• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.143-150
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• 2019

We focus on the weapon target assignment and fire scheduling problem (WTAFSP) with the objective of minimizing the makespan, i.e., the latest completion time of a given set of firing operations. In this study, we assume that there are m available weapons to fire at n targets (> m). The artillery attack operation consists of two steps of sequential procedure : assignment of weapons to the targets; and scheduling firing operations against the targets that are assigned to each weapon. This problem is a combination of weapon target assignment problem (WTAP) and fire scheduling problem (FSP). To solve this problem, we define the problem with a mixed integer programming model. Then, we develop exact algorithms based on a dynamic programming technique. Also, we suggest how to find lower bounds and upper bounds to a given problem. To evaluate the performance of developed exact algorithms, computational experiments are performed on randomly generated problems. From the results, we can see suggested exact algorithm solves problems of a medium size within a reasonable amount of computation time. Also, the results show that the computation time required for suggested exact algorithm can be seen to increase rapidly as the problem size grows. We report the result with analysis and give directions for future research for this study. This study is meaningful in that it suggests an exact algorithm for a more realistic problem than existing researches. Also, this study can provide a basis for developing algorithms that can solve larger size problems.

• Korean Institute of Interior Design Journal
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• v.16 no.2 s.61
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• pp.63-70
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• 2007

Analogy in problem solving is similarity-based reasoning facilitated by verbal and visual operation. This similarity-based reasoning generally supports initial phase of idea search. Therefore, this study intends to infer the types of problem solving by tracing the analogy use of verbal and visual representation through a experimental research. According to the result of this research, the types of problem solving by analogy are classified into 'evolving', 'divergent', and 'poor conversion' type. Firstly, 'evolving type' is distinguished between 'combination type' associated different contents to develope a new design and 'transformation type' associated similar words and sketches to be continuously revised and developed. In these types usually structural analogy rather than surface analogy is used. Secondly, in 'divergent type' associated words or sketches are individually represented, and among them one design solution is selected. In this type usually surface analogy is used. Thirdly, in 'poor conversion type' interaction between verbal representation and visual representation does not go on smoothly, and the generation of idea is poor. In here surface analogy is mostly used. These findings could form the basis of skill development of idea generation and conversion in design education.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.52-59
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• 2008

This paper describes the secant method for solving the economic dispatch (ED) problem with generator constraints and transmission losses. The ED problem is an important optimization problem in the economic operation of a power system. The proposed algorithm involves selection of minimum and maximum incremental costs (lambda values) and then the evaluation of optimal lambda at required power demand is done by secant method. The proposed algorithm has been tested on a power system having 6, 15, and 40 generating units. Studies have been made on the proposed method to solve the ED problem by taking 120 and 200 units with generator constraints. Simulation results of the proposed approach were compared in terms of solution quality, convergence characteristics, and computation efficiency with conventional methods such as lambda iterative method, heuristic methods such as genetic algorithm, and meta-heuristic methods like particle swarm optimization. It is observed from different case studies that the proposed method provides qualitative solutions with less computational time compared to various methods available in the literature.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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• 2007

The optimal power flow(OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• Journal of Agricultural Extension & Community Development
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• v.23 no.3
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• pp.271-283
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• 2016

The purpose of this study is to verify the effect of urban agricultural practice on the factors of adolescent problem behavior. The main findings of this study are as follows. First, as a result of measuring quantitative research, urban agricultural practice improves 16.7% of self-worth, reduces 28.1% of depression, and improves 19.4% of sociality, the factors of adolescent problem behavior, and statistical significance is confirmed in all lower measures. Second, as a result of measuring qualitative research, there are overwhelmingly many cases that most items change positively or are maintained in sentence completion test. Third, a positive change of mind in adolescent communication, cognitive and problem- solving abilities, and participation is confirmed through observation of program participation behavior. It is significant for this study to confirm that urban agricultural experiential activity has a positive impact on the factors of adolescent problem behavior, and to verify educational effects and social values of urban agricultural practice including the necessity of development and operation of urban agricultural program to solve youth problems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.649-651
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• 2017

A clustering problem is one of the organizational problems to improve the network lifetime and scalability in wireless ad-hoc networks. This problem is a difficult combinatorial optimization problem associated with the design and operation of these networks. In this paper, we propose an efficient clustering algorithm to maximize the network lifetime and consider scalability in wireless ad-hoc networks. The clustering problem is known to be NP-hard. We thus solve the problem by using optimization approaches that are able to efficiently obtain high quality solutions within a reasonable time for a large size network. The proposed algorithm selects clusterheads and configures clusters by considering both nodes' power and the clustering cost. We evaluate this performance through some experiments in terms of nodes' transmission energy. Simulation results indicate that the proposed algorithm performs much better than the existing algorithms.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.69-75
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• 2015

This paper suggests heuristic polynomial time algorithm for crew scheduling problem that is a kind of optimization problems. This problem has been solved by linear programming, set cover problem, set partition problem, column generation, etc. But the optimal solution has not been obtained by these methods. This paper sorts transit costs $c_{ij}$ to ascending order, and the task i and j crew paths are merged in case of the sum of operation time ${\Sigma}o$ is less than day working time T. As a result, we can be obtain the minimum number of crews $_{min}K$ and minimum transit cost $z=_{min}c_{ij}$. For the transit cost of specific number of crews $K(K>_{min}K)$, we delete the maximum $c_{ij}$ as much as the number of $K-_{min}K$, and to partition a crew path. For the 5 benchmark data, this algorithm can be gets less transit cost than state-of-the-art algorithms, and gets the minimum number of crews.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.199-206
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• 2008

The introduction of competition in electricity markets emphasizes the importance of sufficient transmission capacities to guarantee effective power transactions. Therefore, for the economic and stable electric power system operation, transmission security constrains should be incorporated into the dispatch scheduling problem. With the intent to solve this problem, we decompose a dispatch scheduling problem into a master problem(MP) and several subproblems(SPs) using Benders decomposition. The MP solves a general optimal power flow(OPF) problem while the SPs inspect the feasibility of OPF solution under respective transmission line contingencies. If a dispatch scheduling solution given by the MP violates transmission security constraints, then additional constraints corresponding to the violations are imposed to the MP. Through this iterative process between the MP and SPs, we derive an optimal dispatch schedule incorporating the post-contingency corrective rescheduling. In addition, we consider interruptible loads as active control variables since the interruptible loads can participate as generators in competitive electricity markets. Numerical examples demonstrate the efficiency of the proposed algorithm.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.

• Journal of Korean Port Research
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• v.10 no.2
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• pp.7-15
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• 1996

A berth assignment problem has a direct impact on assessment of charges made to ships and goods. A berth can be assigned to incoming vessels and operated in tow different ways: as a common user berth, as a preference berth. A common user berth is a berth that any ship calling at a port may be permitted to use according to her time of arrival and to priorities as determined by the port authority. In this paper, we concerned with various types of mathematical programming models for a berth assignment problem to achive an efficient berth operation. In this paper, we focus on a reasonable berth assignment programming in a public container terminal in consideration of trade-off between server and user. We propose a branch and bound algorithm & heuristic algorithm for solving the problem. We suggest three models of berth assignment to minimizing the objective functions such as total port time, total berthing time and maximum berthing time by using a revised Maximum Position Shift(MPS) with which the trade-off between servers and users can be considered. The berth assignment problem is formulated by min-max and 0-1 integer programming and developed heuristic algorithm to solve the problem more easily instead of branch and bound method. Finally, we gave the numerrical solutions of the illustrative examples.