• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3863-3869
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• 2009

In recent years, globalization of markets and increased consumer sophistication in automotive industry have led to an increase in the variety of products and a consequent increase in the number of variants of any given product line that a manufacturer must supply. Also, the modularity trend of automotive production systems greatly affects on the production systems of automotive parts suppliers. These typically requires the implementation of cost efficient, flexible production systems, so called mixed-model assembly lines. We studied the problems of mixed-model assembly and assembly error-preventing techniques, and developed the error-preventing monitoring system using RFID in the mixed-model automotive parts assembly line. Especially, this study aimed to solve the problems and supplement the existing systems for preventing assembly errors, by developing a monitoring system using RFID to minimize the issues currently occurring in the field of automotive parts assembly, such as losses from incorrect installation and omission.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8940-8947
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• 2015

Mixed-model assembly line has been widely used in automotive assembly industry to quickly respond the diverse product demands. But, this model can lead to part confusion, which is a source for assembly errors when parts are physically interchangeable in a mixed-model assembly line. With the recent application of new technologies such as radio frequency identification (RFID) and ZigBee wireless sensor network (WSN) to the assembly process, real-time information has become available in this manufacturing systems through IT infrastructures. At first, this paper presents an RFID application for assembly processes, specifically, for a mixed-model assembly line. Thus, to ensure that parts be picked accurately, each cockpit module on the assembly line is attached with a RFID tag and the tag is scanned using a RFID reader and recognizes the vehicle, and each part of the cockpit module is attached with a barcode and the barcode is scanned by a barcode reader and each part is identified correctly for the vehicle. Second, this paper presents a ZigBee wireless sensor network (WSN) protocol-based application for a reconfigurable mixed-model assembly line of cockpit module to reduce the assembly errors and the cost of the change/reconfiguration on the assembly lines due to the various orders and new models from the motor company, avoiding the wiring efforts and inconvenience by wiring between the several RFID devices and the IT server system. Finally, we presents the operation results for several years using this RFID/ZigBee wireless sensor network (WSN) protocol-based cockpit module assembly line.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.13-13
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• 1988

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.34-35
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• 1999

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.1-16
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• 1997

This paper improves the autonomy for supplier's schedule and the flexibility of the final assembly line. The final assembly line is a single work station and each product taking different assembly time is considered. In the assembly schedule, the heuristic method based on the goal chasing method is used. Consequently, suppliers can independently determine their output rates and thus, change their workload pattern according to their needs and priorities. Moreover, this flexibility can help to avoid expensive final-assembly-line stoppages in case of sudden part supply disruptions. The sequencing method can be easily implemented into an existing just-in-time system. In addition, the mathematical model was formulated and the algorithm was explained through the flow chart. The numerical example was given and the efficiency of this method is shown through the analysis of computational results of that example.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.105-116
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• 2018

In this study, we consider the assembly line balancing (ALB) problem which is known as an very important decision dealing with the optimal design of assembly lines. We consider ALB problems with soft constraints which are expected to be fulfilled, however they are not necessarily to be satisfied always and they are difficult to be presented in exact quantitative forms. In previous studies, most researches have dealt with hard constraints which should be satisfied at all time in ALB problems. In this study, we modify the mixed integer programming model of the problem introduced in the existing study where the problem was first considered. Based on the modified model, we propose a new algorithm using the genetic algorithm (GA). In the algorithm, new features like, a mixed initial population selection method composed of the random selection method and the elite solutions of the simple ALB problem, a fitness evaluation method based on achievement ratio are applied. In addition, we select the genetic operators and parameters which are appropriate for the soft assignment constraints through the preliminary tests. From the results of the computational experiments, it is shown that the proposed algorithm generated the solutions with the high achievement ratio of the soft constraints.

• Korean Management Science Review
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• v.13 no.2
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• pp.25-48
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• 1996

Mixed Model Assembly Lines (MMALs) are increasingly used to produce differentiated products on a single assembly line without work-in-process storage, Usually, a typical MMAL consists of a number of (1) stations doing exactly the same operation on every job, (2) stations involving operations with different choices, and (3) stations offering operations that are not performed on every job, or that are performed on every job but with many options. For stations of the first type there is no sequencing problem at all. However, for the second type a set-up cost is incurred each time the operation switches from one choice to another. At the third type of stations, different models, requring different amounts and choices of assembly work, creates an uneven flow of work along the line and variations in the work load at these stations. When a subsequence of jobs requires more work load than the station can handle, it is necessary to help the operations at the station or to complete the work elsewhere. Therefore, a schedule which minimize the sum of set-up cost and utility work cost is desired. So this study has developed Fixed Random Ordering Rule (FROR), Fixed Ascending Ordering Rule (FAOR), Fixed Descending Ordering Rule, and Extended NHR (ENHR). ENHR is to choose optimal color ordering of each batch with NHR, and to decide job sequence of the batch with it, too. As the result of experiments, ENHR was the best heuristic algorithm. NHR is a new heuristic rule in which only the minimum addition of violations from both partial sequence and unassigned sequence at every branch could be considered. And this is a heuristic sequencing rule for the third type of stations at MMAL. This study developed one more heuristic algorithm to test the performance of NHR, which is named as Practical Heuristic Rule (PHR).