• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1411-1418
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• 2017

The batch job scheduler recognizes the computational resources configured in the cluster environment and plays a role of efficiently arranging the jobs in order. In order to efficiently use the limited available resources in the cluster, it is important to analyze and characterize the characteristics of user tasks. To do this, it is important to identify various scheduling algorithms and apply them to the system environment. Most scheduler software reflects the user's work environment, from job submission to termination, as well as the state of the inventory and system status of the entire managed object. It also stores various information related to task execution, such as job scripts, environment variables, libraries, wait for tasks, start and end times. In this paper, we analyze the execution log of the scheduler such as user 's success rate, execution time, and resource size through information related to job execution through batch scheduler. Based on this, it can be used as a basis to optimize the system by increasing the utilization rate of resources.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.79-87
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• 2023

In this paper, we propose a novel genetic algorithm to reduce the overall span time by optimizing the skid insertion sequence in the shipyard subassembly process. We represented a solution by a permutation of a set of skid ids and applied genetic operators suitable for such a representation. In addition, we combined the genetic algorithm and the existing heuristic algorithm called UniDev which is properly modified to improve the search performance. In particular, the slow skid search part in UniDev was changed to a greedy algorithm. Through extensive large-scaled simulations, it was observed that the span time of our method was stably minimized compared to Multi-Start search and a genetic algorithm combined with UniDev.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.111-117
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• 1998

In order to increase productivity of an assembly system composed of multiple robots along a conveyer line, an efficient sequence planning is necessary because the assembly time is dependent upon the assembly sequence. In this paper, a two-robot assembly system is considered in which two robots operate simultaneously and transfer parts from the part feeders to the workpiece on the conveyer one by one. In this case, the distance from the feeder to the workpiece varies with time because the workpiece moves at a constant speed on the conveyer. Hence, the sequence programming is not a trivial problem. Also, the two robots may interfere with each other kinematically and dynamically due to the simultaneous operation, so the sequence should be programmed to avoid the interferences. In this paper, the task sequence optimization problem is formulated and is solved by employing the simulated annealing which has been shown to be effective for solving large combinatorial optimizations.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.53-53
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• 1999

제품에 대한 수요의 다양화에 대응하기 위해서, 다품종소량생산방식이 정착하고 있다. 다품종소량생산을 효율적으로 실현하는 현대의 전형적인 자동화생산시스템으로서, 유연생산시스템(Flexible Manufacturing System : 이하 FMS)이 급속히 보급되어, FMS의 효율적인 운영이 생산현장에 있어서 중요한 과제가 되고 있다. 다수의 품종이 동시에 병행해서 반복적으로 생산되는 실제의 FMS에서는, 품종의 생산에 필요한 일련의 작업의 종류에 대한 우선순서인 "투입우선순서(Dispatching Priority)"를 설정해서, 그것을 기반으로 시스템이 운영되는 것이 보통이다.본 연구에서는 투입우선순서로 제어되는 동일한 품종의 반복이 있는 FMS를 염두에 두고, 생산요건이 확정적인 상태에서 주어진 평가척도를 최적화하는 투입우선순서를 결정하는 문제로써, FMS의 투입우선순서 결정문제 (FMS dispatching priority problem)를 제기하여, 시뮬레이션을 통해서 그 해법을 논한다. 실재하는 많은 FMS에 있어서 투입우선순서를 단기간에 용이하게 정할 수 있는 우선규칙(dispatching rule)이 실용적으로 넓게 이용되고 있다. 종래로부터, ？샵(job shop)을 대상으로 하는 다수의 우선규칙이 제안, 평가되어 왔지만, 그것들은 각 품종이 서로 다른 종류의 일감인 것을 상정하고 있으며, 동일한 품종의 일감의 존재를 의식하고 있지 않다. 본 연구에서는 재래형의 ？샵 환경에 있어서 그 유효성이 검증되어 있는 기존의 표준적인 우선규칙의 성능이, 동일한 품종의 반복이 있는 경우에도 잘 기능하는지 어떤지를 시뮬레이션을 통해 평가한 후에, 오더사이즈(order size)를 고려한 우선규칙을 제안하고, 납기지체의 평가척도에 관한 유효성을 명확히 하고 있다. 시뮬레이션 실험의 결과에서는, 우선규칙의 기본적인 결정요인이 성능에 미치는 영향을 통계적으로 분석하고 있다.로 분석하고 있다.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.566-571
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• 2008

In this paper, we propose a footstep planning method of biped robot based on the Particle Swarm Optimization(PSO). We define configuration and locomotion primitives for biped robots in the 2 dimensional workspace. A footstep planning method is designed using learning process of PSO that is initialized with a population of random objects and searches for optima by updating generations. The footstep planner searches for a feasible sequence of locomotion primitives between a starting point and a goal, and generates a path that avoids the obstacles. We design a path optimization algorithm that optimizes the footstep number and planning cost based on the path generated in the PSO learning process. The proposed planning method is verified by simulation examples in cluttered environments.

• Journal of Navigation and Port Research
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• v.33 no.6
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• pp.443-450
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• 2009

In this paper, we propose optimizing a remarshaling plan in an automated stacking yard using a cooperative coevolutionary algorithm (CCEA). Remarshaling is the preparation task of rearranging the containers in such a way that the delay are minimized at the time of loading. A plan for remarshaling can be obtained by the following steps: first determining the target slots to which the individual containers are to be moved and then determining the order of movement of those containers. Where a given problem can be decomposed into some subproblems, CCEA efficiently searches subproblems for a solution. In our CCEA, the remarshaling problem is decomposed into two subproblems: one is the subproblem of determining the target slots and the other is that of determining the movement priority. Simulation experiments show that our CCEA derives a plan which is better in the efficiency of both loading and remarshaling compared to other methods which are not based on the idea of problem decomposition.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.49-56
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• 2023

In this study, we propose a genetic algorithm (GA) to optimize the allocation and operation order of transporters. The solution in the GA is represented by a set of lists each of which the operation order of the corresponding transporter. In addition, it was implemented in the form of a hybrid genetic algorithm combining effective local search operations for performance improvement. The local search reduces the number of operating transporters by moving blocks from a transporter with a low workload into that with a high workload. To evaluate the effectiveness of the proposed algorithm, it was compared with Multi-Start and a pure genetic algorithm through a simulation environment similar in scale to an actual shipyard. For the largest problem, compared to them, the number of transporters was reduced by 40% and 34%, and the total task time was reduced by 27% and 17%, respectively.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.1-7
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• 2009

This paper presents a process routing (PR) algorithm with multiple objectives. PR determines the optimum sequence of operations for transforming a raw material into a completed part within the available machining resources. In any computer aided process planning (CAPP) system, selection of the machining operation sequence is one of the most critical activities for manufacturing a part and for the technical specification in the part drawing. Here, the goal could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The Pareto Stratum Niche Cubicle (PS NC) GA has been adopted to find the optimum sequence of operations that optimize two conflicting criteria; production cost and production quality. The numerical analysis shows that the proposed PS NC GA is both effective and efficient to the PR problem.

• Journal of Navigation and Port Research
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• v.36 no.8
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• pp.665-671
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• 2012

A remarshalling is studied as an important operational strategy in an automated container terminal to enhance the productivity of container handling. This means the rearrangements of the containers scattered at a vertical yard block. The dispatching problem for remarshalling is selecting the remarshalling operation considering the available operation time and deciding the operation sequencing to maximize the effectiveness of remarshalling. This study develops the optimal mathematical model for yard crane dispatching problem with mixed integer program and explains dispatching problem using an example. However it is difficult to apply this model to a field problem because of its computational time. Therefore, we compare the representative 5 dispatching rules for real world adaption. In a numerical experiment, maximum weight ratio(MR) rule shows an overall outstanding performance.