• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.21-29
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• 2015

The set partitioning problem is a well-known NP-hard combinatorial optimization problem, and it is formulated as an integer programming model. This paper proposes an Integer Programming-based Local Search for solving the set partitioning problem. The key point is to solve the set partitioning problem as the set covering problem. First, an initial solution is generated by a simple heuristic for the set covering problem, and then the solution is set as the current solution. Next, the following process is repeated. The original set covering problem is reduced based on the current solution, and the reduced problem is solved by Integer Programming which includes a specific element in the objective function to derive the solution for the set partitioning problem. Experimental results on a set of OR-Library instances show that the proposed algorithm outperforms pure integer programming as well as the existing heuristic algorithms both in solution quality and time.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.109-123
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• 1995

Weight of the factors to be considered in layout development with improvement-type heuristics is discussed in this paper. Determination of weight is especially important if it is designed to be used with an improvement-type heuristic. It is same as the value of variable coefficients in a multi-objective function as well as indices for departmental switching orders in the heuristic. Various weights are examined through computer simulation to verify whether the numerical values collected from machinery industries can be used as weights. And then a method to be used for searching an optimum or optimum-tending layout for machinery industries is presented using the weight.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.11 no.17
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• pp.39-46
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• 1988

This Paper is concerned with a man-multi machine assignment problem to minimize idle time. Assuming different types of semi-automatic machines with deterministic service-processing time, the problem approaches by sequential dependent one operator can handle several machines, where determine optimal range the cost of idle labor and machine time is to be minimized in preparing a work schedule. The Procedure, to establish man-machine assignment model, and it's results are: (1) Objective function to minimize opportunity loss cost, which is happened by idle time, is verified recursive process through heuristic method. (2) The algorithm, is programmed by BASIC language for personal computer, and a numerical example is given to illustrate the heuristic algorithm. This study will be helpful to enhance productivity of Shopflooras a result of increasing the efficiencies of both operator and machine.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.441-451
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• 2014

This article addresses an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using the artificial bee colony algorithm. The objective function is adapted to enhance the overall system static voltage stability index and to achieve maximum net yearly savings. Load variations have been considered to optimally scope the fixed and switched capacitors required. The numerical results are compared with those obtained using recent heuristic methods and show that the proposed approach is capable of generating high-grade solutions and validated viability.

• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.19-28
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• 2017

Recently the cargo transportation is increasing according to lager containerships in the container terminal. Thus, the various ways(such as efficient vehicle scheduling and minimizing delay time) are applied to increase productivity to handle the increasing cargo transportation in the container terminal. In this paper, the optimized model(Solvers) is applied to improve the existing heuristic method as a way of increasing productivity. The experimental design is that the result of two objective functions(minimizing travel and delay time of the empty vehicle) is compared to the result of the existing heuristic method by six sample problems. As a result of the two objective function experiments, the optimized model draws 5.3% more improved performance than the heuristic method in four of six problem samples.

• Journal of Korean Society for Quality Management
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• v.50 no.2
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• pp.265-286
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• 2022

Purpose: The time and cost of a project activity exists in a selected mode and there is a quality level for the selected mode, and the time and cost of the current activity is determined by the quality level of the preceding activity. When an activity is a predecessor activity of an activity, it is characterized as a trade-off problem in which the time and cost of the activity are determined according to the quality level of the activity. Methods: A neighbor search heuristic algorithm obtains a solution by (1) randomly determining the mode, quality level, and assignment order for each activity. (2) get a solution by improving the solution by changing the possible modes and quality levels; (3) to find a solution by improving the solution from the point where it is feasible to advance the start time. Here, Case[1] is a method to find the optimal solution value after repeating (1). Case [2] is a method for finding a solution including (1) and (2). Case [3] refers to a method for finding solutions including (1), (2), and (3). Results: It can be seen that the value of the objective function presented by the algorithm changes depending on how the model of the heuristic algorithm is designed and applied. In other words, it suggests the importance of algorithm design and proves the importance of the quality problem of activities in the project schedule. Conclusion: A study significance of the optimization algorithm and the heuristic algorithm was applied to the effect of the quality of the preceding activity on the duration and cost of itself and the succeeding activity, which was not addressed in the project schedule problem.

• Speech Sciences
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• v.14 no.2
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• pp.105-114
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• 2007

The performance of speaker identifier is severely degraded in noisy environments. A study suggested the concept of observation membership for enhancing performances of speaker identifier with noisy speech [1]. The method scaled observation probabilities of input speech by observation identification values decided by SNR. In the paper [1], the authors suggested heuristic parameter values for membership function. In this paper we attempt to apply particle swarm optimization (PSO) for obtaining the optimal parameters for speaker identification in noisy environments. With the speaker identification experiments using the ETRI database we prove that the optimization approach can yield better performance than using only the original membership function.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.195-205
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• 2002

Crew scheduling is the problem of pairing crews with each of the vehicles in operation during a certain period of time. A typical procedure of crew schedule optimization consists of enumerating all possible pairings and then selecting the subset which can cover all the operating vehicles, with the goal of minimizing the number of pairings in the subset. The linear programming approach popularly adopted for optimal selection of pairings, however, is not applicable when the objective function cannot be expressed in a linear form. This paper proposes a method of integrating integer programming and heuristic search to solve difficult crew scheduling problems in which the objective function cannot be expressed in linear form and at the same time the number of crews available is limited. The role of heuristic search is to improve the incomplete solution generated by integer programming through iterative repair. Experimental results show that our method outperforms human experts in terms of both solution quality and execution time when applied to real world crew scheduling Problems which can hardly be solved by traditional methods.