• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.109-124
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• 2001

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed model assembly lines (MMALs). Line balancing and model sequencing are important for an efficient use of MMALs. The two problems of balancing and sequencing MMALs are tightly related with each other. However, In almost all the existing researches on mixed-model production lines, the two problems have been considered separately. In this research, an endosymbiotic evolutionary a1gorithm, which is a kind of coevolutionary a1gorithm, is adopted as a methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary a1gorithm to solving the integrated problem. Some evolutionary schemes are used In the a1gorithm to promote population diversity and search efficiency. The proposed a1gorithm is compared with the existing evolutionary algorithms in terms of solution quality and convergence speed. The experimental results confirm the effectiveness of our approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.

• Journal of the Korea Safety Management & Science
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• v.6 no.2
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• pp.115-125
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• 2004

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed-model U-lines (MMULs). Balancing and sequencing problems are important for an efficient use of MMULs and are tightly related with each other. However, in almost all the existing researches on mixed-model production lines, the two problems have been considered separately. A genetic algorithm for balancing and sequencing in mixed-model U line is proposed. A presentation method and genetic operators are proposed. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that the proposed algorithm is promising in solution quality.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.3
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• pp.39-55
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• 2012

This paper presents an endosymbiotic evolutionary algorithm (EEA) to solve both problems of line balancing and model sequencing in a mixed-model two-sided assembly line (MMtAL) simultaneously. It is important to have a proper balancing and model sequencing for an efficient operation of MMtAL. EEA imitates the natural evolution process of endosymbionts, which is an extension of existing symbiotic evolutionary algorithms. It provides a proper balance between parallel search with the separated individuals representing partial solutions and integrated search with endosymbionts representing entire solutions. The strategy of localized coevolution and the concept of steady-state genetic algorithms are used to improve the search efficiency. The experimental results reveal that EEA is better than two compared symbiotic evolutionary algorithms as well as a traditional genetic algorithm in solution quality.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.930-935
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• 2002

This paper presents a new method that can efficiently solve the integrated problem of line balancing and model sequencing in mixed-model U-lines (MMULs). Balancing and sequencing problem are important for an efficient use of MMULs and are tightly related with each other. However, in almost all the existing researches on mixed­model production lines, the two problems have been considered separately. In 1his research, an endosymbiotic evolutionary algorithm, which is a kind of evolutionary algorithm, is adopted as a methodology in order to solve the two problems simultaneously. Some evolutionary search capability, rapidity of convergence and population diversity. The proposed algorithm is compared with the existing evolutionary algorithm in terms of solution quality. The experimental results confirm the effectiveness of our approach.

• Journal of the Korea Safety Management & Science
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• v.7 no.3
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• pp.85-96
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• 2005

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.95-108
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• 2013

The problem of constrained sequencing of a set of jobs on a conveyor system with the objective of minimizing setup cost is investigated in this paper. A setup cost is associated with extra material, labor, or energy required due to the change of attributes in consecutive jobs at processing stations. A finite set of attributes is considered in this research. Sequencing is constrained by the availability of conveyor junctions. The problem is motivated by the paint purge reduction problem at a major U.S. automotive manufacturer. We first model a diverging junction with a sequence-independent setup cost and predefined attributes as an assignment problem and this model is then extended for a more general situation by relaxing the initial assumptions in various ways.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.523-534
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• 2005

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

• IE interfaces
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• v.10 no.2
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• pp.51-56
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• 1997

This paper concerns with the problem of mixed model assembly sequencing using neural net. In recent years, because of two characteristics of it, massive parallelism and learning capability, neural nets have emerged to solve the problems for which more conventional computational approaches have proven ineffective. This paper proposes a method using neural net that can consider line balancing and grouping problems simultaneously. In order to solve the mixed model assembly sequencing of the motor industry, this paper uses the modified ART1 algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.193-196
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• 2002

Vehicle routing problem is known to be a NP-hard problem, and is traditionally solved by some heuristic approaches. This paper investigates the application of the computer method COMSOAL to the optimal vehicle routing selection problem. The COMSOAL (Computer Method of Sequencing Operations for Assembly Lines) is a computer heuristic originally developed to solve an assembly line balancing problem a few decades ago. The solution methodology of repeatedly running COMSOAL will result in many feasible solutions from which the best is chosen. This solution approach now becomes viable thanks to the significantly increased speed of recent computer technology. This paper discusses the adaptation of the COMSOAL approach to the known set of simple vehicle routing example problem. The results show that the COMSOAL can be a good possible approach to solve the vehicle routing problem.