• Management Science and Financial Engineering
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• v.14 no.2
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• pp.25-44
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• 2008

This paper concentrates on reduction of a Quadratic Fractional Integer Programming Problem (QFIP) to a 0-1 Mixed Linear Programming Problem (0-1 MLP). The solution technique is based on converting the integer variables to binary variables and then the resulting Quadratic Fractional 0-1 Programming Problem is linearized to a 0-1 Mixed Linear Programming problem. It is illustrated with the help of a numerical example and is solved using the LINDO software.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.833-839
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• 2018

This paper presents an analysis and the methodology of optimal operation strategy of the ESS(Energy Storage System) for reduce electricity charges. Electricity charges consist of a basic charge based on the contract capacity and energy charge according to the power usage. In order to use electrical energy at minimal charge, these two factors are required to be reduced at the same time. QP(Quadratic Programming) is appropriate for minimization of the basic charge and LP(Linear Programmin) is adequate to minimize the energy charge. However, the integer variable have to be introduced for modelling of different charge and discharge efficiency of ESS PCS(Power Conversion System), where MILP(Mixed Integer Linear Programming) can be used. In this case, the extent to which the peak load savings is accomplished should be assumed before the energy charge is minimized. So, to minimize the electricity charge exactly, optimization is sequentially performed in this paper, so-called the Two Stage Hybird optimization, where the extent to which the peak load savings is firstly accomplished through optimization of basic charge and then the optimization of energy charge is performed with different charge and discharge efficiency of ESS PCS. Finally, the proposed method is analyzed quantitatively with other optimization methods.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.207-211
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• 2021

This paper addresses a two-stage flexible flow shop scheduling problem in which there is one machine in stage 1 and two identical machines in stage 2. The objective is the minimization of the total completion time. The problem is formulated by a mixed integer quadratic programming (MIQP) and a hybrid simulated annealing (HSA) is proposed to solve the MIQP. The HSA adopts the exploration capabilities of a genetic algorithm and incorporates a simulated annealing to reduce the premature convergence. Extensive computational tests on randomly generated problems are carried out to evaluate the performance of the HSA.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.