• School Mathematics
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• v.16 no.1
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• pp.1-17
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• 2014

Mathematical creativity in school mathematics is connected with problem solving. The purpose of this study was to analyse elementary students' the mathematical creativity using multiple solution tasks which required to solve a mathematical problem in different ways. For this research, I examined and analyzed the response to four multiple solution tasks according to the evaluation system of mathematical creativity which consisted of the factors of creativity(fluency, flexibility, originality). The finding showed that mathematical creativity was different between students with greater clarity. And mathematical creativity in tasks was different. So I questioned the possibility of analysis of students' the mathematical creativity in mathematical areas. According to the evaluation system of mathematical creativity of this research, mathematical creativity was proportional to the fluency. But the high fluency and flexibility was decreasing originality because it was easy for students to solve multiple solution tasks in the same ways. So, finding of this research can be considered to make the criterion in both originality in rare and mathematical aspects.

• Journal of Elementary Mathematics Education in Korea
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• v.18 no.1
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• pp.83-103
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• 2014

The purpose of this paper is to find a method of measuring mathematical creativity reasonably. In the pursuit of this purpose, we designed four multiple solution tasks that consist of two kinds of open tasks; 'tasks with open solutions' and 'tasks with open answers'. We collected data by conducting an interview with a gifted fifth grade student using the four multiple solution tasks we designed and analyzed mathematical creativity of the student using Leikin's model(2009). Research results show that the mathematical creativity scores of two students who suggest the same solutions in a different order may vary. The more solutions a student suggests, the better score he/she gets. And fluency has a stronger influence on mathematical creativity than flexibility or originality of an idea. Leikin's model does not consider the usefulness nor the elaboration of an idea. Leikin's model is very dependent on the tasks and the mathematical creativity score also varies with each marker.

• School Mathematics
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• v.19 no.3
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• pp.481-494
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• 2017

Tasks of multiple solutions have been said to be suitable for the cultivation of mathematical creativity. However, studies on the fact that multiple solutions presented by students are useful or meaningful, and students' thoughts while finding multiple solutions are very short. In this study, we set goals to confirm the qualitative differences among the multiple solutions presented by the students and, if present, from the viewpoint of mathematical creativity. For this reason, after presenting the set of tasks of the two versions to eight mathematically gifted students of the second-grade middle school, we analyzed qualitative differences that appeared among the solutions. In the study, there was a difference among the solution presented first and the solutions presented later, and qualitatively substantial differences in terms of flexibility and creativity. In this regard, it was concluded that the need to account for such qualitative differences in designing and applying multiple solutions should be considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.530-539
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• 2007

Many construction projects such as highways, pipelines, tunnels, and high-rise buildings typically contain repetitive activities. Research has shown that the Critical Path Method (CPM) is not efficient in scheduling linear construction projects that involve repetitive tasks. Linear Scheduling Method (LSM) is one of the techniques that have been developed since 1960s to handle projects with repetitive characteristics. Although LSM has been regarded as a technique that provides significant advantages over CPM in linear construction projects, it has been mainly viewed as a graphical complement to the CPM. Studies of scheduling linear construction projects with resource consideration are rare, especially with multiple resource constraints. The objective of this proposed research is to explore a resource assignment mechanism, which assigns multiple critical resources to all activities to minimize the project duration while satisfying the activities precedence relationship and resource limitations. Resources assigned to an activity are allowed to vary within a range at different stations, which is a combinatorial optimization problem in nature. A heuristic multiple resource allocation algorithm is explored to obtain a feasible initial solution. The Simulated Annealing search algorithm is then utilized to improve the initial solution for obtaining near-optimum solutions. A housing example is studied to demonstrate the resource assignment mechanism.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.75-94
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• 2005

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet. in spite of their potential usefulness, the models are little used in real-world decision making since the model solution processes are too complex for ordinary users to get accustomed. This paper proposes an intelligent and flexible model-solver integration framework that enables the user to solve decision problems using multiple models and solvers without having precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a decision support system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.

• East Asian mathematical journal
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• v.34 no.4
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• pp.429-449
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• 2018

The purpose of this study is to investigate how the mathematical creativity of middle school mathematical gifted students is represented through the process of problem posing activities. For this goal, they were asked to pose real-world problems similar to the tasks which had been solved together in advance. This study demonstrated that just 2 of 15 pupils showed mathematical giftedness as well as mathematical creativity. And selecting mathematically creative and gifted pupils through creative problem-solving test consisting of problem solving tasks should be conducted very carefully to prevent missing excellent candidates. A couple of pupils who have been exerting their efforts in getting private tutoring seemed not overcoming algorithmic fixation and showed negative attitude in finding new problems and divergent approaches or solutions, though they showed excellence in solving typical mathematics problems. Thus, we conclude that it is necessary to incorporate problem posing tasks as well as multiple solution tasks into both screening process of gifted pupils and mathematics gifted classes for effective assessing and fostering mathematical creativity.

• Management Science and Financial Engineering
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• v.6 no.2
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• pp.47-67
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• 2000

It is discussed how to assign delivery tasks to automated guided vehicles (AGVs) for multiple container cranes in automated container terminals. The primary goal of dispatching AGVs is to complete all the lading and discharging operations as early as possible, and the secondary goal is to minimize the total travel distance of AGVs. It is assumed that AGVs are not dedicated to a specific container crane but shared among multiple cranes. A mathematical formulation is developed and a heuristic algorithm is suggested to obtain a near optimal solution with a reasonable amount of computational time. The single-cycle and the dual-cycle operations in both the seaside and the landside operations are analyzed. The effects of pooling AGVs for multiple container cranes on the performance of an entire AGV system are also analyze through a numerical experiment.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.3
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• pp.1-16
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• 2008

This paper considers a scheduling problem to minimize the total tardiness in the two-stage assembly-type flowshop. The system is composed of multiple fabrication machines in the first stage and a final-assembly machine in the second stage. Each job consists of multiple tasks, each task is performed on the fabrication machine specified in advance. After all the tasks of a job are finished, the assembly task can be started on the final-assembly machine. The completion time of a job is the time that the assembly task for the job is completed. The objective of this paper is to find the optimal schedule minimizing the total tardiness of a group of jobs. In the problem analysis, we first derive three solution properties to determine the sequence between two consecutive jobs. Moreover, two lower objective bounds are derived and tested along with the derived properties within a branch-and-bound scheme. Two efficient heuristic algorithms are also developed. The overall performances of the proposed properties, branch-and-bound and heuristic algorithms are evaluated through numerical experiments.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2311-2319
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• 1999

We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.