• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.69-74
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• 2004

A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.640-645
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• 1998

A Sequencing problem is one to find an ordered sequence of some entities which maximizes (or minimize) some objective function. This paper introduces an new type of sequencing problems, named a Sequencing problem with fuzzy preference relation is previded for the evaluation of the quality of sequences, It presents how such a problem can be formulated in the point of objective function. In addition, it proposes a genetic algorithm applicable to such a sequencing problem.

• Journal of the military operations research society of Korea
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• v.29 no.2
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• pp.61-80
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• 2003

A fire sequencing problem is considered. Fire sequencing problem is a kind of scheduling problem that seeks to minimize the overall time span under a result of weapon­target allocation problem. The assigned weapons should impact a target simultaneously and a weapon cannot transfer the firing against another target before all planned rounds are consumed. The computational complexity of the fire sequencing problem is strongly NP­complete even if the number of weapons is two, so it is difficult to get the optimal solution in a reasonable time by the mathematical programming approach. Therefore, a genetic algorithm is adopted as a solution method, in which the representation of the solution, crossover and mutation strategies are applied on a specific condition. Computational results using randomly generated data are presented. We compared the solutions given by CPLEX and the genetic algorithm. Above $7(weapon){\times}15(target)$ size problems, CPLEX could not solve the problem even if we take enough time to solve the problem since the required memory size increases dramatically as the number of nodes expands. On the other hand, genetic algorithm approach solves all experimental problems very quickly and gives good solution quality.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1118-1122
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• 2005

In order to keep production balance at a mixed-model assembly line and a painting line, large WIP(Work- In-Process) inventories are required between two lines. To increase the efficiency of line handling through reducing the inventories under this circumstance, this paper concerns with a sequencing problem for a mixed-model assembly line that includes a painting line where the uncertain elements regarding the defective products exist. Then, we formulate a new type of the sequencing problem minimizing the line stoppage time and the idle time with forecasting the supply time of the products from the painting line. Finally, we examine the effectiveness of the proposed sequencing through computer simulations.

• 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.

• 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.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.21 no.3
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• pp.329-343
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• 1995

Traditionally, machining process sequence was influenced and constrained by the design information obtained from CAD data base, i.e., class of operations, geometric shape, tooling, geometric tolerance, etc. However, even though all the constraints from design information are considered, there may exist more than one way to feasibly machine parts. This research is focused on the integrated problem of operations sequencing and machine tools selection in the presence of the product mix and their production volumes. With the transitional costs among machining operations, the operation sequencing problem can be formulated as a well-known Traveling Salesman Problem (TSP). The transitional cost between two operations is expressed as the sum of total machining time of the parts on a machine for the first operation and transportation time of the parts from the first machine to a machine for the second operation. Therefore, the operation sequencing problem formulated as TSP cannot be solved without transitional costs for all operation pairs. When solved separately or serially, their mutual optima cannot be guaranteed. Machining operations sequencing and machine tool selection problems are two core problems in process planning for discretely machined parts. In this paper, the interrelated two problems are integrated and analyzed, zero-one integer programming model for the integrated problem is formulated, and the solution methods are developed using a Tabu Search technique.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.49-60
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• 1999

This paper addresses a welding task sequencing for robot arc welding process planning. Although welding task sequencing is an essential step in the welding process planning, it has not been considered through a systematic approach, but it depends rather on empirical knowledge. Thus, an effective task sequencing for robot arc welding is required. Welding perations can be classified by the number of welding robots. Genetic algorithms are applied to tackle those welding task sequencing problems. A genetic algorithm for traveling salesman problem (TSP) is utilized to determine welding task sequencing for a MultiWeldline-SingleLayer problem. Further, welding task sequencing for multiWeldline-MultiLayer welding is investigated and appropriate genetic algorithms are introduced. A random key genetic algorithm is also proposed to solve multi-robot welding sequencing : MultiWeldline with multi robots. Finally, the genetic algorithm are implemented for the welding task sequencing of three dimensional weld plate assemblies. Robot welding operations conforming to the algorithms are simulated in graphic detail using a robot simulation software IGRIP.