• Korean Management Science Review
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• v.32 no.1
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• pp.49-63
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• 2015

This paper investigates an aircraft-target assignment problem in consideration of deconfliction. The aircraft-target assignment problem is the problem to assign available aircrafts and weapons to targets that should be attacked, where the objective function is to minimize the total expected damage of aircrafts. Deconfliction is the way of dividing airspaces for aircraft flight to ensure the safety while performing the mission. In this paper, mixed integer programming model is suggested, where it considers deconfliction between aircrafts. However, the suggested MIP model is non-linear and limited to get solution for large size problem. The 2-phase decomposition model is suggested for efficiency and computation, where in the first phase target area is divided into sectors for deconfliction and in the second phase aircrafts and weapons are assigned to given targets for minimizing expected damage of aircraft. The proposed decomposition model shows outperforms the model developed for comparison in the computational experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1057-1064
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• 2009

This paper introduces a new approach based on a quantum-inspired evolutionary algorithm (QEA) to solve unit commitment (UC) problems. The UC problem is a complicated nonlinear and mixed-integer combinatorial optimization problem with heavy constraints. This paper proposes a bounded quantum evolutionary algorithm (BQEA) to effectively solve the UC problems. The proposed BQEA adopts both the bounded rotation gate, which is simplified and improved to prevent premature convergence and increase the global search ability, and the increasing rotation angle approach to improve the search performance of the conventional QEA. Furthermore, it includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in the UC problems. Since the excessive spinning reserve can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed BQEA, it is applied to the large-scale power systems of up to 100-unit with 24-hour demand.

• Industrial Engineering and Management Systems
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• v.13 no.4
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• pp.398-407
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• 2014

Shortages and delays in a humanitarian logistics system can contribute to the pain and suffering of survivors or other affected people. Humanitarian logistics budgets should be sufficient to prevent such shortages or delays. Unlike commercial supply chain systems, the budgets for relief supply chain systems should be able to satisfy demand. This study describes a comprehensive model in an effort to satisfy the total relief demand by minimizing logistics operations costs. We herein propose a strategic model which determines the locations of distribution centers and the total inventory to be stocked for each distribution center where a flood or other catastrophe may occur. The proposed model is formulated and solved as a mixed-integer programming problem that integrates facility location and inventory decisions by considering capacity constraints and time restrictions in order to minimize the total cost of relief operations. The proposed model is then applied to a real flood case involving 47 disaster areas and 13 distribution centers in Thailand. Finally, we discuss the sensitivity analysis of the model and the managerial implications of this research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4087-4102
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• 2014

Two-way relaying is an effective way of improving system spectral efficiency by making use of physical layer network coding. However, energy efficiency in OFDM-based bidirectional relaying with asymmetric traffic requirement has not been investigated. In this study, we focused on subcarrier transmission mode selection, bit loading, and power allocation in a multicarrier single amplified-and-forward relay system. In this scheme, each subcarrier can operate in two transmission modes: one-way relaying and two-way relaying. The problem is formulated as a mixed integer programming problem. We adopt a structural approximation optimization method that first decouples the original problem into two suboptimal problems with fixed subcarrier subsets and then finds the optimal subcarrier assignment subsets. Although the suboptimal problems are nonconvex, the results obtained for a single-tone system are used to transform them to convex problems. To find the optimal subcarrier assignment subsets, an iterative algorithm based on subcarrier ranking and matching is developed. Simulation results show that the proposed method can improve system performance compared with conventional methods. Some interesting insights are also obtained via simulation.

• Journal of Communications and Networks
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• v.15 no.1
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• pp.102-110
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• 2013

This paper considers a cooperative orthogonal frequency division multiple access (OFDMA)-based cognitive radio network where the primary system leases some of its subchannels to the secondary system for a fraction of time in exchange for the secondary users (SUs) assisting the transmission of primary users (PUs) as relays. Our aim is to determine the cooperation strategies among the primary and secondary systems so as to maximize the sum-rate of SUs while maintaining quality-of-service (QoS) requirements of PUs. We formulate a joint optimization problem of PU transmission mode selection, SU (or relay) selection, subcarrier assignment, power control, and time allocation. By applying dual method, this mixed integer programming problem is decomposed into parallel per-subcarrier subproblems, with each determining the cooperation strategy between one PU and one SU. We show that, on each leased subcarrier, the optimal strategy is to let a SU exclusively act as a relay or transmit for itself. This result is fundamentally different from the conventional spectrum leasing in single-channel systems where a SU must transmit a fraction of time for itself if it helps the PU's transmission. We then propose a subgradient-based algorithm to find the asymptotically optimal solution to the primal problem in polynomial time. Simulation results demonstrate that the proposed algorithm can significantly enhance the network performance.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.74-84
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• 2013

Modern companies have been facing devastating impacts from unexpected events such as demand uncertainties, natural disasters, and terrorist attacks due to the increasing global supply chain complexity. This paper proposes a multi stage logistic network model under disruption risk. To formulate the problem practically, we consider the effect of continuity rate, which is defined as a percentage of ability of the facility to provide backup allocation to customers in the abnormal situation and affect the investments and operational costs. Then we vary the fixed charge for opening facilities and the operational cost according to the continuity rate. The operational level of the company decreases below the normal condition when disruption occurs. The backup source after the disrup-tion is recovered not only as soon as possible, but also as much as possible. This is a concept of the business continuity plan to reduce the recovery time objective such a continuity rate will affect the investments and op-erational costs. Through numerical experiments, we have shown the proposed idea is capable of designing a resilient logistic network available for business continuity management/plan.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.105-114
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• 2022

We examine berth allocation problems in tidal bulk ports with an objective of minimizing the demurrage and dispatch associated berthing cost. In the proposed optimization model inventory (or stock) level constraints are considered so as to satisfy the service level requirements in bulk terminals. It is shown that the mathematical programming formulation of this research provides improved schedule resolution and solution accuracy. We also show that the conventional big-M method of standard resource allocation models can be exempted in tidal bulk ports, and thus the computational efficiency can be significantly improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3204-3217
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• 2023

In this paper, the downlink of the multicast based spatial modulation systems is investigated. Specifically, physical layer multicasting is introduced to increase the number of access users and to improve the communication rate of the spatial modulation system in which only single radio frequency chain is activated in each transmission. To minimize the bit error rate (BER) of the multicast based spatial modulation system, a joint optimizing algorithm of antenna selection and multicast precoding is proposed. Firstly, the joint optimization is transformed into a mixed-integer non-linear program based on single-stage reformulation. Then, a novel iterative algorithm based on the idea of branch and bound is proposed to obtain the quasioptimal solution. Furthermore, in order to balance the performance and time complexity, a low-complexity deflation algorithm based on the successive convex approximation is proposed which can obtain a sub-optimal solution. Finally, numerical results are showed that the convergence of our proposed iterative algorithm is between 10 and 15 iterations and the signal-to-noise-ratio (SNR) of the iterative algorithm is 1-2dB lower than the exhaustive search based algorithm under the same BER accuracy conditions.

• Korean Chemical Engineering Research
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• v.47 no.1
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• pp.119-126
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• 2009

A district energy system plays very important role to fulfill energy demand in regional areas. This paper diagnoses the necessity of the development of an economical operation system for the efficient operation of district energy plants located in Seoul. The effect anticipated from the use of the optimal operation system is also analyzed. Production and consumption of energy are estimated for the district energy plants at Suseo, Bundang, Ilwon and Jungang located near in Seoul, Korea. The problem is formulated as a mixed integer linear programming(MILP) problem where the objective is to minimize the overall cost of the district energy system. From the results of numerical simulations we can see that the energy efficiency is improved due to the application of the optimal operation conditions provided by the proposed model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.