical to the industries. Besides the advantages of GAs, there are still opportunities to use other SC approaches and the hybrid-systems among them that could increase the suitability of these approaches, especially for multi-objective ALB problems. This study also recommends that human involvement in ALB needs to be considered as a problem factor in ALB

show abstract

Heuristic approaches to solve the U-shaped line balancing problem augmented by genetic algorithms

Cited by 14 publications

References 14 publications

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

Robotic U-shaped assembly line balancing using particle swarm optimization

Soft Computing in Optimizing Assembly Lines Balancing

Contact Info

Product

Resources

About