• Journal of the Korea Safety Management & Science
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• v.9 no.4
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• pp.129-136
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• 2007

This paper considers the sequencing of products in mixed model assembly lines under Just-In-Time (JIT) systems. Under JIT systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. The sequencing problem is solved using Genetic Algorithm Genetic Algorithm is a heuristic method which can provide a near optimal solution in real time. The performance of proposed technique is compared with existing heuristic methods in terms of solution quality. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.515-532
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• 1997

When balancing mixed model assembly lines (MMALs), workload smoothness should be considered on the model-by-model basis as well as on the station-by-station basis. This is because although station-by-station assignments may provide the equality of workload to workers, it causes the utilization of assembly lines to be inefficient due to the model sequences. This paper presents a genetic algorithm to improve the workload smoothness on both the station-by-station and the model-by-model basis in balancing MMALs. Proposed is a function by which the two kinds of workloads smoothness can be evaluated according to the various preferences of line managers. To enhance the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Experimental results show that our algorithm can provide better solutions than existing heuristics. In particular, our algorithm is outstanding on the problems with a larger number of stations or a larger number of tasks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.647-647
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• 1994

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.10a
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• pp.224-231
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• 1994

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.15-34
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• 1994

This paper considers the genetic algorithms(GAs) for the mixed model assembly line sequencing(MMALS) in which the objective is to minimize the overall line length. To apply the GAs to the MMALS, the representation, selection, genetic sequencing operators, and genetic parameters are studied. Especially, the existing sequencing binary operators such as partially map crossover(PMX), cycle crossover(CX), and order crossover (OX) are modified to be suitable for the MMALS, and a new sequencing binary operator called immediate successor relationship crossover (ISR) is introduced. These binary operators mentioned above and/or unary operators such as swap, insertion, inversion, displacement, and splice are compared to find operators which work well in the MMALS. Experimental results indicate that 1) among the binary operators ISR operator is the best, followed by the modified OX, and the modified PMX, with the modified CX being the worst, 2) among the unary operators inversion operator is the best, followed by displacement, swap, and insertion, with splice being the worst, and 3) in general, the unary operators perform better than the binary operators for the MMALS.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.159-168
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• 1992

생산라인의 line balancing이 흐름생산에 있어서 일관된 생산을 하기 위한 필수조건이다. 여러 다양한 제품을 생산하는 printed circuit board(PCB) 공장에서의 line balancing을 얻기 위해서는 mixed model line balancing절차를 설명하고자 한다.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.310-320
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• 1999

A mixed model assembly line (MMAL) is a special type of production line where a variety of product models similar in product characteristics are assembled. Determining the model sequence is an important problem for the efficient use of MMALs. This paper considers interactive multiobjective decision making problems for MMAL sequencing. Evolution program is employed as an underlying framework. In this study, a way of approximating the linear utility function is first studied. To improve its search efficiency to the solution space preferred by a decision maker, some modifications of a standard evolution program are made: operating several subpopulations instead of a single population and merging two or more subpopulations to a single subpopulation, and using a Pareto pool. Extensive computational experiments are carried out to verify the performance of the proposed approach. The computational results show that our approach is promising in solution quality.

• 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.18 no.2
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• pp.136-147
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• 2005

This paper introduces a simulation study regarding the operation of the Painted Body Storage (PBS) in an automobile factory. In the paint shop of the factory, same colored bodies are grouped together in order to increase the effectiveness of process, for example decrease the loss of cleaning the painting-gun when the color of body changes from one to another. However the production of automobiles in the assembly shop is a typical example of the mixed model assembly production. Therefore PBS locates between the paint shop and the assembly shop for control the input sequence of bodies to the assembly shop, and it enables to meet the smoothing requirement of assembly sequence. There are highly restricted constraints on the assembly sequence in a assembly shop. Those are spacing restriction and smoothing restriction. If such restrictions are violated, conveyor-stop or utility work will be necessary. Thus the major objective of PBS is to control the assembly sequence in a way to meet the two restrictions. In this paper a case study of PBS in an automotive factory is introduced. The storage/retrieval algorithms are suggested and the proposed system is verified using simulation models. Sensitivity analysis for operating factors is also conducted.

• IE interfaces
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• v.13 no.2
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• pp.204-210
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• 2000

This paper deals with the problem of mixed-model sequencing on an assembly line. In this sequencing problem we want to minimize the risk of the conveyor stoppage and the total utility work. This paper applies genetic algorithm to solve the mixed-model sequencing problem which is formulated as an integer programming. The solution we get from this algorithm is compared with the solution of Tsai(1995)'s.