• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.72-78
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• 2009

This paper considers a multiagent scheduling problem under public information where a machine is shared by multiple agents. Each agent has a local objective among the minimization of total completion time and the minimization of maximum. In this problem, it is assumed that scheduling information is public. Therefore an agent can access to complete information of other agents and pursue efficient schedules in a centralized manner. We propose an enumeration scheme to find Pareto optimal schedules and a multiobjective genetic algorithm as a heuristic approach. Experimental results indicate that the proposed genetic algorithm yields close-to Pareto optimal solution under a variety of experimental conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.1
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• pp.87-94
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• 2007

Many researchers have modeled complicated problems with distributed AI technologies in distributed environment. Especially, intelligent agent technology we often proposed among researchers as efficient method to solve the complicated problems because agent technology makes possibile to share different problem solving abilities among processors in distributed processing environment. In this paper, we propose multiagent system model to solve distributed scheduling problem. At this point we apple CSP techniques to the multiagent model as individual problem solving ability of member agents. Scheduling problem is divided into subproblems according to constraints by distributed resources, then each agent solves its subproblem using CSP solver in the proposed model. This method improves scheduling efficiency. For meeting scheduling problem of case study, we show CSP modeling process and suggest problem solving procedure by multiagent system model.

• 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 Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.324-329
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• 2004

This paper presents a coordination method to allocate and schedule tasks for multiagent systems of which agents have fuzzy processing time for their operations earlier on and their processing times are determined at the time the task operations are carried out later on. The proposed method is organized in a two-level genetic algorithm in which the upper level genetic algorithm plays the role of finding efficient task allocation and the lower level genetic algorithm takes charge of searching for efficient schedules corresponding to the task allocation proposed by the upper level genetic algorithm. It presents a strategy to deal with agent failures in the considered multiagent system. It also shows some experiment results for the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.154-158
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• 1997

This paper proposes a new resource allocation system where overall performance can be improved using production smoothing method. In economic point of view, market price is determined by the market mechanism that is subject to the law of demand and supply. Similarly, agents determine whether to allocate tasks to machines by profit and loss or not. In existing resource allocation system, tasks are exclusively allocated to agents with better manufacturing conditions, because they are evaluated by the only currency. But in the proposed resource allocation system, agents are evaluated by not only a currency but also machine specifications. Hereby, the production smoothing is achieved and we expect to improve system performance In this study, we propose a resource allocation system with consideration of Production Smoothing.