• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.211-220
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• 2013

This paper proposes an algorithm that easily finds an optimal solution for linear bottleneck assignment problems. It is either threshold or augmenting path algorithm that is generally used to solve the bottleneck assignment problem. This paper proposes a reverse-delete algorithm that follows 2 steps. Firstly, the algorithm deletes the maximum cost in a given matrix until it renders a single row or column. Next, the algorithm improves any solution that contains a cost exceeding the threshold value $c^*_{ij}$. Upon its application to 28 balanced assignment problems and 7 unbalanced problems, the algorithm is found to be both successful and simple.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.601-604
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• 1997

In this paper, a pole assignment problem in the unit disk for a linear discrete system is discussed. The analysis is based on the Luenberger's canonical form and Gershgorin's disk. The proposed method for pole assignment is convenient for a linear time invariant discrete system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.6
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• pp.51-58
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• 2015

This paper suggests assignment-swap algorithm with time complexity O(mn) to obtain the optimal solution for large-scale of transportation problem (TP) with incomplete cost lists. Generally, the TP with complete cost lists can be solved with TSM (Transportation Simplex Method). But, we can't be solved for large-scale of TP with TSM. Especially. It is hard to solve for large-scale TP with incomplete cost lists using TSM. Therefore, experts simply using commercial linear programming package. Firstly, the proposed algorithm applies assignment strategy of transportation quantity to ascending order of transportation cost. Then, we reassign from surplus of supply to shortage of demand. Secondly, we perform the 2-opt and 1-opt swap optimization to obtain the optimal solution. Upon application to $31{\times}15$ incomplete cost matrix problem, the proposed assignment-swap algorithm more improves the solution than LINGO of commercial linear programming.

• Management Science and Financial Engineering
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• v.2 no.1
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• pp.19-51
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• 1996

We suggest a distributed framework for task assignment in the computer-controlled shop floor where each of the resource agents and part agents acts like an independent profit maker. The job allocation problem is formulated as a linear programming problem. The LP formulation is analyzed to provide a rationale for the distributed task assignment procedure. We suggest an auction based negotiation procedure including a price-based bid construction and a price revising mechanism. The performance of the suggested procedure is compared with those of an LP formulation and conventional dispatching procedures by simulation experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.641-646
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• 2001

Eigenstructure assignment is a typical method with the capability of consideration of the time-domain specifications in designing a linear control system. In this paper, we propose a new method for eigenstructure to achieve desired eigenvectors more precisely than with the conventional method. In the proposed method, the conventional eigenstructure assignment problem is interpreted as a constrained optimization one, and it converted into an unconstrained optimization problem to deal with the problem easily. Numerical examples are presented to illustrate the proposed flexible eigenstructure assignment method.

• Journal of IKEEE
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• v.23 no.4
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• pp.1353-1359
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• 2019

It is presented in this paper that the static output feedback (SOF) pole-assignment problem of some linear time-invariant systems can be completely resolved by parametrization in real Grassmann space. For the real Grassmannian parametrization, the so-called Plucker matrix is utilized as a linear matrix formula formulated from the SOF variable's coefficients of a characteristic polynomial constrained in Grassmann space. It is found that the exact SOF pole assignability is determined by the linear independency of columns of Plucker sub-matrix and by full-rank of that sub-matrix. It is also presented that previous diverse pole-assignment methods and various computation algorithms of the real SOF gains for 2-input, 2-output, 4^th order systems are unified in a deterministic way within this real Grassmannian parametrization method.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.56-69
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• 2005

In this paper, it is clearly shown that the two well-known necessary and sufficient conditions mp n as generic static output feedback pole-assignment and mp + d(m+p) n+d as generic minimum d-th order dynamic output feedback pole-assignment on complex field, unbelievably, do not match up each other in strictly proper linear systems. For the analysis, a diagram analysis is newly created (which is defined by the analysis of 'convoluted rectangular/dot diagrams' constructed via node-branch conversion of the signal flow graphs of output feedback gain loops). Under this diagram analysis, it is proved that the minimum d-th order dynamic output feedback compensator for pole-assignment in m-input, p-output, n-th order systems is quantitatively decomposed into static output feedback compensator and its associated d number of arbitrary 1st order dynamic elements in augmented (m+d)-input, (p+d)-output, (n+d)-th order systems. Total configuration of the mismatched data is presented in a Table.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.939-947
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• 2008

The paper proposes a new formulation of the output feedback pole assignment problem. In this formulation, a unified approach is presented for solving the pole assignment problem with various additional objectives. These objectives include optimizing a variety of performance indices, and imposing constraints on the output feedback matrix structure, e.g. decentralized structure. Conditions for the existence of the output feedback are discussed. However, the thrust of the paper is on the development of a convergent pole assignment algorithm. It is shown that when exact pole assignment is not possible, the method can be used to place the poles close to the desired locations. Examples are provided to illustrate the method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.11
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• pp.787-794
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• 2019

In this paper, a Mixed Integer Linear Programming(MILP) approach for solving optimal Weapon-Target Assignment(WTA) problem of multiple dissimilar Closed-In Weapon Systems (CIWS) is proposed. Generally, WTA problems are formulated in nonlinear mixed integer optimization form, which often requires impractical exhaustive search to optimize. However, transforming the problem into a structured MILP problem enables global optimization with an acceptable computational load. The problem of interest considers defense against several threats approaching the asset from various directions, with different time of arrival. Moreover, we consider multiple dissimilar CIWSs defending the asset. We derive a MILP form of the given nonlinear WTA problem. The formulated MILP problem is implemented with a commercial optimizer, and the optimization result is proposed.