• Korean Management Science Review
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• v.29 no.2
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• pp.91-103
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• 2012

Revenue management problems originated in the 1970's in the context of the airline industry have been successfully introduced in airline industries. It has started on the capacity control by booking classes for available seats, and has been recognized as a powerful tool to maximize the total revenue. Changing customer behavior and airline market environments, however, has required a new mechanism for improving the revenue. Dynamic pricing is one of innovative tools which is to adjust prices according to the market status. In this paper, we consider a dynamic pricing and seat control problem for discrete time horizon. The problem can be modeled as a stochastic programming problem. Applying the linear approximation technique and given the price set for each time, we suggest a mixed Integer Programming model to solve our problem efficiently. From the simulation results, we can find our model makes good performance and can be expanded to other comprehensive problems.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.3
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• pp.403-410
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• 2009

The aim of this research is to develop the Bees Algorithm named 'the dynamic allocated Bees Algorithm' for multi-objective problem, especially in order to be suit for Pareto optimality. In addition two new neighbourhood search methods have been developed to produce enhanced solutions for a multi-objective problem named 'random selection neighbourhood search' and 'weighted sum neighbourhood search' and they were compared with the basic neighbourhood search in the dynamic allocated Bees Algorithm. They were successfully applied to an Environmental/Economic (electric power) dispatch (EED) problem and simulation results presented for the standard IEEE 30-bus system and they were compared to those obtained using other approaches. The comparison shows the superiority of the proposed dynamic allocated Bees Algorithms and confirms its suitability for solving the multi-objective EED problem.

• The Mathematical Education
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• v.46 no.3
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• pp.331-349
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• 2007

This study aims to improve the teaching and learning method on the conic sections. To do that the researcher analyzed the impact of dynamic geometry software on students' problem solving of the conic sections. Students often say, "I have solved this kind of problem and remember hearing the problem solving process of it before." But they often are not able to resolve the question. Previous studies suggest that one of the reasons can be students' tendency to approach the conic sections only using algebra or analytic geometry without the geometric principle. So the researcher conducted instructions based on the geometric and historico-genetic principle on the conic sections using dynamic geometry software. The instructions were intended to find out if the experimental, intuitional, mathematic problem solving is necessary for the deductive process of solving geometric problems. To achieve the purpose of this study, the researcher video taped the instruction process and converted it to digital using the computer. What students' had said and discussed with the teacher during the classes was checked and their behavior was analyzed. That analysis was based on Branford's perspective, which included three different stage of proof; experimental, intuitive, and mathematical. The researcher got the following conclusions from this study. Firstly, students preferred their own manipulation or reconstruction to deductive mathematical explanation or proving of the problem. And they showed tendency to consider it as the mathematical truth when the problem is dealt with by their own manipulation. Secondly, the manipulation environment of dynamic geometry software help students correct their mathematical misconception, which result from their cognitive obstacles, and get correct ones. Thirdly, by using dynamic geometry software the teacher could help reduce the 'zone of proximal development' of Vigotsky.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.1976-1997
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• 2016

This paper investigates the problem of co-tier interference mitigation for dynamic small- cell networks, in which the load of each small-cell varies with the number of active associated small-cell users (SUs). Due to the fact that most small-cell base stations (SBSs) are deployed in an ad-hoc manner, the problem of reducing co-tier interference caused by dynamic loads in a distributed fashion is quite challenging. First, we propose a new distributed channel allocation method for small-cells with dynamic loads and define a dynamic interference graph. Based on this approach, we formulate the problem as a dynamic interference graph game and prove that the game is a potential game and has at least one pure strategy Nash equilibrium (NE) point. Moreover, we show that the best pure strategy NE point minimizes the expectation of the aggregate dynamic co-tier interference in the small-cell network. A distributed dynamic learning algorithm is then designed to achieve NE of the game, in which each SBS is unaware of the probability distributions of its own and other SBSs' dynamic loads. Simulation results show that the proposed approach can mitigate dynamic co-tier interference effectively and significantly outperform random channel selection.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.71-84
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• 2009

This paper considers a remanufacturing and purchasing planning problem, in which either used products(or wastes) are remanufactured or remanufactured products(or final products) are purchased to satisfy dynamic demands of remanufactured products over a discrete and finite time horizon. Also, as remanufactured products are purchased more than or equal to a special quantity Q, a discount price policy is applied. The problem assumes that the related cost(remanufacturing and inventory holding costs of used products, and the purchasing and inventory holding costs of remanufactured products) functions are concave and backlogging is not allowed. The objective of this paper is to determine the optimal remanufacturing and purchasing policy that minimizes the total cost to satisfy dynamic demands of remanufactured products. This paper characterizes the properties of the optimal policy and then, based on these properties, presents a dynamic programming algorithm to find the optimal policy. Also, a network-based procedure is proposed for the case of a large quantity of low cost used products. A numerical example is then presented to demonstrate the procedure of the proposed algorithm.

• The Mathematical Education
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• v.60 no.4
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• pp.543-554
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• 2021

The dynamic geometric environment plays a positive role in solving students' geometric problems. Students can infer invariance in change through dragging, and help solve geometric problems through the analysis method. In this study, the continuous spectrum of the dynamic geometric environment can be used to solve problems of students. The continuous spectrum can be used in the 'Understand the problem' of Polya(1957)'s problem solving stage. Visually representation using continuous spectrum allows students to immediately understand the problem. The continuous spectrum can be used in the 'Devise a plan' stage. Students can define a function and explore changes visually in function values in a continuous range through continuous spectrum. Students can guess the solution of the optimization problem based on the results of their visual exploration, guess common properties through exploration activities on solutions optimized in dynamic geometries, and establish problem solving strategies based on this hypothesis. The continuous spectrum can be used in the 'Review/Extend' stage. Students can check whether their solution is equal to the solution in question through a continuous spectrum. Through this, students can look back on their thinking process. In addition, the continuous spectrum can help students guess and justify the generalized nature of a given problem. Continuous spectrum are likely to help students problem solving, so it is necessary to apply and analysis of educational effects using continuous spectrum in students' geometric learning.

• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.945-952
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• 2010

In this paper, existence criteria of one solution to a nonlinear first-order periodic boundary value problem of impulsive dynamic equation on time scales are obtained by using the well-known Schaefer fixed-point theorem.

• IE interfaces
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• v.24 no.2
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• pp.112-118
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• 2011

This paper introduces one type of prize collecting travelling salesman problem with time windows (PCTSPTW), proposes a mixed integer programming model for the problem, and shows that the problem can be reduced to the elementary shortest path problem with time windows and capacity constraints (ESPPTC). Then, a new dynamic programming algorithm is proposed to solve ESPPTC quickly. Computational results show the effectiveness of the proposed algorithm.

• Journal of applied mathematics & informatics
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• v.24 no.1_2
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• pp.399-409
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• 2007

In this paper we use iterative dynamic programming in the discrete case to solve a wide range of the nonlinear equations systems. First, by defining an error function, we transform the problem to an optimal control problem in discrete case. In using iterative dynamic programming to solve optimal control problems up to now, we have broken up the problem into a number of stages and assumed that the performance index could always be expressed explicitly in terms of the state variables at the last stage. This provided a scheme where we could proceed backwards in a systematic way, carrying out optimization at each stage. Suppose that the performance index can not be expressed in terms of the variables at the last stage only. In other words, suppose the performance index is also a function of controls and variables at the other stages. Then we have a nonseparable optimal control problem. Furthermore, we obtain the path from the initial point up to the approximate solution.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.