• IE interfaces
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• v.17 no.spc
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• pp.20-27
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• 2004

A flexible assembly system (FAS) is a production system that assembles various parts with many constraints and manufacturing flexibilities. This paper presents a new method for efficiently solving the integrated process planning and scheduling in FAS. The two problems of FAS process planning and scheduling are tightly related with each other. However, in almost all the existing researches on FAS, the two problems have been considered separately. In this research, an endosymbiotic evolutionary algorithm is adopted as methodology in order to solve the two problems simultaneously. This paper shows how to apply an endosymbiotic evolutionary algorithm to solving the integrated problem. Some evolutionary schemes are used in the algorithm to promote population diversity and search efficiency. The experimental results are reported.

• 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 Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.1-9
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• 2011

This paper presents a new method that can solve the integrated problem of combined location routing and inventory problem (CLRIP) efficiently. The CLRIP is used to establish facilities from several candidate depots, to find the optimal set of vehicle routes, and to determine the inventory policy in order to minimize the total system cost. We propose a mathematical model for the CLRIP with budget constrained. Because this model is a nonpolynomial (NP) problem, we propose a endosymbiotic evolutionary algorithm (EEA) which is a kind of symbiotic evolutionary algorithm (SEA). The heuristic method is used to obtaining the initial solutions for the EEA. The experimental results show that EEA perform very well compared to the existing heuristic methods with considering inventory control decisions.

• 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 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.