• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.41-48
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• 1982

This paper analyzed md developed flow - shop sequencing heuristic method. The essence of the heuristic approach is in the application of selective routine that reduce the size of a problem. The advantages of this approach are consistency. Speed, endurance and the ability to cope with more data and larger systems than is humanly possible, In recent years many heuristic procedures have been suggested for the flow - shop sequencing problem. Although limited comparisons of these procedures have been made, a full scale test and evaluation have not been reported previously. The maximum flow - time criterion is selected as the evaluation criterion is selected as the evaluation criterion of flow - shop's efficiency. The author evaluated these 3 heuristic method's performance. By the evaluation of the result, we can see that the modified methods produce a shorter maximum flow - time than the original methods.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.91-104
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• 1992

Flowshop scheduling problem is known to be NP-complete. Since the optimization apporach like branch-and-bound is limited by exponentially growing computation time, many heuristic methods have been developed. Total tardiness is one of the criteria that the researchers have recently considered in flowshop scheduling. There, however, are few literatures which studied the general (m machine)-flowshop scheduling under the total tardiness criterion. In this paper, a heuristic scheduling method to minimize total tardiness at the (m machine, n job)-flowshop is presented. A heuristic value function is proposed to be used as a dispatching criterion in initial schedule generation. And the schedule improving procedure, by pairwise interchange of tardy job with the job right ahead of it, is introduced. Illustrative examles and simulated results are presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.13-19
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• 1981

This paper developed 3 flow-shop sequencing heuristic methods: modified RA method, modified RACS method and modified RAES method. These methods modified RA method, RACS method and RAES method developed by D. G. Dannenbring. These methods can easily determine desirable sequence of orders and can improve nx3 flow-shop's productivity and efficiency. The maximum flow-time criterion is selected as the evaluation criterion of flow-shop's efficiency, We evaluated these 6 heuristic methods’ performance. By the evaluation of the result, we can see that the modified methods produce a shorter maximum flow-time than the original methods.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.2
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• pp.36-41
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• 1987

This paper presents a heuristic algorithm for solving the simple facility location problem. Its main procedure is essentially of 'add' type, which progressively selects facilities to open according to a certain criterion derived from the analysis of the linear programming dual. Computational experience with test problem from the literature is presented.

• International Journal of Reliability and Applications
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• v.4 no.1
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• pp.13-26
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• 2003

This paper presents a comparative study of a few commonly used maintenance scheduling methods for small utilities that consists solely of thermal generating plants. Two deterministic methods and a stochastic method are examined. The deterministic methods employ the leveling of reserve capacity criterion, of which one uses a heuristic rule to level the deterministic equivalent load obtained by using the product of the unit capacity and its corresponding forced outage rate. The stochastic method simulates the leveling of risk criterion by using the peak load carry capacity of available units. The results indicate that for the size and type of the maintenance scheduling problem described In this study, the stochastic method does not produce a schedule which is significantly better than the deterministic methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.3
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• pp.90-96
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• 2011

Finding the critical path (or the longest path) on acyclic directed graphs, which is well-known as PERT/CPM, the ambiguity of each acr's length can be modeled as a range or an interval, in which the actual length of arc may realize. In this case, the min-max regret criterion, which is widely used in the decision making under uncertainty, can be applied to find the critical path minimizing the maximum regret in the worst case. Since the min-max regret critical path problem with the interval arc's lengths is known as NP-hard, this paper proposes a heuristic algorithm to diminish the maximum regret. Then the computational experiments shows the proposed algorithm contributes to the improvement of solution compared with the existing heuristic algorithms.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.15-15
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• 2003

We provide some recent results of approximation algorithms for solving Markov Games and discuss their applications to problems that arise in Computer Science. We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with an infinite horizon discounted cost criterion. We present error bounds from the optimal equilibrium value of the game when both players take “correlated” receding horizon policies that are based on exact or approximate solutions of receding finite horizon subgames. Motivated by the worst-case optimal control of queueing systems by Altman, we then analyze error bounds when the minimizer plays the (approximate) receding horizon control and the maximizer plays the worst case policy. We give two heuristic examples of the approximate receding horizon control. We extend “parallel rollout” and “hindsight optimization” into the Markov game setting within the framework of the approximate receding horizon approach and analyze their performances. From the parallel rollout approach, the minimizing player seeks to combine dynamically multiple heuristic policies in a set to improve the performances of all of the heuristic policies simultaneously under the guess that the maximizing player has chosen a fixed worst-case policy. Given $\varepsilon$>0, we give the value of the receding horizon which guarantees that the parallel rollout policy with the horizon played by the minimizer “dominates” any heuristic policy in the set by $\varepsilon$, From the hindsight optimization approach, the minimizing player makes a decision based on his expected optimal hindsight performance over a finite horizon. We finally discuss practical implementations of the receding horizon approaches via simulation and applications.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• Journal of Convergence Society for SMB
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• v.3 no.2
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• pp.19-25
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• 2013

Decision procedure was done with the evaluation of multi-criterion analysis. Importance of each criterion was considered through heuristically method, specially it was based on the heuristic least mean square algorithm. To consider coalition evaluation, it was carried out by calculation of Shapley index and Interaction value. The model output is also analyzed with the help of those two indexes, and the procedure was also displayed with details. Finally, the differences between the model output and the desired results are evaluated thoroughly, several problems are raised at the end of the example which require for further studying.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2470-2476
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• 1997

We propose a new real-time search algorithm and provide experimental evaluation and comparison of the new algorithm with mini-min lookahead algorithm. Many other real-time heuristic-search approached often divide the problem space to several sub-problems. In this paper, the proposed algorithm guarantees not only the sub-problem deadline but also total deadline. Several heuristic real-time search algorithms such as $RTA^{\ast}$, SARTS and DYNORA have been proposed. The performance of such algorithms depend on the quality of their heuristic functions, because such algorithms estimate the search time based on the heuristic function. In real-world problem, however, we often fail to get an effective heuristic function beforehand. Therefore, we propose a new real-time algorithm that determines the sub-problem deadline based on the status of search space during sub-problem search process. That uses the cut-off method that is a dynamic stopping-criterion-strategy to search the sub-problem.