• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.15-17
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• 2022

In order to minimize surplus parts in ball bearing selective assembly systems, it is necessary to optimize the selection probability by grasping the dimensional distribution of each part. But the use of a complex system causes delays in the production process. In this paper, we propose cluster priority selection algorithm that can quickly and simply determine the selection priority in ball bearing selective assembly system. In addition, we assume the simulated situation with the data collected in the actual ball bearing selective assembly process, and evaluate the incidence of surplus part and runtime by simulating the cluster priority selection algorithm and the exiting algorithm. As a result of the simulation, the cluster priority selection algorithm generated 83.8% less surplus parts, and 39.7% less runtime than the existing algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.693-698
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• 1991

This paper describes a fuzzy rule-based assembly algorithm for precision parts mating, The difficulties in devising reliable assembly strategies result from the complexity of the assembly process and the uncertainty such as imperfect knowledge of the parts being assembled as well as the limitations of the devices performing the assembly. To cope with above problems, we propose an assembly algorithm utilizing fuzzy set theory. The presented method allows us to represent the uncertainty by using fuzzy membership function and treat nonlinear sapping from measured force/torque to corrective motions using rules. Finally, the performance of this method is evaluated through a series of experiments. Experimental results show that the proposed method can be effectively used for chamferless and precision parts mating.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.3
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• pp.180-193
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• 2005

This paper deals with a multiobjective process planning problem of flexible assembly systems(FASs). The FAS planning problem addressed in this paper is an integrated one of the assignment of assembly tasks to stations and the determination of assembly routing, while satisfying precedence relations among the tasks and flexibility capacity for each station. In this research, we consider two objectives: minimizing transfer time of the products among stations and absolute deviation of workstation workload(ADWW). We place emphasis on finding a set of diverse near Pareto or true Pareto optimal solutions. To achieve this, we present a new multiobjective coevolutionary algorithm for the integrated problem here, named a multiobjective symbiotic evolutionary algorithm(MOSEA). The structure of the algorithm and the strategies of evolution are devised in this paper to enhance the search ability. Extensive computational experiments are carried out to demonstrate the performance of the proposed algorithm. The experimental results show that the proposed algorithm is a promising method for the integrated and multiobjective problem.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.417-429
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• 1998

This paper considers two-sided (left and right side) assembly lines which are often used, especially in assembling large-sized products such as trucks and buses. A large number of exact algorithms and heuristics have been proposed to balance one-sided lines. However, little attention has been paid to balancing two-sided assembly lines. We present an efficient algorithm based on a branch and bound for balancing two-sided assembly lines. The algorithm involves a procedure for generating an enumeration tree. To efficiently search for the near optimal solutions to the problem, assignment rules are used in the method. New and existing bound strategies and dominance rules are else employed. The proposed algorithm can find a near optimal solution by enumerating feasible solutions partially. Extensive computational experiments are carried out to make the performance comparison between the proposed algorithm and existing ones. The computational results show that our algorithm is promising and robust in solution quality.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.238-238
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• 2000

In this paper, to increase the utilization of uranium resources contained in the spent fuel, the spent fuel is reused. for this, the spent fuel is dismantled or spent fuel rod is extracted from the spent fuel assembly. Therefore, to achieve the performance of compacting the spent fuel assembly, we proposed the controller consisting of adaptive and fuzzy with teaming algorithm. In order to show the performance of proposed algorithm compares, we compared the controller with conventional controller in plant.

• Korean Management Science Review
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• v.17 no.1
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• pp.135-144
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• 2000

This paper describes a scheduling problem of the panel block assembly shop in a shipbuilding industry. Because the shipbuilding is a labor intensive industry the most important consideration in a panel block assembly shop is the workload balancing. which balances man-hour weight and welding length and so on. It should be determined assembly schedule and workstation considering a daily load balancing and a workstation load balancing simultaneously. To solve the problem we develop a hybrid genetic algorithm. Hybrid genetic algorithm proposed in this paper consists of two phases. The first phase uses the heuristic method to find a initial feasible solution which provides a useful information about optimal solution. The second phase proposes the genetic algorithm to derive the optimal solution with the initial population consisting of feasible solutions based on the initial solution. Finally we carried out computational experiments for this load balancing problem which indicate that developed method is effective for finding good solutions.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.514-518
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• 1990

This paper describes an experimental Knowledge-Based Control System, named KBCS, for manufacturing and assembly. The KBCS of five parts and function : data-base, knowledge acquisition, optimization, and graphic monitoring. The KBCS is utilized for a FMS which is of five machine centers and automatic assembly lines. Each machine can perform almost all manufacturing functions which some difference in efficiency. Buffers store temporarily the incoming components and the outing components. Parts arrive at assembly lines after many steps of manufacturing, and the transfer path and time are determined by procedural knowledge of control systems. Nine different incoming components are set up. The total control system is expected to perform four algorithms, timing algorithm ,sequencing algorithm, penalty algorithm, and cart algorithm. The construction of controller require basic components of manufacturing systems in which knowledges are formulated on the base of the results and the repeated simulation of KBCS with graphic monitoring system. Simulation results by KBCS are compared with those by the other rules of manufacturing.

• IE interfaces
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• v.12 no.1
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• pp.132-142
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• 1999

Two-sided (left- and right-sided) assembly lines in practice can provide several advantages over one-sided lines in terms of the required line length, throughput time, cost of tools and fixtures, and so on. This paper considers two-sided assembly line balancing with the objective of minimizing line length. The balancing problem is more complicated than that of one-sided lines due to sequence-dependency of tasks assigned to a pair of directly facing workstations. This paper shows how genetic algorithm can be used to solve two-sided assembly line balancing. For this, an encoding and a decoding method suitable to the problem are presented. Proper genetic operators and an evalutation function are also employed. Extensive computational experiments are carried out to show the efficacy of the proposed algorithm. The results show that the algorithm is viable and promising in solution quality and computation time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.98-108
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• 2005

The assembly line balancing problem has been focused by many research works because the efficient management of the assembly line might influence not only the quality of the products but also the working conditions for the workers. This paper deals with U-shape mixed-model assembly line balancing and considers both the processing time and the physical workloads. We suggest the goal programming approach for this situation and to overcome some difficulties of finding optimal solution, we adopt the genetic algorithm that is one of the most promising solution techniques. We tested several test problems and present the results that indicate some improvement for the line balancing as well as the stable performance of the algorithm.

• Korean Management Science Review
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• v.23 no.2
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• pp.175-185
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• 2006

How to efficiently manage assembly blocks at shipyard has been a hot management issue in the shipbuilding Industry, because it has significantly influenced on the productivity of shipbuilding process. This paper introduces the real practice of assembly block operations management in Hyundai Heavy Industries (HHI) and the Ship Assembly Block Operations Optimization (SABOO) project that h3s been launched in HHI as an academy-and-industry collaborative project, aimed to diagnose problems, propose possible solutions, and develop a prototype system in order to search ways of improving the assembly block operations management. Through the field interviews, observations, and benchmarking studies, the SABOO project diagnosed the most rudimental and urgent problem and proposed possible solutions. In addition, the SABOO project developed the prototype system that embodied the visual function of monitoring the shipyard on a real-time and the Interactive block assignment function that utilized the assembly block assignment algorithm developed by the project. As a whole, the SABOO project tested the possibility and gained an insight in extending the functions of block transportation/stockyard management system.