• 대한교통학회지
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• 제18권1호
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• pp.87-97
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• 2000

Signal optimization model is divided bandwidth-maximizing model and delay-minimizing model. Bandwidth-maximizing model express model formulation as MILP(Mixed Integer Linear Programming) and delay-minimizing model like TRANSYT-7F use "hill climbing" a1gorithm to optimize signal times. This study Proposed optimization model using genetic algorithm one of evolution algorithm breaking from existing optimization model This Proposed model were tested by several scenarios and evaluated through NETSIM with TRANSYT-7F\`s outputs. The result showed capability that can obtain superior solution.

• 한국산업정보학회논문지
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• 제15권5호
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• pp.187-196
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• 2010

네트워크 최적화 문제는 의사결정 문제 중에서 노드와 아크로 표현되는 수 많은 문제를 포함하고 있어서, 그 응용범위가 매우 다양할 뿐만 아니라 매우 실질적인 문제를 해결하는 좋은 방법론이다. 직접적으로 관련이 없는 많은 최적화 문제들도 네트워크로 적절히 표현할 수 있는 경우가 많으며 이를 통해 보다 섬도 있는 문제에 대한 이해와 해의 도출이 가능하게 된다. 이처럼 광범위한 응용분야를 가지는 네트워크 최적화 문제는 경영과학 및 산업공학에서 기본이 중요 학문이며, 이를 체계적으로 이해하고 실제 문제를 해결하려면 최적화 이론, 계산이론, 프로그래밍 등의 종합적인 지식을 필요로 한다. 본 연구에서는 네트워크 최적화 문제를 실질적으로 해결하는 필요한 지식 전달에 중점을 두고, 선형계획법 및 정수계획법을 위한 소프트웨어인 LPSolve를 소개하고 이 LPSolve와 엑셀을 연동하는 방법을 알아본다. 또한 네트워크 자체를 엑셀에서 그리는 방법을 알아보고, 이를 통해 네트워크 최적화 문제를 보다 실질적인 다룰 수 있도록 한다.

• 한국안전학회지
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• 제35권1호
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• pp.25-33
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• 2020

In most plant layout optimization researches, MILP(Mixed Integer Linear Programming) problems, in which the objective function includes the costs of pipelines connecting process equipment and cost associated with safety issues, have been employed. Based on these MILP problems, various optimization solvers have been applied to investigate the optimal solutions. To consider safety issues on the objective function of MILP problems together, the accurate information about the impact and the frequency of potential accidents in a plant should be required to evaluate the safety issues. However, it is really impossible to obtain accurate information about potential accidents and this limitation may reduce the reliability of a plant layout problem. Moreover, in real industries such as plant engineering companies, the plant layout is previously fixed and the considerations of various safety instruments and systems have been performed to guarantee the plant safety. To reflect these situations, the two step optimization problems have been designed in this study. The first MILP model aims to minimize the costs of pipelines and the land size as complying sufficient spaces for the maintenance and safety. After the plant layout is determined by the first MILP model, the optimal locations of blast walls have been investigated to maximize the mitigation impacts of blast walls. The particle swarm optimization technique, which is one of the representative sampling approaches, is employed throughout the consideration of the characteristics of MILP models in this study. The ethylene oxide plant is tested to verify the efficacy of the proposed model.

• Korean Chemical Engineering Research
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• 제50권3호
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• pp.479-491
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• 2012

본 연구에서는 공급자와 소비자, 열 저장시설과 연계네트워크로 구성된 통합 지역난방시스템의 경제성을 평가하고 최적 운전조건을 규명하였다. 최적화에 있어서는 혼합 정수선형 계획법이 이용되었으며 1주일 동안의 열 요구량을 만족함과 동시에 통합 지역난방 시스템의 운전제한 조건에 따른 전체 운영비용을 목적함수로 하였다. 지역난방 네트워크 연결망을 열 병합 발전이 포함되지 않은 구역과 이를 포함하는 구역으로 나누어 최적화를 진행함으로써 열 병합 발전에 의한 비용절감 효과를 확인할 수 있었다. 아울러 계통한계가격 예측모델에 의해 예측된 계통한계가격과 실제 계통한계가격을 각각 적용하여 최적화를 진행하고 그 결과를 비교 분석하였다. 수치모사 결과 개발된 최적화 운영시스템의 도입에 의해 통합 지역난방시스템의 에너지 효율성이 증가함을 확인할 수 있었다.

• Industrial Engineering and Management Systems
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• 제7권2호
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• pp.113-125
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• 2008

This paper presents a model for designing cellular manufacturing systems (CMS) by integrating system cost, machine reliability, and preventive maintenance (PM) planning. In a CMS, a part is processed using alternative process routes, each consisting of a sequence of visits to machines. Thus, a level of 'system reliability' is associated with the machines along the process route assigned to a part type. Assuming machine reliabilities to follow the Weibull distribution, the model assigns the machines to cells, and selects, for each part type, a process route which maximizes the overall system reliability and minimizes the total costs of manufacturing operations, machine underutilization, and inter-cell material handling. The model also incorporates a reliability based PM plan and an algorithm to implement the plan. The algorithm determines effective PM intervals for the CMS machines based on a group maintenance policy and thus minimizes the maintenance costs subject to acceptable machine reliability thresholds. The model is a large mixed integer linear program, and is solved using LINGO. The results point out that integrating PM in the CMS design improves the overall system reliability markedly, and reduces the total costs significantly.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.1044-1051
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• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.170-174
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• 2003

In this research, we consider an integrated manufacturing/distribution planning problem in supply chain (SC) which has non-integer time lags. We focus on a capacitated manufacturing planning and capacity allocation problem for the system. We develop a mixed binary integer linear programming (MBLP) model and propose an efficient heuristic procedure using an adaptive genetic algorithm, which is composed of a regeneration procedure for evaluating infeasible chromosomes and the reduced costs from the LP-relaxation of the original model. The proposed an adaptive genetic algorithm was tested in terms of the solution accuracy and algorithm speed during numerical experiments. We found that our algorithm can generate the optimal solution within a reasonable computational time.

• 대한산업공학회지
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• 제34권4호
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• pp.481-488
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• 2008

This paper deals with a scheduling problem for two-machine flow shop, in which the preceding machine is a batch processing machine that can process a number of jobs simultaneously. To minimize makespan of the system, we present a mixed integer linear programming formulation for the problem, and using this formulation, it is shown that an optimal solution for small problem can be obtained by a commercial optimization software. However, since the problem is NP-hard and the size of a real problem is very large, we propose a number of heuristic algorithms including genetic algorithm to solve practical big-sized problems in a reasonable computational time. To verify performances of the algorithms, we compare them with lower bound for the problem. From the results of these computational experiments, some of the heuristic algorithms show very good performances for the problem.

• 대한산업공학회지
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• 제35권1호
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• pp.1-14
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• 2009

The efficiency of transportation requests fulfillment can be increased through extending the problem of vehicle routing and scheduling by the possibility of subcontracting a part of the requests to external carriers. This problem extension transforms the usual vehicle routing and scheduling problems to the more general integrated operational transportation problems. In this contribution, we analyze the motivation, the chances, the realization, and the challenges of the integrated operational planning and report on experiments for extending the plain Vehicle Routing Problem to a corresponding problem combining vehicle routing and request forwarding by means of different sub-contraction types. The extended problem is formalized as a mixed integer linear programming model and solved by a commercial mathematical programming solver. The computational results show tremendous costs savings even for small problem instances by allowing subcontracting. Additionally, the performed experiments for the operational transportation planning are used for an analysis of the decision on the optimal fleet size for own vehicles and regularly hired vehicles.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.409-413
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• 2005

The logical analysis of data(LAD) is an effective Boolean-logic based data mining tool. A critical step in analyzing data by LAD is the pattern generation stage where useful knowledge and hidden structural information in data is discovered in the form of patterns. A conventional method for pattern generation in LAD is based on term enumeration that renders the generation of higher degree patterns practically impossible. In this paper, we present a new optimization-based pattern generation methodology and propose two mathematical programming medels, a mixed 0-1 integer and linear programming(MILP) formulation and a well-studied set covering problem(SCP) formulation for the generation of optimal and heuristic patterns, respectively. With benchmark datasets, we demonstrate the effectiveness of our models by automatically generating with much ease patterns of high complexity that cannot be generated with the conventional approach.