• Progress in Superconductivity and Cryogenics
• /
• v.18 no.1
• /
• pp.50-54
• /
• 2016

In this paper, a hybrid method is proposed to design an air-core superconducting solenoid system for 6 T axial uniform magnetic field using Niobium Titanium (NbTi) superconducting wire. In order to minimize the volume of conductor, the hybrid optimization method including a linear programming and a nonlinear programming was adopted. The feasible space of solenoid is divided by several grids and the magnetic field at target point is approximated by the sum of magnetic field generated by an ideal current loop at the center of each grid. Using the linear programming, a global optimal current distribution in the feasible space can be indicated by non-zero current grids. Furthermore the clusters of the non-zero current grids also give the information of probable solenoids in the feasible space, such as the number, the shape, and so on. Applying these probable solenoids as the initial model, the final practical configuration of solenoids with integer layers can be obtained by the nonlinear programming. The design result illustrates the efficiency and the flexibility of the hybrid method. And this method can also be used for the magnet design which is required the high homogeneity within several ppm (parts per million).

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• 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.

• Journal of KIISE:Software and Applications
• /
• v.31 no.6
• /
• pp.829-839
• /
• 2004

Methods based on integer programming have been shown to be very effective in solving various crew pairing optimization problems. However, their applicability is limited to problems with linear constraints and objective functions. Also, those methods often require an unacceptable amount of time and/or memory resources given problems of larger scale. Heuristic methods such as neighborhood search, on the other hand, can handle large-scaled problems without too much difficulty and can be applied to problems having any form of objective functions and constraints. However, neighborhood search often gets stuck at local optima when faced with complex search spaces. This paper presents ,i hybrid algorithm of neighborhood search and integer programming, which nicely combines the advantages of both methods. The hybrid algorithm has been successfully tested on a large-scaled crew pairing optimization problem for a real subway line.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.42 no.3
• /
• pp.1-12
• /
• 2017

In this paper, we generalize lifted inequalities to a 0-1 mixed-integer bilinear covering set with linear terms. This work is motivated by the observation that Generalized Bilinear Inequality (GBI) occurs in the Branch and Bound process. We find some conditions and prove the subadditivity of lifting functions for lifting to be sequence-independent. Using the theoretical results, we develop facet-defining inequalities for a GBI-defined set through three steps of lifting.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.19 no.2
• /
• pp.1-19
• /
• 1994

A generalized network problem is a special class of linear programming problem whose coefficient matrix contains at most two nonzero elements per column. A generalized network problem with 0-1 flow restrictions is called an integer generalized network(IGN) problem. In this paper, we presented a branch and bound algorithm for the IGN that uses network relaxation. To improve the procedure, we develop various strategies, each of which employs different node selection criterion and/or branching variable selection criterion. We test these solution strategies and compare their efficiencies with LINDO on 70 randomly generated problems.

• Journal of the Korean Mathematical Society
• /
• v.43 no.2
• /
• pp.297-309
• /
• 2006

In this paper, we consider cooperative games arising from integer domination problem on graphs. We introduce two games, ${\kappa}-domination$ game and its monotonic relaxed game, and focus on their cores. We first give characterizations of the cores and the relationship between them. Furthermore, a common necessary and sufficient condition for the balancedness of both games is obtained by making use of the technique of linear programming and its duality.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.8
• /
• pp.67-75
• /
• 2013

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, and lower multi-path distortion. To further utilize vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subchannel and bit allocation among users. In this paper, we compare the performance of the linear programming dual of the 0-1 integer programming formulation with the existing convex optimization approach for the optimal subchannel and bit allocation problem of the multiuser OFDM. Utilizing tight lower bound provided by the LP dual formulation, we develop a primal heurisitc algorithm based on the LP dual solution. The performance of the primal heuristic is compared with MAO, ESA heuristic solutions, and integer programming solution on MATLAB simulation on a system employing M-ary quadrature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multi-paths.

• Korean Management Science Review
• /
• v.32 no.4
• /
• pp.109-133
• /
• 2015

This paper considers the allocation and engagement scheduling of air defense missiles by using MIP (mixed integer programming). Specifically, it focuses on developing a realistic MIP model for a real battle situation where multiple enemy missiles are headed toward valuable defended assets and there exist multiple air defense missiles to counteract the threats. In addition to the conventional objective such as the minimization of surviving target value, the maximization of total intercept altitude is introduced as a new objective. The intercept altitude of incoming missiles is important in order to minimize damages from debris of the intercepted missiles and moreover it can be critical if the enemy warhead contains an atomic or chemical bomb. The concept of so called the time window is used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. Lastly, the model is extended to simulate the situation where the guidance radar, which guides a defense missile to its target, has the maximum guidance capacity. The initial mathematical model developed contains several non-linear constraints and a non-linear objective function. Hence, the linearization of those terms is performed before it is solved by a commercially available software. Then to thoroughly examine the MIP model, the model is empirically evaluated with several test problems. Specifically, the models with different objective functions are compared and several battle scenarios are generated to evaluate performance of the models including the extended one. The results indicate that the new model consistently presents better and more realistic results than the compared models.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2196-2203
• /
• 1999

This paper describes the bus scheduling problem and register optimization method in datapath synthesis. Scheduling is process of operation allocation to control steps in order to minimize the cost function under the given circumstances. For that purpose, we propose some formulations to minimize the cost function for bus assignment to get an optimal and minimal cost function in hardware allocations. Especially, bus and register minimization technique are fully considered which are the essential topics in hardware allocation. Register scheduling is done after the operation and bus scheduling. Experiments are done with the DFG model of fifth-order digital ware filter to show its effectiveness. Structural integer programming formulations are used to solve the scheduling problems in order to get the optimal scheduling results in the integer linear programming environment.

• Information Systems Review
• /
• v.5 no.2
• /
• pp.203-217
• /
• 2003

Constraint Programming and Optimization have developed in different fields to solve common problems in real world. In particular, constraint propagation and linear Programming are their own fundamental and complementary techniques with the potential for integration to benefit each other. This intersection has evoked the efforts to combine both for a solution method to combinatorial optimization problems. Attempts to combine them have mainly focused on incorporating either technique into the framework of the other with traditional models left intact. This paper argues that integrating both techniques into an old modeling fame loses advantages from another and the integration should be molded in a new framework to be able to exploit advantages from both. The paper propose a declarative modeling framework in which the structure of the constraints indicates how constraint programming and optimization solvers can interact to solve problems.