• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.115-121
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• 2015

In this paper, we proposed an iterative data-flow optimal scheduling algorithm based on genetic algorithm for high-performance multiprocessor. The basic hardware model can be extended to include detailed features of the multiprocessor architecture. This is illustrated by implementing a hardware model that requires routing the data transfers over a communication network with a limited capacity. The scheduling method consists of three layers. In the top layer a genetic algorithm takes care of the optimization. It generates different permutations of operations, that are passed on to the middle layer. The global scheduling makes the main scheduling decisions based on a permutation of operations. Details of the hardware model are not considered in this layer. This is done in the bottom layer by the black-box scheduling. It completes the scheduling of an operation and ensures that the detailed hardware model is obeyed. Both scheduling method can insert cycles in the schedule to ensure that a valid schedule is always found quickly. In order to test the performance of the scheduling method, the results of benchmark of the five filters show that the scheduling method is able to find good quality schedules in reasonable time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.22
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• pp.35-42
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• 1990

This paper deals with a single machine scheduling problem with a quadratic penalty function of completion times. The objective is to find a optimal sequence which minimizes the total penalty. A new type of node elimination procedure and precedence relation is developed that determines the ordering between adjacent jobs and is incorporated into a branch and bound algorithm. In addition, modified penalty function is considered and numerical examples are provided to test the effectiveness of the optimum algorithm.

• Journal of Korean Society for Quality Management
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• v.16 no.1
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• pp.32-42
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• 1988

The general n/m job-shop problem is easy to state what is required, but it is extremely difficult to make any progress whatever toward a solution. This paper was first to examine a heuristic procedure of general n/m scheduling generation focused on the procedure of MWRK (Most Work Remaining) presented by Giffler and Thompson (1960) among others. Then modified procedure was proposed to obtain better solution in light of the key measure of performance compared with that of the literature presented by Baker (1974). The modified procedure then has been extended to other example problem to test the better results and to assure the properness of application.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.154-163
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• 1998

In this paper a heuristic procedure for a resource constrained project scheduling problem with multiple crashable modes is presented. A similar heuristic procedure by Ahn and Erengue was recently introduced, but the procedure assumes the resource capacities constant over the project life. The Procedure of this paper is able to deal with the resource capacities varying over the project life. The computational results with a set of 110 test problems demonstrate the efficacy of the heuristic procedure.

• Management Science and Financial Engineering
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• v.12 no.1
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• pp.95-112
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• 2006

Disassembly scheduling is the problem of determining the quantity and timing of disassembling used or end-of-life products while satisfying the demand of their parts and/or components over a planning horizon. The case of assembly product structure is considered while the resource capacity constraints are explicitly considered. A cost-based objective is considered that minimizes the sum of disassembly operation and inventory holding costs. The problem is formulated as an integer programming model, and a two-stage heuristic with construction and improvement algorithms is suggested in this paper. To test the performance of the heuristic, computational experiments are done on randomly generated problems, and the results show that the heuristic gives near optimal solutions within a very short amount of computation time.

• Korean Management Science Review
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• v.34 no.1
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• pp.47-56
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• 2017

We focus on the Real time Fire Scheduling Problem (RFSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing threatening probability to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We suggest mathematical modeling for RFSP and several heuristic algorithms. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithms outperform the firing method which is generally adopted in the field artillery.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1348-1356
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• 2017

Integration of wind generators with the conventional power plants will raise operational challenges to the electric power utilities due to the uncertainty of wind availability. Thus, the Generation Scheduling (GS) among the online generating units has become crucial. This process can be formulated mathematically as an optimization problem. The GS problem of wind integrated power system is inherently complex because the formulation involves non-linear operational characteristics of generating units, system and operational constraints. As the robust tool is viable to address the chosen problem, the modern bio-inspired algorithm namely, Grey Wolf Optimization (GWO) algorithm is chosen as the main optimization tool. The intended algorithm is implemented on the standard test systems and the attained numerical results are compared with the earlier reports. The comparison clearly indicates the intended tool is robust and a promising alternative for solving GS problems.

• Korean Management Science Review
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• v.14 no.2
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• pp.47-61
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• 1997

This paper considers the parallel machines scheduling problem characterized as a multi-objective combinatorial problem. As this problem belongs to the NP-complete problem, genetic algorithms are applied instead of the traditional analytical approach. The purpose of this study is to show how the problem can be effectively solved by using genetic algorithms with a permutation approach. First, a permutation representation which can effectively represent the chromosome is introduced for this problem . Next, a schedule builder which employs the combination of scheduling theories and a simple heuristic approach is suggested. Finally, through the computer experiments of genetic algorithm to test problems, we show that the niche formation method does not contribute to getting better solutions and that the PMX crossover operator is the best among the selected four recombination operators at least for our problem in terms of both the performance of the solution and the operational convenience.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.114-120
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• 2002

In a distributed real-time control system, it is essential to confirm the timing behavior of all tasks because these tasks of each real-time controller have to finish their processes within the specified time intervals called a deadline. In order to satisfy this objective, the timing analysis of a distributed real-time system such as shcedulability test must be performed during the system design phase. In this study, a simple application of CAN fur a vehicle body network system is formulated to apply to a holistic scheduling analysis, and the worst-case execution time (WCET) and the worst-case end-to-end response time (WCRT) are evaluated in the point of holistic system view.

• ETRI Journal
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• v.39 no.1
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• pp.13-20
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• 2017

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round-robin scheduling.