• 대한산업공학회지
• /
• 제22권2호
• /
• pp.303-311
• /
• 1996

The Self Healing Ring (SHR) is one of the most Intriguing schemes which provide survivability for telecommunication networks. To design a cost effective SONET ring it is necessary to consider load balancing problems by which the link capacity is determined. The load balancing problem in SONET ring when demand splitting is not allowed is considered in this paper. An efficient algorithm is presented which provides the best solution starting from various Initial solutions. The initial solution is obtained by routing ell demands such that no demands pass through an are In the ring. The proposed algorithm iteratively improves the Initial solution by examining each demand and selecting the maximum load are in its path. The demand whose maximum arc load is biggest is selected to be routed in opposite direction. Computational results show that the proposed algorithm is excellent both in the solution quality and in the computational time requirement. The average error bound of the algorithm is 0.11% of the optimum and compared to dual-ascent approach which has good computational results than other heuristics.

• 전산구조공학
• /
• 제9권3호
• /
• pp.187-197
• /
• 1996

Explicit 직접적분법 알고리듬을 사용하여 Euler기둥의 동적 좌굴거동을 해석할 수 있는 수치해석법을 제시하였다. 평면뼈대 유한요소를 기하학적 비선형 거동과 전체좌굴의 영향을 고려할 수 있도록 보의 대변위 이론으로부터 유도하였고, central difference method를 바탕으로 해석 알고리듬을 개발하였다. 다양한 형상, 크기, 재하시간을 갖는 충격하중에 대하여 Euler기둥의 동적좌굴거동과 고유치 문제를 해석하였다. 수치해석 예제를 통하여 본 연구의 결과를 검증하였다.

• 한국경영과학회지
• /
• 제32권3호
• /
• pp.47-61
• /
• 2007

This paper presents two elimination methods of subtours, which is obtained by applying the Out-of-Kilter algorithm to the sequential ordering problem (SOP) to produce a feasible solution for the SOP. Since the SOP is a kind of asymmetric traveling salesman problem (ATSP) with precedence constraints, we can apply the Out-of-Kilter algorithm to the SOP by relaxing the precedence constraints. Instead of patching subtours, both of two elimination methods construct a feasible solution of the SOP by using arcs constructing the subtours, and they improve solution by running 3-opt and 4-opt at each iteration. We also use a perturbation method. cost relaxation to explore a global solution. Six cases from two elimination methods are presented and their experimental results are compared to each other. The proposed algorithm found 32 best known solutions out of the 34 instances from the TSPLIB in a reasonable time.

• 산업경영시스템학회지
• /
• 제19권39호
• /
• pp.9-18
• /
• 1996

Many algorithms have been developed for optimizing the asymmectric traveling salesman problem known as a representative NP-Complete problem. The most efficient ones of them are branch and bound algorithms based on the subtour elimination approach. To increase efficiency of the branch and bound algorithm. number of decision nodes should be decreased. For this the minimum bound that is more close at the optimal solution should be found or an effective bounding strategy should be used. If the optimal solution has been known, we may apply it usefully to branching. Because a good feasible solution should be found as soon as possible and have similar features of the optimal solution. By the way, the upper bound solution in branch and bound algorithm is most close at the optimal solution. Therefore, the upper bound solution can be used instead of the optimal solution and information of which can be applied to new branching criteria. As mentioned above, this paper will propose an effective branching rule using the information of the upper bound solution in the branch and bound algorithm. And superiority of the new branching rule will be shown by comparing with Bellmore-Malone's one and carpaneto-Toth's one that were already proposed.

• Communications for Statistical Applications and Methods
• /
• 제7권1호
• /
• pp.357-370
• /
• 2000

This study has adapted a genetic algorithm for an optimal design for the first time. the models that was used a simulation are the first and second order response surfaces model, Using an genetic algorithm in D-opimal it is more efficient than previous algorithms to get an object function. Not like other algorithm without any restrictions like troublesome about the initial solution not falling into a local optimal solution it's the most suitable algorithm.

• 대한원격탐사학회지
• /
• 제18권5호
• /
• pp.291-297
• /
• 2002

Optimal allocation of viewers on a terrain in such a wav that the hidden area would be minimized has many practical applications. However, it is impossible in practical sense to evaluate all the possible allocations. In this paper, we propose an optimal allocation of viewers based on genetic algorithm that enables probabilistic search of huge solution space. An experiment for one and three viewers was performed. The algorithm converges to good solutions. Especially, in one viewer case, the algorithm found the best solution.

• 산업경영시스템학회지
• /
• 제18권33호
• /
• pp.111-118
• /
• 1995

This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2008년도 정기 학술대회
• /
• pp.416-421
• /
• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• Management Science and Financial Engineering
• /
• 제4권2호
• /
• pp.1-13
• /
• 1998

The Shortest Path Problem in Time-dependent Networks, where the travel time of each link depends on the time interval, is not realistic since the model and its solution violate the Non-passing Property (NPP:often referred to as FIFO) of real phenomena. Furthermore, solving the problem needs much more computational and memory complexity than the general shortest path problem. A new model for Time-dependent Networks where the flow speeds of each link depend on time interval, is suggested. The model is more realistic since its solution maintains the NPP. Solving the problem needs just a little more computational complexity, and the same memory complexity, as the general shortest path problem. A solution algorithm modified from Dijkstra's label setting algorithm is presented. We extend this model to the problem of Minimum Expected Time Path in Time-dependent Stochastic Networks where flow speeds of each link change statistically on each time interval. A solution method using the Kth-shortest Path algorithm is presented.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
• /
• pp.65-68
• /
• 2000

The problem of selecting a portfolio is to find Un investment plan that achieves a desired return while minimizing the risk involved. One stream of algorithms are based upon mixed integer linear programming models and guarantee an integer optimal solution. But these algorithms require too much time to apply to real problems. Another stream of algorithms are fur a near optimal solution and are fast enough. But, these also have a weakness in that the solution generated can't be guaranteed to be integer values. Since it is not a trivial job to tansform the scullion into integer valued one simutaneously maintaining the quality of the solution, they are not easy to apply to real world portfolio selection. To tackle the problem more efficiently, we propose an algorithm which generates a very good integer solution in reasonable amount of time. The algorithm is tested using Korean stock market data to verify its accuracy and efficiency.