• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.105-111
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• 2015

This paper suggests O(n) linear-time algorithm for car sequencing problem (CSP) that has been classified as NP-complete because of the polynomial-time algorithm to solve the solution has been unknown yet. This algorithm applies maximum options-equiped car type first production rule to decide the car sequencing of n meet the r:s constraint. This paper verifies thirteen experimental data with the six data are infeasible. For thirteen experimental data, the proposed algorithm can be get the solution for in all cases. And to conclude, This algorithm shows that the CSP is not NP-complete but the P-problem. Also, this algorithm proposes the solving method to the known infeasible cases. Therefore, the proposed algorithm will stand car industrial area in good stead when it comes to finding a car sequencing plan.

• Journal of the Korea Safety Management & Science
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• v.5 no.1
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• pp.69-82
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• 2003

The sequence which minimizes overall utility work in car assembly lines reduces the cycle time, the number of utility workers, and the risk of conveyor stopping. This study suggests mathematical formulation of the sequencing problem to minimize overall utility work, and present a genetic algorithm which can provide a near optimal solution in real time. To apply a genetic algorithm to the sequencing problem in car assembly lines, the representation, selection methods, and genetic parameters are studied. Experiments are carried out to compare selection methods such as roullette wheel selection, tournament selection and ranking selection. Experimental results show that ranking selection method outperforms the others in solution quality, whereas tournament selection provides the best performance in computation time.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.35-39
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• 2008

This paper considers the sequencing of products in mixed model assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time, the number of utility workers, and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Proceedings of the Korean Society for Quality Management Conference
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• 2009.10a
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• pp.131-135
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• 2009

This paper considers the sequencing of products in car assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search 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 and computation time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Korea Safety Management & Science
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• v.4 no.3
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• pp.95-105
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• 2002

This paper considers the sequencing of products in car assembly lines under Just-In-Time systems. Under Just-In-Time systems, the most important goal for the sequencing problem is to keep a constant rate of usage every part used by the systems. In this paper, tabu search technique for this problem is proposed. Tabu search 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 and computation time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.1-5
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• 2004

• IE interfaces
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• v.16 no.3
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• pp.352-361
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• 2003

Vehicle manufacturing plant is a typical mixed-model production system. Generally it consists of three main shops including body shop, painting shop and assembly shop in addition to engine shop. Each shop contains diverse manufacturing processes, all of which are integrated in a form of flow line. Due to the high pressure from the market requesting small-volume large variety production, production planning becomes very critical for the competitiveness of automotive industry. In order to save costs and production time, production planning system is requested to meet some designated requirements for each shop: to balance the work load in body and assembly shops, and to minimize the number of color changes in painting shop. In this context, we developed a sequencing module for a vehicle production planning system using the ILOG Solver Library. It is designed to take into account all the manufacturing constraints at a time with meeting hard constraints in body shop, minimizing the number of soft constraints violated in assembly shop, and minimizing the number of color changes in painting shop.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.177-186
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• 2020

This paper deals with the car paint sequencing problem (CPSP) that the entrance sequence is to same colored group with maximum sequenced cars for the buffer arriving cars from the body shop. This problem classified by NP-complete problem because of the exact solution has not obtained within polynomial time. CPSP is aim to minimum pugging number that each pugging must be performs at color changing time in order to entirely cleaning the remaining previous color. To be obtain the minimum number of moving distance with window concept and minimum number of pugging, this paper sorts same color and arriving sequence. Then we basically decide the maximum length section color time to winner team using stage race method. For the case of the loser team with no more racing or yield to loser team and more longer stage in upcoming racing, the winner team give way to loser team. As a result, all cars(runners) are winner in any stage without fail. For n cars, the proposed algorithm has a advantage of simple and fast with O(nlogn) polynomial time complexity, this algorithm can be get the minimum number of moving distance and purging for all of experimental data.