• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.1007-1017
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• 1997

The existing survivability measure is defined as the only ratio of survival traffic volume on the given traffic demand. In this paper we suggest a new network survivability evaluation model. Sinceit depends on the importance of traffic, we can evaluatethe affect of telecommunication disaster. With the suggested evaluation model we formulate optimization models for restoration paths and traffic assinment on them. The optimization models are represented as mixed integer programming problems, which are difficult to find exact solutions. We develop heuristic algorithms according to the optimization models and apply them to an example network with 10 nodes and 17 links.

• Asian Journal of Innovation and Policy
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• v.8 no.1
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• pp.122-140
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• 2019

Providing free primary care to everyone is an important goal pursued by many countries under universal health care programs. Countries like India need to efficiently utilize their limited capacities towards this purpose. Unfortunately, due to a variety of reasons, patients incur substantial travel and out-of-pocket expenses for getting primary care from publicly-funded facilities. We propose a set-covering optimization model to assist health policy-makers in managing existing capacity in a better way. Decision-making should consider upgrading centers with better potential to reduce patient expenses and reallocating capacities from less preferred facilities. A multinomial logit choice model is used to predict the preferences. In this article, a brief background and literature survey along with the mixed integer linear programming (MILP) optimization model are presented. The working of the model is illustrated with the help of numerical experiments.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.118-123
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• 2004

In this study, genetic algorithm mixed binary and real encoding is proposed to deal with design variables of various types. And that is applied to optimum design of Multi-stage gear drive. Design of pressure vessel which is mixed discrete and continuous variables is applied to verify reasonableness of proposed genetic algorithm. The proposed genetic algorithm is applied for the gear ratio optimization and the volume minimization of geared motor which is used in field. In result, it shows that the volume has decreased about 8% compared with the existing geared motor.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.32-37
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• 2015

In the fields of plant layout optimization, the main goal is to minimize the construction cost including pipelines as satisfying all constraints such as safety and operating issues. However, what is the lacking of considerations in previous researches is to consider proper safety and maintenance spaces for a complex plant. Based on the mathematical programming, MILP(Mixed Integer Linear Programming) problems including various constraints can be formulated to find the optimal solution which is to achieve the best economic benefits. The objective function of this problem is the sum of piping cost, pumping cost and area cost. In general, many conventional optimization solvers are used to find a MILP problem. However, it is really hard to solve this problem due to complex inequality and equality constraints, since it is impossible to use the derivatives of objective functions and constraints. To resolve this problem, the PSO (Particle Swarm Optimization), which is one of the representative sampling approaches and does not need to use derivatives of equations, is employed to find the optimal solution considering various complex constraints in this study. The EO (Ethylene Oxide) plant is tested to verify the efficacy of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.89-97
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• 2016

This paper addresses the problem of the construction planning of artillery positions, for which we present an optimization model and propose a heuristic algorithm to solve problems of practical size. The artillery position construction plan includes the assignment of engineers to support the artillery and the schedule of the support team construction sequence. Currently, in the army, managers construct the plan based on their experience. We formulate the problem as a mixed integer program and present a heuristic that utilizes the decomposition of the mixed integer model. We tested the efficacy of the proposed algorithm by conducting computational experiments on both small-size test problems and large-size practical problems. The average optimality gap in the small-size test problem was 6.44% in our experiments. Also, the average computation time to solve the large-size practical problems consisting of more than 200 artillery positions was 79.8 seconds on a personal computer. The result of our computational experiments shows that the proposed approach is a viable option to consider for practical use.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.2
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• pp.16-26
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• 2010

The transmission valuation methodology we propose here captures the interaction between generation and transmission investment decisions recognizing that a transmission expansion can impact the profitability of new resources investment, so that a methodology should consider both the objectives of investors in resources and the transmission planner. In this perspective, this paper purports to develop the mixed-integer programming based transmission expansion planning optimization software, which is well designed to determine the construction time and place of new generators, transmission lines, and substations as well as their capacities to minimize total expenditures related to their investment and operations while meeting technical constraints such as capacity margin, constitution ratio of power resources, spinning reserves, energy and fuel constraints, transmission line outages and losses, pi-type branching, and so on. Finally, Garver's simple system is adopted to validate not simply the accuracy but the efficiency of the proposed model in this paper.

• Journal of the Korean Geographical Society
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• v.49 no.3
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• pp.423-437
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• 2014

Districting is a spatial decision making process to make a new regional framework for affecting human activities. Natural barriers such as rivers and mountains located within a reorganized district may reduce the efficiency of reorganized human activities. This implies that it is necessary to consider boundary characteristics in a districting process. The purpose of this research is to develop a new spatial optimization model based on boundary characteristics for districting problems. The boundary characteristics are evaluated as continuous value expressing the possibility of combining adjacent two basic spatial units rather than a dichotomous value with 1 or 0 and are defined as an objective function in the model. In addition, the model has explicitly formulated contiguity constraints as well as constraints enforcing demand balance among districts such as population and area. The boundary attributes are categorized into physical and relational characteristics. Suitability analysis is used to combine various variables related to each boundary characteristic and to evaluate the coupling possibility between two neighboring basic units. The model is applied to an administrative redistricting problem. The analytical results demonstrate that various boundary characteristics could be modeled in terms of mixed integer programming (MIP).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.67-78
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• 2021

The number of domestic e-commerce transactions has been breaking its own record by an annual average growth rate of over 20% based on volume for the past 5 years. Due to the rapid increase in e-commerce market, retail companies that have difficulty meeting consumers in person are in fierce competition to take the lead in the last mile service, which is the only point of contact with customers. Especially in the delivery area, where competition is most intense, the role of the fulfillment center is very important for service differentiation. It must be capable of fast product preparation ordered by consumers in accordance with the delivery service level. This study focuses on the order picking system for rapid order processing in the fulfillment center as an alternative for companies to gain competitive advantage in the e-commerce market. A mixed integer programming model was developed and implemented to optimize the stock replenishment in order picking facilities. The effectiveness was scientifically and objectively verified by simulation using the actual operation process and data.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1722-1729
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• 2011

This paper presents an optimization model for revising train timetable based on an existing timetable to improve transfer time at each station. The transfer time consists of walking and waiting time. The model is formulated as a mixed integer programming. The objective function is to minimize the transfer time from one train to another train at each station. To reflect real situations, range of revising departure time is considered as major condition in the model. To validate the effectiveness of the model, rudimentary computational results are included, and the results are analyzed in terms of transfer time.