• The Journal of Society for e-Business Studies
• /
• v.5 no.2
• /
• pp.143-150
• /
• 2000

This paper presents a methodology how to utilize management of research scheduling plan, processing, and results using Web Knowledge Database System, which integrates research knowledge management model under the Research ＆ Development Environment. The content of this paper consists of description on utilization of the Web Knowledge Database System, sharing of the Research Knowledge through design data review, communications, and management of research knowledge flow during the Research ＆ Development Period.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.316-316
• /
• 1996

• Journal of Korean Institute of Industrial Engineers
• /
• v.19 no.3
• /
• pp.11-23
• /
• 1993

A basic problem of sequencing a set of independent tasks at a single facility with the objective of minimizing total tardiness is considered. A variation of beam search, called knowledge-based beam search, has been studied which uses domain knowledge to reduce the problem size with an evaluation function to generate nodes probable to include the optimal solution. Its performance behavior is compared with some existing algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.28 no.1B
• /
• pp.27-32
• /
• 2003

This paper presents the Genetic Algorithm based Task Scheduling (GATS) method for the scheduling of programs with diverse embedded parallelism types in Distributed Parallel Systems, which consist of a set of loosely coupled parallel and vector machines connected via high speed networks The distributed parallel processing tries to solve computationally intensive problems that have several types of parallelism, on a suite of high performance and parallel machines in a manner that best utilizes the capabilities of each machine. When scheduling in distributed parallel systems, the matching of the parallelism characteristics between tasks and parallel machines rather than load balancing should be carefully handled with the minimization of communication cost in order to obtain more speedup. This paper proposes the based initialization methods for an initial population and the knowledge-based mutation methods to accommodate the parallelism type matching in genetic algorithms.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.16 no.4
• /
• pp.113-118
• /
• 2017

As the demand for industrial robots and Automated Guided Vehicles (AGVs) increases, higher performance is also required from them. Fuzzy controllers, as part of an intelligent control system, are a direct control method that leverages human knowledge and experience to easily control highly nonlinear, uncertain, and complex systems. This paper uses a $LabVIEW^{(R)}-based$ fuzzy controller with gain scheduling to demonstrate better performance than one could obtain with a fuzzy controller alone. First, the work area was set based on forward kinematics and inverse kinematics programs. Next, $LabVIEW^{(R)}$ was used to configure the fuzzy controller and perform the gain scheduling. Finally, the proposed fuzzy gain scheduling controller was compared with to controllers without gain scheduling.

• Journal of Korean Society for Quality Management
• /
• v.22 no.3
• /
• pp.161-178
• /
• 1994

FMS are being installed to improve productivity, manufacturing consistency and flexibility. However, FMS are quite expensive and efforts must be made to avoid the high investment risk. The objective of this paper is to enable the real-time rescheduling under dynamic changes in FMS environment. For this purpose, a KBSS (Knowledge-Based Scheduling System) in FMS environment is developed. This KBSS will meet various requirements of users, for example, to minimize makespan, average flow time, or to maximize machine utilization.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.3
• /
• pp.862-870
• /
• 2000

Recently, according to the rapid development of internet and communication technologies, a lot of works based on network techniques have been developed. In relation to this trend, the agent systems that had been studied in the early AI have been being studied greatly in association with the network techniques. This paper present a design model of a secretary agent system in which each secretary agent can manage the schedules of her/his superior, and the descriptions about the implementation of the secretary agent system. In the presented secretary agent system, a database system and a knowledge-based systems are included and cooperated with each secretary agent to provide the ability of manipulating lots of schedule data and making decisions on them. This paper also shows that the presented secretary agent system can behavior like a real secretary through the various superior-secretary-meeting-requester interactions, which is different from the roup scheduling programs or personal scheduling programs.

• IE interfaces
• /
• v.7 no.1
• /
• pp.59-66
• /
• 1994

In scheduling open-field type FMS, process planning of decision making between alternate machines is taken into consideration. This idea is validated via implementing two experimental systems; One is a knowledge-based system and the other is to solve a Constraint Satisfaction Problem. The former generates some promising schedules in view of improving machine utilization, makespan and meanflow time, and the latter does in view of meeting due date.

• Journal of the Korean Institute of Navigation
• /
• v.24 no.5
• /
• pp.361-371
• /
• 2000

This paper treats a genetic algorithm for ship scheduling problem in set packing formulation. We newly devised a partition based representation of solution and compose initial population using a domain knowledge of problem which results in saving calculation cost. We established replacement strategy which makes each individual not to degenerate during evolutionary process and applied adaptive mutate operator to improve feasibility of individual. If offspring is feasible then an improve operator is applied to increase objective value without loss of feasibility. A computational experiment was carried out with real data and showed a useful result for a large size real world problem.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1100-1122
• /
• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.