• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.163-171
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• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• The Journal of Korean Academic Society of Nursing Education
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• v.3
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• pp.26-33
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• 1997

Problem-Based Learning(PBL) is at the forefront of educational reform. The acceptance of PBL as an educational approach with wide application represents a major change in thinking about educational processes and their relationships to the wider community. In 1969, PBL as a method was introduced at the Medical School of McMaster University in Hamilton, Canada. The most important advantages in PBL are acquiring knowledge that can be retrieved and applied, learning to learn(self-directed learning) and learning to analyze and solve Problems. PBL is widely used within the sector where it had its origin, namely health profession education. A generally accepted starting point in the development of a problem-based curriculum is the set of professional competencies of future graduates, which describe the typical problems professionals have to deal with. Formulating learning objectives highly depends on the format and content of the presented problems. Contrary to that, in a classic course in higher education, it is customary that teachers express objectives in a compulsory subject matter. Curricula which advocate problem-based learning generally use case studies in the form of paper cases, simulations and real patients with the intention of stimulating classroom discussion of clinical and basic science concepts within a problem-solving framework. One goal of using paper cases is to stimulate the learning of basic science within a clinical situation. Through self-directed study the students solve problems and explore the psycho-social dimensions within the cases. The general outcome based on the program evaluation research of PBL is that PBL students respond positively about the learning experience. In summary, PBL is a curriculum design and a teaching/learning strategy which simultaneously develops higher order thinking and disciplinary knowledge bases and skills by placing students in the active role of practitioners(or problem solvers) confronted with a situation(ill-structured problem) which reflects the real world.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.591-599
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• 2020

Deep learning is widely utilized to classify or recognize objects of real-world. An abundance of data is trained on high-performance computers and a trained model is generated, and then the model is loaded in an inferencer. The inferencer is used in various environments, so that it may cause unrecognized objects or low-accuracy objects. To solve this problem, real-world objects are collected and they are trained periodically. However, not only is it difficult to immediately improve the recognition rate, but is not easy to learn an inferencer on embedded devices. We propose a real-time handwriting recognizer based on partial learning on embedded devices. The recognizer provides a training environment which partially learn on embedded devices at every user request, and its trained model is updated in real time. As this can improve intelligence of the recognizer automatically, recognition rate of unrecognized handwriting increases. We experimentally prove that learning and reasoning are possible for 22 numbers and letters on RK3399 devices.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.113-116
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• 2000

The purpose of this study is to present a method for determining the k most vital arcs in shortest path problem using an evolutionary algorithm. The problem of finding the k most vital arcs in shortest path problem is to find a set of k arcs whose simultaneous removal from the network causes the greatest increase in the total length of shortest path. The problem determining the k most vital arcs in shortest path problem has known as NP-hard. Therefore, in order to deal with the problem of real world the heuristic algorithm is needed. In this study we propose to the method of finding the k-MVA in shortest path problem using an evolutionary algorithm which known as the most efficient algorithm among heuristics. For this, the expression method of individuals compatible with the characteristics of shortest path problem, the parameter values of constitution gene, size of the initial population, crossover rate and mutation rate etc. are specified and then the effective genetic algorithm will be proposed. The method presented in this study is developed using the library of the evolutionary algorithm framework (EAF) and then the performance of algorithm is analyzed through the computer experiment.

• Journal of Engineering Education Research
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• v.8 no.1
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• pp.99-112
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• 2005

Problem-Based Learning is focused, experiential learning organized around the investigation and resolution of messy, real-world problem. It is both a curriculum organizer and instructional strategy, two complementary processes. The PBL model developed in this study was composed the two components of Problem Design(curriculum organizer) and Problem Implementation(instructional strategy). The basic process of Problem Implementation Model were composed the 8 steps ; 1) the identification of problem, 2) the specification of problem, 3) the exploration and generation for solution, 4) the selecting of best idea, 5) the specific planning of best idea, 6) the implementation and realization, 7) the evaluation, 8) the applying and reflection.

• IE interfaces
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• v.20 no.2
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• pp.186-194
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• 2007

Recent deregulation of Korean electricity industry has made each power generation company pay more attention to maximizing its own profit instead of minimizing the overall system operation cost while guaranteeing system security. Electricity power generation problem is typically defined as the problem of determining both the on and off status and the power generation level of each generator under the given fuel constraints, which has been known as Profit-Based Unit Commitment (PBUC) problem. To solve the PBUC problem, the previous research mostly focused on devising Lagrangian Relaxation (LR) based heuristic algorithms due to the complexity of the problem and the nonlinearity of constraints and objectives. However, these heuristic approaches have been reported as less practical in real world applications since the computational run time is usually quite high and it may take a while to implement the devised heuristic algorithms as software applications. Especially when considering long-term planning problem which spans at least one year, the complexity becomes higher. Therefore, this paper proposes an explicit column generation algorithm using power generation patterns and the proposed algorithm is successfully applied to a Korean power generation company. The proposed scheme has a robust structure so that it is expected to extend general PBUC problems.

• Korean Journal of Computational Design and Engineering
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• v.13 no.5
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• pp.362-371
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• 2008

Generally, a PLM education program in university consists of lectures of theory, software lab and software development raining as an advanced subject. Most industries want more than these, such as practical problem solving capabilities, teamwork skills and engineering communications including human relationship, rhetoric, technical writing, presentation and etc. Problem-Based Learning is a problem-stimulated and student-centered teaming method, and an innovative education strategy for collaborative and self-directed learning by applying real world problems. Education paradigm changes from "teaching" to "learning" accomplished by team working, and students are encouraged to develop, present, explain and defense their ideas, suggestions or solutions of a problem, and the "cooperative teaming" proceeds spontaneously during team operations. Co-operative education program is an into-grated academic model and a structured educational program combining classroom learning with productive work experience in a field related to a student's academic or career goals. Based on the partnership between academic institutions and industries, students are engaged in real and productive "work" in the industry, in contrast with merely observing. In this paper, PBL with Co-op program is suggested as an effective approach for PLM education, and we made and operated a PBL-based education course with industry co-op program. The Co-op education in industry accompanied with the PBL course in university can improve practical problem solving capabilities of students, including modeling and management of P3R(Product, Process, resource and Plant) using commercial PLM software tools. By the result, we found this to be an effective strategy for helping students, professors and industries succeed in engineering education, especially PLM area.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.75-86
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• 2008

A remarshalling is one of the operational strategies considered importantly at a port container terminal for the fast ship operations and heighten efficiency of slacking yard. The remarshalling rearranges the containers scattered at a yard block in order to reduce the transfer time and the rehandling time of container handling equipments. This Paper deals with the rearrangement problem, which decides to where containers are transported considering time value of each operations. We propose the mixed integer programming model minimizing the weighted total operation cost. This model is a NP-hard problem. Therefore we develope the heuristic algorithm for rearrangement problem to real world adaption. We compare the heuristic algorithm with the optimum model in terms of the computation times and total cost. For the sensitivity analysis of configuration of storage and cost weight, a variety of scenarios are experimented.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.3
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• pp.73-82
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• 1999

In this paper, we address a single machine non-preemptive n-job scheduling problem to minimize the sum of earliness and tardiness with different release times and due dates. To solve the problem, we propose a genetic algorithm with new crossover and mutation operators to find the job sequencing. For the proposed genetic algorithm, the optimal pair of crossover and mutation rates is investigated. To illustrate the suitability of genetic algorithm, solutions of genetic algorithm are compared with solutions of exhaustive enumeration method in small size problems and tabu search method in large size problems. Computational results demonstrate that the proposed genetic algorithm provides the near-optimal job sequencing in the real world problem.

• Korean Management Science Review
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• v.25 no.1
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• pp.93-106
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• 2008

This study is focused on the problem of seat allocation for group travel demand in airlines. We first explain the characteristic of group demand and its seat allocation process. The group demand in air travel markets can be classified into two types : incentive and package groups. Allocating seats for group demand depends on the types of group demand and the relationship between airlines and travel agents. In this paper we concentrate on the package group demand and develop an optimization model for seat allocation on the demand to maximize the total revenue. With some assumptions on the demand distribution and the linear approximation technique, we develop a mixed IP model for solving our problem optimally. From the computational experiments, we can find our optimization model can be applied well for real-world application.