• Proceedings of the Korean Information Science Society Conference
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• 2005.11a
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• pp.994-996
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• 2005

P2P 그리드 컴퓨팅 환경에서 피어의 자율성으로 인한 자원 제공의 휘발성과 피어의 이질적인 특성은 스케줄링 과정에서 해결해야 할 중요한 문제이다. 이에 본 논문에서는 이동 에이전트 기반 적응적 그룹 스케줄링 기법(Mobile Agent based Adaptive Group Scheduling Mechanism: MAAGSM)을 제안한다. 제안 기법은 피어의 특성 (즉, 자원제공자 자율성 고장, 자원제공자 가음용, 자원 제공 시간)에 따라 피어들을 동질적인 그룹(자원제공자 그룹)으로 구성한 후, 그룹에 적합한 다양한 스케줄링 알고리즘은 이동에이전트 기술을 이용하여 적용한다.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.77-88
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• 2015

In this paper, we consider a two-agent single-machine scheduling problem with exponential job-dependent position-based learning effects. The objective is to minimize the total weighted completion time of one agent with the restriction that the makespan of the other agent cannot exceed an upper bound. First, we propose a branch-and-bound algorithm by developing some dominance /feasibility properties and a lower bound to find an optimal solution. Second, we design an efficient simulated annealing (SA) algorithm to search a near optimal solution by considering six different SAs to generate initial solutions. We show the performance superiority of the suggested SA using a numerical experiment. Specifically, we verify that there is no significant difference in the performance of %errors between different considered SAs using the paired t-test. Furthermore, we testify that random generation method is better than the others for agent A, whereas the initial solution method for agent B did not affect the performance of %errors.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.156-163
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• 2004

This paper is concerned with coordinated collaboration of multiagent system in which there exist multiple agents which have their own set of skills to perform some tasks, multiple external resources which can be either used exclusively by an agent or shared by the specified number of agents at a time, and a set of tasks which consists of a collection of subtasks each of which can be carried out by an agent. Even though a subtask can be carried out by several agents, its processing cost may be different depending on which agent performs it. To process tasks, some coordination work is required such as allocating their constituent subtasks among competent agents and scheduling the allocated subtasks to determine their processing order at each agent. This paper proposes a genetic algorithm-based method to coordinate the agents to process tasks in the considered multiagent environments. It also presents some experiment results for the proposed method and shows that the proposed method is a useful coordination collaboration method of multiagent system.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.227-233
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• 2004

Complex scheduling problems are related to planning, scheduling, constraint satisfaction problems, object-oriented concepts, and agent systems. Human preference-driven scheduling technique was to solve complex scheduling problems using constraint satisfaction problems and object-oriented concepts. We have tried to apply human preference-driven scheduling technique to reservation problems. For customer's satisfaction, we have considered customer's preferences in the reservation scheduling. The technique of reservation scheduling proposed in this thesis is based on object-oriented concepts. 1'o consider the over all satisfaction, the events of every object are alloted to the board along its priority. Constraints to reservation scheduling are classified to global and local. The definition of board and information of every event are global constraints and the preferences to object's board slots are local constraints. We have applied look-ahead technology to reservation scheduling in order to minimize backtracking not to fail the allotment of events.

• Convergence Security Journal
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• v.15 no.2
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• pp.43-55
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• 2015

Security infrastructure of Educational Network responds to threats by collecting and analyzing security events from various information protection system based on the integrated management system. Even if this system provides useful and detailed information to the administrator, there are some problems that this system does not provide effective response process and management systems for various threatening situations and the simultaneous threat processes. To solve this problem, we propose and develop security agents that enable the administrator to effectively manage integrated security for Educational Network. The proposed solution provides the administrator with efficient management techniques and process scheduling for various security events so that the administrator can response promptly to problems with the initial threat to Educational Network.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.99-123
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• 2006

In an effect to composite an optimal supply chain, this study has introduced an agent-based negotiation as a method to assign a lot of orders to a large number of participants. As a resources allocation mechanism to form a strategic cooperation based on information sharing between supply chain members(buyers, manufacturers, suppliers), this agent negotiation provides coordination functions allowing all participants to make a profit and accomplishing Pareto optimum solution from the viewpoint of a whole supply chain. A SET model-based scheduling takes into consideration both earliness production cost and tardiness production cost, along with a competitive relationship between multiple participants. This study has tried to prove that the result of an agent-based negotiation is a Pareto optimal solution under the dynamic supply chain environment, establishing the mathematical formulation for a performance test, and making a comparison with the heuristic Branch & Bound method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.130-137
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• 2013

In this paper, we consider a two-agent scheduling with sequence-dependent exponential learning effects consideration, where two agents A and B have to share a single machine for processing their jobs. The objective function for agent A is to minimize the total completion time of jobs for agent A subject to a given upper bound on the objective function of agent B, representing the makespan of jobs for agent B. By assuming that the learning ratios for all jobs are the same, we suggest an enumeration-based backward allocation scheduling for finding an optimal solution and exemplify it by using a small numerical example. This problem has various applications in production systems as well as in operations management.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.183-190
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• 2005

In this thesis, first, we propose I-frame encryption techniques of video data for video data itself encryption and propose license agent that processing user's certification and decryption in client system automatically when user execute encrypted video data in system server. License agent runs user's certification, encryption and decryption of video data based on PID(Public Key Infrastructure) using shared key-pool when execute of video data. Also, compose duplex buffer control and propose real time decryption method using efficient buffer scheduling to reduce much playing delay times that happen processing decryption when execute of videoa data of high-capacity.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.