• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.77-82
• /
• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• Journal of Navigation and Port Research
• /
• v.28 no.6
• /
• pp.531-539
• /
• 2004

In this paper, we propose a new heuristic solution for 3D container packing problem for the variable sizes and types of freight. Frist of all, we consider the total cost of container charge i.e., handling, loading and transportation, where each freight will be specifically identified The types of containers and its number to be loaded am be selected automatically by minimizing the total cost of container charge. Maximization of loading space am be achieved efficiently by operating the palletizing and/or depalletizing of freight. By considering these factors we can determine the position of freight in the container and the loading sequence to be packing into the container. In container packing simulation, we can verify that the proposed heuristic algorithm indicates more efficiency space utilization and shows the possibility of using on commercial business.

• Structural Engineering and Mechanics
• /
• v.62 no.6
• /
• pp.749-758
• /
• 2017

In structural mechanics, traditional analyses methods usually employ matrix operations for obtaining displacement and internal forces of the structure under the external effects, such as distributed loads, earthquake or wind excitations, and temperature changing inter alia. These matrices are derived from the well-known principle of mechanics called minimum potential energy. According to this principle, a system can be in the equilibrium state only in case when the total potential energy of system is minimum. A close examination of the expression of the well-known equilibrium condition for linear problems, $P=K{\Delta}$, where P is the load vector, K is the stiffness matrix and ${\Delta}$ is the displacement vector, it is seen that, basically this principle searches the displacement set (or deformed shape) for a system that minimizes the total potential energy of it. Instead of using mathematical operations used in the conventional methods, with a different formulation, meta-heuristic algorithms can also be used for solving this minimization problem by defining total potential energy as objective function and displacements as design variables. Based on this idea the technique called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) is proposed. The method has been successfully applied for linear and non-linear analyses of trusses and truss-like structures, and the results have shown that the approach is much more successful than conventional methods, especially for analyses of non-linear systems. In this study, the application of TPO/MA, with Harmony Search as the selected meta-heuristic algorithm, to cables net system is presented. The results have shown that the method is robust, powerful and accurate.

• The Journal of Information Systems
• /
• v.26 no.4
• /
• pp.107-135
• /
• 2017

Purpose Along with the growth of smart phones and social networking service (SNS), social commerce continues to expand. Although online reviews have become an important source of the information that consumers use to make purchasing decisions, theoretical development and empirical testing in this area are still limited. Thus, there is a need to develop further understanding about the influence of electronic word-of-mouth (eWOM). Drawing upon the heuristic - systematic model (HSM) which is one of the dual-process theories, this study develops a research model that explains key factors influencing consumers' eWOM credibility. Furthermore, this study verifies that consumer's eWOM credibility is a key determinant of eWOM and purchase intentions. Design/methodology/approach The proposed model is empirically tested with 493 users who have experience in WeChat-based social commerce. The structural equation model (SEM) analysis is used to evaluate the research model and hypotheses. Findings The major findings are as follows. First, argument quality of eWOM (a systematic factor) has a positive effect on eWOM credibility. Second, source credibility and recommendation consistency of eWOM (heuristic factors) are positively associated with eWOM credibility. Finally, purchase and eWOM intentions greatly depend on eWOM credibility. These results confirm the effectiveness of HSM in explaining eWOM mechanisms in SNS-based social commerce. The details of findings and implications are presented.

• Journal of Korean Elementary Science Education
• /
• v.29 no.4
• /
• pp.505-514
• /
• 2010

This study aims to explore group inquiry using science writing heuristic in elementary school science. We observed and examined what elementary students experienced in their science classes and moreover how they used their data to support their claims in the view point of scientific epistemology. The research question of this study is 'what is the result of coherence measurement for figuring out the relationship between argument and ground in group inquiry activity?'. To solve the study question, one fifth-grade class at an elementary school was selected. Four lessons in group inquiry using a worksheet of science writing heuristic were videotaped and transcribed. It was found that the hours of group inquiry when students can discuss meaningfully occupied approximately 37 to 45%. When measuring coherence in each lesson by developing a scale from one to five to estimate the coherence of the work sheet designed, the total average was 3.424. It implicated that students had their claims supported by some of their evidences. Although the figure was not enough to assist that they found strong bonds between their claims and data, they started to move forward to epistemological approach in their science lab works.

• Industrial Engineering and Management Systems
• /
• v.4 no.1
• /
• pp.1-22
• /
• 2005

This paper presents new scheduling heuristics, namely Mean Progressive Weighted Tardiness Estimator (MPWT) Heuristic Method and modified priority rules with sequence-dependent setup times consideration. These are designed to solve job shop scheduling problems with new performance measures - progressive weighted tardiness penalties. More realistic constraints, which are inter-machine overlapping sequence-dependent setup times, are considered. In real production environments, inter-machine overlapping sequence-dependent setups are significant. Therefore, modified scheduling generation algorithms of active and nondelay schedules for job shop problems with inter-machine overlapping sequence-dependent setup times are proposed in this paper. In addition, new customer-based measures of performance, which are total earliness and progressive weighted tardiness, and total progressive weighted tardiness, are proposed. The objective of the first experiment is to compare the proposed priority rules with the consideration of sequence-dependent setup times and the standard priority rules without setup times consideration. The results indicate that the proposed priority rules with setup times consideration are superior to the standard priority rules without the consideration of setup times. From the second experiment and the third experiment to compare the proposed MPWT heuristic approach with the efficient priority rules with setup times consideration, the MPWT heuristic method is significantly superior to the Batched Apparent Tardiness Cost with Sequence-dependent Setups (BATCS) rule, and other priority rules based on total earliness and progressive weighted tardiness, and total earliness and tardiness.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.9
• /
• pp.1-8
• /
• 2010

The key challenge in HW/SW co-design is how to choose the appropriate HW/SW partitioning from the vast array of possible options in the mapping set. In this paper we present a unique and efficient approach for addressing this problem known as Customized Heuristic Algorithm for Reducing Mapping Sets(CHARMS). CHARMS uses sensitivity to individual task computational complexity as well the computed weighted values of system performance influencing metrics to streamline the mapping sets and extract the most optimal cases. Using H.263 encoder, we show that CHARMS sieves out 95.17% of the sub-optimal mapping sets, leaving the designer with 4.83% of the best cases to select from for run-time implementation.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.2
• /
• pp.58-65
• /
• 2021

Meta-heuristic algorithms have been developed to efficiently solve difficult problems and obtain a global optimal solution. A common feature mimics phenomenon occurring in nature and reliably improves the solution through repetition. And at the same time, the probability is used to deviate from the regional optimal solution and approach the global optimal solution. This study compares the algorithm created based on the above common points with existed SA and HS to show advantages in time and accuracy of results. Existing algorithms have problems of low accuracy, high memory, long runtime, and ignorance. In a two-variable polynomial, the existing algorithms show that the memory increases and the accuracy decrease. In order to improve the accuracy, the new algorithm increases the number of initial inputs and increases the efficiency of the search by introducing a direction using vectors. And, in order to solve the optimization problem, the results of the last experiment were learned to show the learning effect in the next experiment. The new algorithm found a solution in a short time under the experimental conditions of long iteration counts using a two-variable polynomial and showed high accuracy. And, it shows that the learning effect is effective in repeated experiments.

• Journal of Computing Science and Engineering
• /
• v.7 no.1
• /
• pp.67-80
• /
• 2013

The worst-case execution time (WCET) of each real-time task in multicore processors with shared caches can be significantly affected by inter-thread cache interferences. The worst-case inter-thread cache interferences are dependent on how tasks are scheduled to run on different cores. Therefore, there is a circular dependence between real-time task scheduling, the worst-case inter-thread cache interferences, and WCET in multicore processors, which is not the case for single-core processors. To address this challenging problem, we present an offline real-time scheduling approach for multicore processors by considering the worst-case inter-thread interferences on shared L2 caches. Our scheduling approach uses a greedy heuristic to generate safe schedules while minimizing the worst-case inter-thread shared L2 cache interferences and WCET. The experimental results demonstrate that the proposed approach can reduce the utilization of the resulting schedule by about 12% on average compared to the cyclic multicore scheduling approaches in our theoretical model. Our evaluation indicates that the enhanced scheduling approach is more likely to generate feasible and safe schedules with stricter timing constraints in multicore real-time systems.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.2
• /
• pp.256-263
• /
• 1998

Among process synthesis problems, the heat exchanger network (HEN) has been subjected to the most concentrated effort because this kind of problems was well defined for solving it and 20-30% energy savings could be realized in the present chemical processes. In this paper, we use an evolutionary approach for HEN synthesis because this approach can overcome the local optimum and combine some heuristic rules. The basic evolutionary approach is composed of three parts, that is, initialization step, growth step and mutation step, as in the simulated annealing and genetic algorithm. This algorithm uses the ecological rule that a better cell will live and worse cell should decompose after repeated generations. With this basic concept, a new procedure is developed and a more efficient method is proposed to generate initial solutions. Its effectiveness is shown using test examples.