• Journal of Information Technology Applications and Management
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2011

As the sales volume of online shops increases, the job burden in the back-offices of the online shops also increases. Order picking is the most labor-intensive operation among the jobs in a back-office and mid-size pure click online shops are experiencing the time delay and complexity in order picking nowadays while fulfilling their customers' orders. Those warehouses of the mid-size shops are based on manual systems, and as order pickings are repeated, the warehouses get a mess and lots of products in those warehouses are getting missing, which results in severe delay in order picking. To overcome this kind of problem in online clothing shops, we research a methodology to locate warehousing products. When products arrive at a warehouse, they are packed into a box and located on a rack in the warehouse. At this point, the operator should determine the box to be put in and the location on the rack for the box to be put on. This problem could be formulated as an Integer Programming model, but the branch-and bound algorithm to solve the IP model requires enormous computation, and sometimes it is even impossible to get a solution in a proper time. So, we relaxed the problem, developed a set of heuristics as a methodology to get a semi-optimum in an acceptable time, and proved by an experiment that the solutions by our methodology are satisfactory and acceptable by field managers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.163-170
• /
• 2006

This paper presents a approach to the measurement of efficiency. Data envelopment analysis(DEA), as it is called, has particular applicability in the service sector. Applying mathematical programming techniques, DEA enables relative efficiency ratings to be derived within a set of analysed units. This paper investigates the efficiency employing DAE-CCR Model and DEA-BCC Model on data for 15 container terminals covering 1998$^{\sim}$2005 in Korea Results of this paper, suggests to some plan for operation strategy in Container terminals.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.574-575
• /
• 2007

This paper proposes a method for choosing the best transmission system expansion plan using nodal/bus delivery marginal rate criterion ($BMR_k$) defined newly in this paper. The objective method minimizes a total cost which is an investment budget for constructing new transmission lines subject to the $BMR_k$ which means a nodal deterministic reliability level requirement at specified load point. The proposed method models the transmission system expansion problem as an integer programming problem. It solves for the optimal strategy using a branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Test results on an existing 21-bus system are included in the paper. It demonstrated the suitability of the proposed method for solving the transmission system expansion planning problem in competitive electricity market environment.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.4
• /
• pp.566-578
• /
• 2017

For optimizing the operation plan when strike packages attack multiple targets, this article suggests a new mathematical model and a parallel hybrid genetic algorithm (PHGA) as a solution methodology. In the model, a package can assault multiple targets on a sortie and permitted the use of mixed munitions for a target. Furthermore, because the survival probability of a package depends on a flight route, it is formulated as a mixed integer programming which is synthesized the models for vehicle routing and weapon-target assignment. The hybrid strategy of the solution method (PHGA) is also implemented by the separation of functions of a GA and an exact solution method using ILOG CPLEX. The GA searches the flight routes of packages, and CPLEX assigns the munitions of a package to the targets on its way. The parallelism enhances the likelihood seeking the optimal solution via the collaboration among the HGAs.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.10a
• /
• pp.321-322
• /
• 2018

본 연구에서는 학습자의 프로그래밍 학습능력을 향상시키기 위하여, 학습효과에 영향을 미치는 교수 전략을 규명하는 것을 목표로 하였다. 프로그래밍 학습효과에 영향을 미치는 교수전략으로는 진도 적절성, 난이도 적절성, 교수자의 개입수준, 유머 사용으로 측정하였으며 학습효과는 성취도와 만족도를 측정하였다. A 전문대학의 컴퓨터공학과 110명의 학생을 대상의 설문을 실시한 후 학습효과에 대한 교수전략의 영향을 분석한 결과, 난이도 적절성이 성취도에 영향을 끼치며, 난이도 적절성과 교수자의 개입수준이 수업만족도에 영향을 미치는 것으로 규명되었다. 이에 따라 프로그래밍의 학습효과를 향상시킬 수 있는 시사점을 제안하였다.

• Proceedings of the KIEE Conference
• /
• 2004.11b
• /
• pp.297-300
• /
• 2004

This paper proposes a method for choosing the best composite power system expansion plan considering probabilistic reliability criterion. The proposed method was modeled as the minimization of the investment budget (economics) for constructing new transmission lines subject to not only deterministic(demand constraint) but also probabilistic reliability criterion(LOLE) with considering the uncertainties of the system elements. This is achieved by modeling the power system expansion problem as an integer programming one. The method solves for the optimal strategy using a probabilistic theory based branch and bound method that utilizes a network flow approach and the maximum flow-minimum cut set theorem. Although the proposed method is applied to a simple sample study, the test results demonstrate a fact that the proposed method is suitable for solving the power system expansion planning problem subject to practical uncertainties for future.

• Journal of Korean Society for Quality Management
• /
• v.37 no.4
• /
• pp.61-70
• /
• 2009

This paper introduces quantitative tools for evaluating the relative efficiency of Career & Counseling Jobs in universities. As tools, it uses Data Envelopment Analysis (DEA) developed by Charnes and Cooper. It finally selects 29 DMUs which are listed on the Ministry Of Education, Science And Technology(http://academyinfo.go.kr). We measures the technical efficiency of each DMU with the use of DEA-CRS, rather then DEA-VRS because DEA-CRS not only compares relative efficiencies but also implicitly considers economies of scale based on the assumption of linearity. We run a linear programming model Frontier Analyst Program for the estimation of the relative efficiencies of each DMU. The model also indicates the precise amount of inefficiencies for each input, which mean how much inputs are wasted for a given output and how much the university is inefficiently operated. This analysis helps to give guideline for the organization to construct a futureoriented operational strategy and also to show clear picture of contents of mismanagement for the past. The details of mismanagement are to be identified, analysed and finally corrected.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.176-176
• /
• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• Journal of Information Processing Systems
• /
• v.17 no.4
• /
• pp.721-736
• /
• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• Journal of Korea Multimedia Society
• /
• v.21 no.11
• /
• pp.1305-1316
• /
• 2018

Graphical user interface(GUI) has a very important role to interact with software users. However, designing and coding of GUI are tedious and pain taking processes. In many studies, the researchers are trying to convert GUI elements or widgets to code or describe formally their structures by help of domain knowledge of stochastic methods. In this paper, we propose the GUI elements detection approach based on object detection strategy using deep neural networks(DNN). Object detection with DNN is the approach that integrates localization and classification techniques. From the experimental result, if we selected the appropriate object detection model, the results can be used for automatic code generation from the sketch or capture images. The successful GUI elements detection can describe the objects as hierarchical structures of elements and transform their information to appropriate code by object description translator that will be studied at future.