• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.118-128
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• 2017

Uncertain factors in finical markets make the prediction of future returns and risk of asset much difficult. In this paper, a model,assuming the admissible errors on expected returns and risks of assets, assisted in the multiperiod mean variance portfolio selection problem is built. The model considers transaction costs, upper bound on borrowing risk-free asset constraints, cardinality constraints and threshold constraints. Cardinality constraints limit the number of assets to be held in an efficient portfolio. At the same time, threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Because of these limitations, the proposed model is a mix integer dynamic optimization problem with path dependence. The forward dynamic programming method is designed to obtain the optimal portfolio strategy. Finally, to evaluate the model, our result of a meaning example is compared to the terminal wealth under different constraints.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.64-71
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• 2015

In this paper, we present a multi-period 0-1 knapsack problem which has the cardinality constraints. Theoretically, the presented problem can be regarded as an extension of the multi-period 0-1 knapsack problem. In the multi-period 0-1 knapsack problem, there are n jobs to be performed during m periods. Each job has the execution time and its completion gives profit. All the n jobs are partitioned into m periods, and the jobs belong to i-th period may be performed not later than in the i-th period, i = 1, ${\cdots}$, m. The total production time for periods from 1 to i is given by $b_i$ for each i = 1, ${\cdots}$, m, and the objective is to maximize the total profit. In the extended problem, we can select a specified number of jobs from each of periods associated with the corresponding cardinality constraints. As the extended problem is NP-hard, the branch and bound method is preferable to solve it, and therefore it is important to have efficient procedures for solving its linear programming relaxed problem. So we intensively explore the LP relaxed problem and suggest a polynomial time algorithm. We first decompose the LP relaxed problem into m subproblems associated with each cardinality constraints. Then we identify some new properties based on the parametric analysis. Finally by exploiting the special structure of the LP relaxed problem, we develop an efficient algorithm for the LP relaxed problem. The developed algorithm has a worst case computational complexity of order max[$O(n^2logn)$, $O(mn^2)$] where m is the number of periods and n is the total number of jobs. We illustrate a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.857-862
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• 1987

Given a network with k specified vertices bi called centers, a cardinality constrained cover is a family {Bi} of k subsets covering the vertex set of a network, such that each subset Bi corresponds to and contains center bi, and satisfies a given cardinality constraint. A set of cardinality constrained overlapping territories is a cardinality constrained cover such that the total sum of T(B$_{i}$) for all subsets is minimum among all cardinality constrained covers, where T(B$_{i}$) is the summation of the shortest path lengths from center bi to every vertex in B$_{I}$. This paper considers a problem of finding a set of cardinality constrained overlapping territories. and proposes an algorithm for the Problem which has the time and space complexities are O(k$^{3}$$\mid$V$\mid$$^{2}$) and O(k$\mid$V$\mid$+$\mid$E$\mid$), respectively, where V and E are the sets of vertices and edges of a given network, respectively. The concept of overlapping territories has a possibility to be applied to a job assignment problem.oblem.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.95-104
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• 2001

In this paper, we consider a maximin version of the linear programming knapsack problem with extended generalized upper bound (GUB) constraints. We solve the problem efficiently by exploiting its special structure without transforming it into a standard linear programming problem. We present an O(n$^3$) algorithm for deriving the optimal solution where n is the total number of problem variables. We illustrate a numerical example.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.81-90
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• 2008

BCC(Boolean Cardinality Constraint) is to select one boolean variable from n different variables. It is widely used in many areas including software engineering. Thus, many efficient encoding techniques of BCC into CNF have been studied extensively In this paper we analyze some representative encodings with respect to flexibility as well as efficiency. In addition we use a visualization tool to draw the CNF clauses generated from each encodings. Visualizing the clauses exposes a hidden structure in encodings and makes to compare each encodings on the structure level, which is one of the prominent achievement in our work compared to other works. And we apply our analysis on the pigeon-hole problems to have confidence. In our experimental settings, the commander encoding shows the best performance.

• Journal of KIISE:Software and Applications
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• v.35 no.11
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• pp.661-670
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• 2008

In some applications of software engineering such as the verification of software model or embedded program, SAT solver is used. To practical use a SAT solver, a problem is encoded to a CNF formula, but because the formula has lower expressiveness than software models or source codes, optimal CNF encoding is required. In this paper, we propose optimal encoding techniques for the problem of "Selecting adjacent $k{\leq}n$ among n objects," Through experimental results we show the proposed constraint is efficient and correct to solve Japanese puzzle. As we know, this paper is the first study about CNF encoding for adjacency in BCC.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.777-784
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• 2008

Recently in the field of model checking, to overcome the state explosion problem, the method of using a SAT-solver is mainly researched. To use a SAT-solver, the system to be verified is translated into CNF and the Boolean cardinality constraint is widely used in translating the system into CNF. In BCC it is dealt with set of boolean variables, but there is no translating method of the sequence among Boolean variables. In this paper, we propose methods for translating the problem, which is extracting a subsequence with length k from a sequence of Boolean variables, into CNF formulas. Through experimental results, we show that our method is more efficient than using only BCC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1569-1586
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• 2015

This paper considers the optimal cell association and resource allocation for load balancing in a heterogeneous cellular network subject to user's quality-of-service (QoS) constraints. We adopt the proportional fairness (PF) utility maximization formulation which also accommodates the QoS constraints in terms of minimum rate requirements. With equal resource allocation this joint optimization problem is either infeasible or requires relaxation that yields a solution which is difficult to implement. Nevertheless, we show that this joint optimization problem can be effectively solved without any priori assumption on resource allocation and yields a cell association scheme which enforces single BS association for each user. We re-formulated the joint optimization problem as a network-wide resource allocation problem with cardinality constraints. A reweighted heuristic l₁-norm regularization method is used to obtain a sparse solution to the re-formulated problem. The cell association scheme is then derived from the sparsity pattern of the solution, which guarantees a single BS association for each user. Compared with the previously proposed method based on equal resource allocation, the proposed framework results in a feasible cell association scheme and yields a robust solution on resource allocation that satisfies the QoS constraints. Our simulations illustrate the impact of user's minimum rate requirements on cell association and demonstrate that the proposed approach achieves load balancing and enforces single BS association for users.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.77-89
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• 2022

In this paper, we propose a system which automatically generates SHACL schemas to describe and validate RDF knowledge graphs constructed by RML mappings. Unlike existing studies, the proposed system generates the schemas based on not only RML mapping rules but also metadata extracted from RML mapping input data in various formats such as CSV, JSON, XML or databases. Therefore, our schemas include the constraints on data type, string length, value range and cardinality, which were not present in the existing schemas. And we solves the problem with "repeated properties" which overlooked in existing studies. Through a conformance test consisting of 297 cases, we show that the proposed system generates correct constraints for the graphs. The proposed system can contribute to automation of the tedious and error-prone existing manual validation processes.

• Korean Management Science Review
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• v.29 no.1
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• pp.57-71
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• 2012

In this paper, we deal with a sector investment strategy by implementing the black-litterman model that incorporates expert evaluation and sector rotation momentum. Expert evaluation analyzes the relative performance of the industry sector compared with the market, while sector rotation momentum reflects the price impact of significant sector anomaly. In addition, we consider the portfolio impact of sector cardinality and weight constraints within the context of mean-variance portfolio optimization. Finally, we demonstrate the empirical viability of the proposed sector investment strategy with KOSPI 200 data.