• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1477-1487
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• 2010

In this paper, we give a solving approach based on a logarithmic-exponential multiplier penalty function for the constrained minimization problem. It is proved exact in the sense that the local optimizers of a nonlinear problem are precisely the local optimizers of the logarithmic-exponential multiplier penalty problem.

• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.97-109
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• 2023

Objective : This study aimed to investigate the mediating effect of play on the relationship between toddlers' sleep habits and problem-solving. Methods : In total, 1,734 participants were selected from the 3rd wave of the Panel Study on Korean Children. A structural equation modeling approach was utilized to examine the relationship among toddlers' play, sleep habits, and problem-solving, as well as to investigate the mediating effect of play. Results : The monthly age of the study participants ranged from 23 to 32 months, with 885 (51.0%) boys and 849 (49.0%) girls. The indirect effects of play on problem-solving skills (β = 0.137, p = .006) were statistically significant, but the direct effects of sleep habits on problem-solving skills (β = -.015, p = .871) and the total effect (β = 0.122, p = .057) were not significant. Conclusion : This study indicated that sleep habits did not have a direct effect on problem-solving ability, but that the indirect effects were significant and fully mediated by play. Incorrect sleep habits can negatively affect lifelong development. Therefore, parents would need to be aware of whether their child is developing good sleep habits during the toddler age.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.369-382
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• 2014

This paper introduces a new multi-product multi-period multi-objective aggregate production planning problem. The proposed problem is modeled using multi-objective mixed-integer mathematical programming. Three objective functions, including minimizing total cost, maximizing customer services level, and maximizing the quality of end-product, are considered, simultaneously. Several constraints such as quantity of production, available time, work force levels, inventory levels, backordering levels, machine capacity, warehouse space and available budget are also considered. Some parameters of the proposed model are assumed to be qualitative and modeled using fuzzy sets. Then, a fuzzy goal programming approach is proposed to solve the model. The proposed approach is applied on a real-world industrial case study of a color and resin production company called Teiph-Saipa. The approach is coded using LINGO software. The efficacy and applicability of the proposed approach are illustrated in the case study. The results of proposed approach are compared with those of the existing experimental methods used in the company. The relative dominance of the proposed approach is revealed in comparison with the experimental method. Finally, a data dictionary, including the way of gathering data for running the model, is proposed in order to facilitate the re-implementation of the model for future development and case studies.

• Child Health Nursing Research
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• v.25 no.4
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• pp.507-517
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• 2019

Purpose: This study was conducted to examine the effects of team-based problem-based learning combined with smart education among nursing students. Methods: A quasi-experimental non-equivalent control group, pre-posttest design was used. The experimental group (n=36) received problem-based learning combined with smart education and lectures 7 times over the course of 7 weeks (100 minutes weekly). Control group (n=34) only received instructor-centered lectures 7 times over the course of 7 weeks (100 minutes weekly). Data were analyzed using the $x^2$ test, the Fisher exact test, and the independent t-test with SPSS for Windows version 21.0. Results: After the intervention, the experimental group reported increased learning motivation (t=2.70, p=.009), problem-solving ability (t=2.25, p=.028), academic self-efficacy (t=4.76, p<.001), self-learning ability (t=2.78, p<.001), and leadership (t=2.78, p=.007) relative to the control group. Conclusion: Team-based problem-based learning combined with smart education and lectures was found to be an effective approach for increasing the learning motivation, problem-solving ability, academic self-efficacy, self-learning ability, and leadership of nursing students.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.97-108
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• 2001

Frauds detection is a difficult problem, requiring huge computer resources and complicated search activities Researchers have struggled with the problem. Even though a fee research approaches have claimed that their solution is much better than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds. a novel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new self of decision-makin rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.293-304
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• 2001

frauds detection is a difficult problem, requiring huge computer resources and complicated search activities. researchers have struggled with the problem. Even though a flew research approaches have claimed that their solution is much bettor than others, research community has not found 'the best solution'well fitting every fraud. Because of the evolving nature of the frauds, a Revel and self-adapting method should be devised. In this research a new approach is suggested to solving frauds in insurance claims and credit card transaction. Based on evolutionary computing approach, the method is itself self-adjusting and evolving enough to generate a new set of decision-making rules. We believe that this new approach will provide a promising alternative to conventional ones, in terms of computation performance and classification accuracy.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.761-783
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• 2009

Problem solving ability can be fostered by dealing with many different types of problems. We investigated how $5^{th}$ and $6^{th}$ graders who did not learn traditional algebraic methods might approach the word problems of simultaneous equations. This result reveals that the strategy of guess-and-check serves as a basis for elementary school students in solving simultaneous equations. A noticeable remark is that students used the guess-and-check strategy in various ways. Whereas some students changed a variable given in the problem step by step, others did in a sophisticated way focusing on the relation between two variables. Moreover, some students were able to write an equation which was not typical but meaningful and correct. This paper emphasizes the need of connections between pre-algebraic and algebraic solutions.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.240-246
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• 2022

This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.29-43
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• 2021

In this paper, a new approach to solve the constrained dynamic programming is proposed by using the constraint programming. While the conventional dynamic programming scheme has the state space augmented with states on constraints, this approach, without state augmentation, represents states of constraints as domains in a contraining programming solver. It has a hybrid computational mechanism in its computation by combining solving the Bellman equation in the dynamic programming framework and exploiting the propagation and inference methods of the constraint programming. In order to portray the differences of the two approaches, this paper solves a simple version of the long-term car rental financing problem. In the conventional scheme, data structures for state on constraints are designed, and a simple inference borrowed from the constraint programming is used to the reduction of violation of constraints because no inference risks failure of a solution. In the hybrid approach, the architecture of interface of the dynamic programming solution method and the constraint programming solution method is shown. It finally discusses the advantages of the proposed method with the conventional method.