• Korean Management Science Review
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• v.20 no.1
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• pp.51-64
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• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.2
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• pp.59-68
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• 2001

The job shop scheduling problem is not only NP-hard, but is one of the well known hardest combinatorial optimization problems. The goal of this research is to develop an efficient scheduling method based on hybrid genetic algorithm to address job shop scheduling problem. In this scheduling method, generating method of initial population, new genetic operator, selection method are developed. The scheduling method based on genetic algorithm are tested on standard benchmark job shop scheduling problem. The results were compared with another genetic algorithm0-based scheduling method. Compared to traditional genetic, algorithm, the proposed approach yields significant improvement at a solution.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.97-109
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• 2002

Manufacturing environments in the real world are subject to many sources of change and uncertainty, such as new job releases, job cancellations, a chance in the processing time or start time of some operation. Thus, the realistic scheduling method should Properly reflect these dynamic environment. Based on the release times of jobs, JSSP (Job Shoe Scheduling Problem) can be classified as static and dynamic scheduling problem. In this research, we mainly consider the dynamic JSSP with continually arriving jobs. The goal of this research is to develop an efficient scheduling method based on GA (Genetic Algorithm) to address dynamic JSSP. we designed scheduling method based on SGA (Sing1e Genetic Algorithm) and PGA (Parallel Genetic Algorithm) The scheduling method based on GA is extended to address dynamic JSSP. Then, This algorithms are tested for scheduling and rescheduling in dynamic JSSP. The results is compared with dispatching rule. In comparison to dispatching rule, the GA approach produces better scheduling performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.9
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• pp.1088-1095
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• 1999

This paper presents short-term hydro scheduling method for hydrothermal coordination by genetic algorithms. Hydro scheduling problem has many constraints with fixed final reservoir volume. In this paper, the difficult water balance constraints caused by hydraulic coupling satisfied throughout dynamic decoding method. Adaptive penalizing method was also proposed to handle the infeasible solutions that violate various constraints. In this paper, we proposed GA to solve hydrothermal scheduling with appropriate decoding method and dynamic penalty method. The effectiveness of the proposed method is demonstrated in the case study.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.167-174
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• 2005

This paper experimentally validates the efficacy of scheduling method that satisfies the performance requirement of periodic data in the Foundation Fieldbus. The scheduling method allocates periodic data traffic to a bandwidth-limited fieldbus medium. The scheduling method generates schedule list that records schedule starting time and schedule period of each periodic data. This study developed an experimental model of a Foundation Fieldbus network system. Using the experimental model, this study showed that the scheduling method can be easily implemented in a practical Foundation Fieldbus network system. The results obtained from the experimental evaluation showed that the scheduling method restricts the delay of periodic data to a pre-specified bound.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.25-49
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• 1997

Printed circuit board (PCB) assembly line configuration is characterized by very long set-up times and high work in process (WIP) inventory level. The scheduling method can significantly reduce the set-up times and WIP inventory level. Greedy sequence dependent scheduling (GSDS) method is proposed based on the current methods. The proposed method is compared with the current method in terms of three performance measures: line throughput, average WIP inventory level, and implementation complexity.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.719-727
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• 2006

This paper proposes a new method to obtain a maximum allowable delay bound for a scheduling of networked discrete control systems and event-based scheduling method. The proposed method is formulated in terms of linear matrix inequalities and can give a much less conservative delay bound than the existing methods. A network scheduling method is presented based on the delay obtained through the proposed method, and it can adjust the sampling period to allocate same utilization to each control loop. The presented method can handle three types of data (sporadic, emergency data, periodic data and non real-time message) and guarantees real-time transmission of periodic and sporadic emergency data using modified EDF scheduling method.

• IE interfaces
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• v.12 no.3
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• pp.362-373
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• 1999

Resource Constrained Dynamic Multi-Projects Scheduling (RCDMPS) is intended to schedule activities of two or more projects sequentially arriving at die shop under restricted resources. The aim of this paper is to develop a new problem solving method for RCDMPS to make an effect schedule based by constraint programming. The constraint-based scheduling method employs ILOG Solver which is C++ constraint reasoning library for solving complex resource management problems and ILOG Schedule which is a add-on library to ILOG Solver dedicated to solving scheduling problems. And this method interfaces with ILOG Views so that the result of scheduling displays with Gantt chart. The scheduling method suggested in this paper was applied to a company scheduling problem and compared with the other heuristic methods, and then it shows that the new scheduling system has more preference.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.11-20
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• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.215-218
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• 1996

In work scheduling problems, scheduling constraints are not absolutely rigid; they may be changed depending on the scheduling aspect effected. In order to cope with changes in scheduling constraints and assignment strategies and to optimize scheduling results quickly, this paper will propose a new scheduling method which combines knowledge engineering and mathematical programming techniques.