• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.211-225
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• 2004

A mixed type dual to a programming problem containing support functions in a objective as well as constraint functions is formulated and various duality results are validated under generalized convexity and invexity conditions. Several known results are deducted as special cases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.720-730
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• 2002

Automated design of large structures requires efficient and accurate optimization algorithms because of a large number of design variables and design constraints. The objective of this study is to examine the characteristics of the Kreisselmeier -Steinhauser envelope function and to investigate va tidily of accumulating constraint functions into a small number of constraint functions or even into a single constraint function. The commercial package DOT is adopted as a local optimizer. The optimum results using the envelope function are compared with those of the conventional method for a number of numerical examples and the differences between them are shown to be negligible.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.75-106
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• 2008

In this paper, we derive necessary optimality conditions for a continuous programming problem in which both objective and constraint functions contain support functions and is, therefore, nondifferentiable. It is shown that under generalized invexity of functionals, Karush-Kuhn-Tucker type optimality conditions for the continuous programming problem are also sufficient. Using these optimality conditions, we construct dual problems of both Wolfe and Mond-Weir types and validate appropriate duality theorems under invexity and generalized invexity. A mixed type dual is also proposed and duality results are validated under generalized invexity. A special case which often occurs in mathematical programming is that in which the support function is the square root of a positive semidefinite quadratic form. Further, it is also pointed out that our results can be considered as dynamic generalizations of those of (static) nonlinear programming with support functions recently incorporated in the literature.

• Journal of IKEEE
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• v.13 no.1
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• pp.71-76
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• 2009

As one of the most important requirements for wireless sensor networks, prolonging network lifetime can be realized by minimizing energy consumption in cluster heads as well as sensor nodes. While most of the previous researches have focused on the energy of sensor nodes, we devote our attention to cluster heads because they are most dominant source of power consumption in the cluster-based sensor networks. Therefore, we seek to minimize energy consumption by minimizing the maximum(MINMAX) energy dissipation at each cluster heads. This work requires energy-efficient clustering of the sensor nodes while satisfying given energy constraints. In this paper, we present a constraint satisfaction modeling of cluster-based routing in a heterogeneous sensor networks because mixed integer programming cannot provide solutions to this MINMAX problem. Computational experiments show that substantial energy savings can be obtained with the MINMAX algorithm in comparison with a minimum total energy(MTE) strategy.

• ETRI Journal
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• v.36 no.3
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• pp.506-509
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• 2014

This paper studies the energy efficiency power allocation for cognitive radio networks based on uplink orthogonal frequency-division multiplexing. The power allocation problem is intended to minimize the maximum energy efficiency measured by "Joule per bit" metric, under total power constraint and robust aggregate mutual interference power constraint. However, the above problem is non-convex. To make it solvable, an equivalent convex optimization problem is derived that can be solved by general fractional programming. Then, a robust energy efficiency power allocation scheme is presented. Simulation results corroborate the effectiveness of the proposed methods.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.941-946
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• 2005

It is essential for project managers to make schedule adjustment based on their professional experience, in terms of rescheduling action discussed in this paper. This paper discusses the topics of resource-constrained construction rescheduling by modifying the concepts of manufacturing rescheduling. Moreover, the influence factors of construction rescheduling problems are investigated and identified in this paper. According to initial schedule plan and present progress, a new rescheduling mechanism based on Constraint Programming (CP) techniques is developed to reschedule projects with the objective of minimizing total project cost or duration, under three rescheduling policies. Through case study, the behavior of three different rescheduling policies is analyzed and discussed in this paper.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.2
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• pp.87-95
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and il usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.451-458
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• 2003

Dynamic Programming Matching (DPM) is a mathematical optimization technique for sequentially structured problems, which has, over the years, played a major role in providing primary algorithms in pattern recognition fields. Most practical applications of this method in signature verification have been based on the practical implementational version proposed by Sakoe and Chiba [9], and is usually applied as a case of slope constraint p = 0. We found, in this case, a modified version of DPM by applying a heuristic (forward seeking) implementation is more efficient, offering significantly reduced processing complexity as well as slightly improved verification performance.

• The Journal of Fisheries Business Administration
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• v.45 no.2
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• pp.61-72
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• 2014

This study aimed to empirically investigate the applicability of ecosystem-based TAC (Total Allowable Catch) fisheries management targeting the large purse seine fishery where multi-species are regulated by TAC. Using a linear programming, the optimal fishing effort and the catch amount by species which maximize fishing profits were analyzed under the constraint condition of catch limits by species. Analytical results showed that an application of TAC on only chub mackerel would have negative impacts on fish stocks such as hairtail and jack mackerel by increasing the level of fishing effort to achieve its allocated catch limit. However, under the constraint condition of catch limits of all species, it was shown that optimal catches of all species were achieved within their catch limits. It implies the importance of ecosystem-based management considering biological and technical interactions of species those were excluded in the traditional single species fisheries management.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.13-14
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• 2017

본 논문에서는 강의 시간표 최적화를 위한 제약 프로그래밍의 적용 방안을 제시한다. 제약 프로그래밍은 제약 만족 문제를 해결하기 위한 기법으로 대상 문제를 결정 변수, 도메인, 제약조건으로 표현한다. 본 논문에서는 시간표 작성 최적화 문제의 결정 변수로 강의실, 요일, 교시를 사용하였으며, 추가로 요일과 교시를 결합한 변수를 사용함으로써 보다 쉽게 제약 조건을 표현할 수 있도록 하였다. 또한 제약 프로그래밍에 의해 도출된 초기해를 또 다시 제약 프로그래밍을 통해 반복적으로 개선함으로써 더 좋은 강의 시간표를 작성할 수 있도록 하였다. 특정 학과의 강의 시간표 문제를 대상으로 한 실험 결과, 본 논문에서 제안한 방법을 통해 보다 빠른 시간 내에 초기해를 도출할 수 있을 뿐 아니라 최종적으로 더 좋은 해의 도출이 가능함을 확인하였다.