Earticle

자율주행 기술은 기초적인 연구 단계를 넘어 상용화 초입 단계에 접어들었으며, 최근에는 자율주행 기술과 정보통신 기술 기반 지능형교통시스템을 접목한 모빌리티 서비스들이 활발 히 개발되고 있다. 본 연구는 모빌리티 기반 서비스 중 하나인 수요응답형 자율배송 서비스의 운영 효율성 향상을 위해 다중 모빌리티 전역경로를 생성하는 것으로, 지능형교통시스템을 통 해 수집한 교통소통정보와 서비스 사용자 수요를 고려하여 최단 시간 내에 자율배송을 완료할 수 있는 혼합 정수 최적화 기반 전역경로 생성 시스템이 개발되었다. 개발된 전역경로 생성 시스템은 교통소통정보 갱신 또는 서비스 사용자의 추가 수요 발생에 따라 전역경로를 갱신하 며 상암 자율주행 테스트베드의 교통소통정보를 활용하여 수요응답형 자율배송 서비스 운영 이 가능함이 확인되었다. 또한, 기존 유인 배송 서비스와 비교분석을 통해 운영 비용 절감과 물품 배송 및 공차 시간이 단축이 확인되었다.

Autonomous driving technologies have entered the initial stage of commercialization. Recently, mobility services that combine autonomous driving technologies and information and communication technologies based intelligent transportation systems are being actively developed. This study develops a global path planning system that considers traffic information and user demands to generate the shortest time paths for autonomous delivery services using Mixed Integer Programming. While providing the autonomous delivery services, the generated paths are updated recursively according to traffic information updates or additional demands. The developed global path planning system was verified by simulations with traffic information in the Sangam autonomous driving test-bed, and comparative analysis with existing manned delivery services shows that operating costs, product delivery time, and empty driving time were reduced.

본 연구는 폐기되는 자동차의 리버스물류 네트워크 구축을 위해 폐차수집센터(CC), 분해 및 해체센터(DC) 등의 최적위치와 경로를 결정하는데 활용할 수 있는 모델링을 제시하고자 한다. 폐기되는 자동차의 리싸이클링 네트워크를 구축하는데 따른 비용을 고려하여 리싸이클링 네트워크 구축을 위한 위치-경로문제에 대한 최적모델을 제안하였으며, 이러한 목적을 달성하기 위해 유전적 해법(algorithm)과 Tabu탐색해법을 결합한 혼합해법을 사용하였다. 제안된 모델은 청두의 X 기업에서 실행된 실제사례를 기반으로 수집센터, 분해 및 해체센터의 위치 결정뿐만 아니라 수집센터 혹은 분해 및 해체센터와 리싸리클링 사이트 간 비용 최소화 조건의 최적 경로설정을 제시하였다. 이러한 연구결과는 연구의 대상이 된 X기업에 적용하게 된다면 자동차의 라싸이클링 물류네트워크 구축을 위한 투자비용과 시간을 절감할 수 있을 뿐만 아니라 기업의 경쟁력을 제고효과도 있을 것이다. 아울러 다른 종류의 리버스 물류네트워크를 구축하는데 적용될 수 있을 것이다.

This paper presents modelling approach that could be used to establish one important part of end-of-life vehicles (ELVs) reverse logistics network by identifying optimum location for collection centers, dismantling centers and routing arrangement for vehicles. Considering the cost optimization in ELVs recycling logistics network, this paper suggests optimization model of ELVs recycling logistics network location-routing problem and use the algorithm which combine genetic algorithm and tabu search algorithm for solving the proposed model. The proposed model is validated by a real case performed in X company and determines the location of collection centers (CCs) and dismantling centers (DCs), as well as the route arrangement between CCs or DCs and recycling sites (RSs) in ELVs recycling logistics network of X company in Chengdu city of China. In the result, the investment cost and time in the X company could be diminished and the competitiveness of X company would be enhanced as well. Also, this proposed Model may be used to determines the location and routing of different kinds of facilities organized in a reverse recycling network.

This paper presents a multi objective approach to solve a Capacitated Vehicle Routing Problem ith Time Windows (CVRPTW). The proposed model was implemented and tested in a real life roblem of a distribution company “Just in Time Delivery S.A” in Portugal. In this paper we ave considered an objective function with two main goals: the first is to minimize the total number f vehicles used in the distribution of the commodities to the several clients and the second is to inimize the travelling time of the used vehicles. The proposed model has been solved numerically sing the GLPK software and the optimal solution is presented.

Renewable sources integration is gaining importance in electrical utilities all over the world. The liberization of power sector in competitive regime, the share of renewable energy sources is increasing and it is essential to carry out the impact of clean energy on the system performance In this paper, analysis has been carried out with the PV-based distribution generation in the power system network. A Mixed Integer Nonlinear Programming (MINLP) approach has been utilized for determining optimal location and number of distributed generators considering minimization of fuel cost of conventional and solar PV power. The pattern of nodal real and reactive power prices have been obtained with and without PV integration. The results are also obtained for, loss reduction, fuel cost saving and voltage profile. The impact of different load models as PQ load and Zip load model has been studied. The proposed MINLP based optimization approach has been applied for IEEE24 bus reliability test system.

In this paper, we deal with a Steiner Ring Star (SRS) problem arising from the design of survivable telecommunication networks. We develop two mixed integer programming formulations for the SRS problem by implementing Miller-Tucker-Zemlin (MTZ) and Sarin-Sherali-Bhootra (SSB) subtour elimination constraints, and then apply the reformulation-linearization technique (RLT) to enhance the lower bound obtained by the LP relaxation. By exploiting the ring-star structure of underlying network, we devise some valid inequalities that tighten the LP relaxation. Computational results demonstrate the effectiveness of the proposed solution procedure.

For about the past decade and a half research efforts into cognitive radio networks (CRNs) have increased dramatically. This is because CRN is recognized as a technology that has the potential to squeeze the most out of the existing spectrum and hence virtually increase the effective capacity of a wireless communication system. The resulting increased capacity is still a limited resource and its optimal allocation is a critical requirement in order to realize its full benefits. Allocating these additional resources to the secondary users (SUs) in a CRN is an extremely challenging task and integer programming based optimization tools have to be employed to achieve the goals which include, among several aspects, increasing SUs throughput without interfering with the activities of primary users. The theory of the optimization tools that can be used for resource allocations (RA) in CRN have been well established in the literature; convex programming is one of them, in fact the major one. However when it comes to application and implementation, it is noticed that the practical problems do not fit exactly into the format of well established tools and researchers have to apply approximations of different forms to assist in the process. In this survey paper, the optimization tools that have been applied to RA in CRNs are reviewed. In some instances the limitations of techniques used are pointed out and creative tools developed by researchers to solve the problems are identified. Some ideas of tools to be considered by researchers are suggested, and direction for future research in this area in order to improve on the existing tools are presented.

This paper presents scheduling models for dispatching vehicles to accomplish a sequence of container jobs at the container terminal, in which the starting times as well as the order of vehicles for carrying out these jobs need to be determined. To deal with this scheduling problem, three mixed 0-1 integer programming models, Model 1, Model 2 and Model 3 are provided. We present interesting techniques to reformulate the two mixed integer programming models, Model 1 and Model 2, as pure 0-1 integer programming problems with simple constraint sets and present a lower bound for the optimal value of Model 1. Model 3 is a complicated mixed integer programming model because it involves a set of non-smooth constraints, but it can be proved that its solutions may be obtained by the so-called greedy algorithm. We present numerical results showing that Model 3 is the best among these three models and the greedy algorithm is capable of solving large scale problems.

경영 분류 문제에 대한 많은 연구들은 여러가지 기법들간의 성과 비교에 대한 것이었지만, 각각의 연구들마다 가장 좋은 기법이 어떤 것인가에 대해서는 상이한 결론을 내고 있다. 다수의 분류 기법 중에서 가장 좋은 것을 사용하는 방법에 대한 대안으로,분류 기법을 통합하여 성과를 향상시키는 방법이 있다. 본 연구에서는 개별 분류 기법의 결과를 선형 결합하여 예측력을 높이는 방법을 제시하였다. 최 적 선형 결합 가중치를 계산하기 위해 혼합 정수 계 획 법을 사용하였다. 목적 함수로 사용한 오분류 비용의 최소화에서 오분류 비용은 부도 기업을 모형에서 정상으로 예측한 오류와 정상기업을 모형에서 부도 기업으로 예측한 오류의 합으로 정의하였다. 문제 풀이 과정을 단순화하기 위하여 본 논문에서는 절사점 (cutoff value)을 고정하였고, 경계 함수 (threshold function)를 배제하였다. 정수계획법의 계산을 위해 branch 8, bound 방법을 사용하였다. 선형 결합에 의한 모형의 예측력이 개별 기법에 의해 구축된 모형의 예측력을 상회하였고, 그 차이가 통계적으로도 유의하였다.

Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective in the classification problems. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques. This study proposes the linearly combining methodology of different classification techniques. The methodology is developed to find the optimal combining weight and compute the weighted-average of different techniques' outputs. The proposed methodology is represented as the form of mixed integer programming. The objective function of proposed combining methodology is to minimize total misclassification cost which is the weighted-sum of two types of misclassification. To simplify the problem solving process, cutoff value is fixed and threshold function is removed. The form of mixed integer programming is solved with the branch and bound methods. The result showed that proposed methodology classified more accurately than any of techniques individually did. It is confirmed that Proposed methodology Predicts significantly better than individual techniques and the other combining methods.

SoC내의 IP 개수 및 데이터 통신량이 증가함에 따라 온칩 크로스바 네트워크가 SoC의 중추 연결망으로서 널리 사용되어지고 있다. 온칩 크로스바 네트워크는 여러 개의 크로스바 스위치들과 이들간의 연결로 이루어지는데, 시스템의 복잡도가 증가함에 따라 IP들과 스위치들간의 연결 형태를 결정하는 것, 즉 토폴로지를 결정하는 것이 점점 복잡해지고 있다. 이 문제를 해결하기 위해 본 논문에서는 목표 시스템의 칩내 통신 특성을 고려하여 최적의 온칩 크로스바 네트워크의 토폴로지를 찾아주는 방법을 제안한다. 제안하는 토폴로지 합성 방법은 mixed integer linear programming(MIILP)를 이용하여 다른 휴리스틱 합성 방법과 달리 전역 최적점(global optimum)을 찾을 수 있는 장점이 있다. 또한, 기존에 제안 되었던 MILP를 이용한 토폴로지 합성 방법들이 토폴로지를 표현하는데 IP 노드들과 스위치들 간의 인접 행렬들을 이용했던 것과 달리, 본 논문에서는 IP들 간통신을 표현하는 엣지들을 기본으로 하는 새로운 표현 방식을 제안한다. 실험 결과 본 논문에서 제안하는 새로운 MILP 표현 방식을 이용할 경우 기존 MILP 표현 방식을 이용했을 때보다 4개의 예제들에 대해 합성 속도가 평균 77.1 배 향상되었다.

As the number of IPs and the communication volume among them have constantly increased, on-chip crossbar network is now the most widely-used on-chip communication backbone of contemporary SoCs. The on-chip crossbar network consists of multiple crossbars and the connections among the IPs and the crossbars. As the complexity of SoCs increases, it has also become more and more complex to determine the topology of the crossbar network. To tackle this problem, this paper proposes an on-chip crossbar network topology method for application-specific systems. The proposed method uses mixed integer linear programming to solve the topology synthesis problem, thus the global optimality is guaranteed. Unlike the previous MILP-based methods which represent the topology with adjacency matrixes of IPs and crossbar switches, the proposed method uses the communication edges among IPs as the basic element of the representation. The experimental results show that the proposed MILP formulation outperforms the previous one by improving the synthesis speed by 77.1 times on average, for 4 realistic benchmarks.

This paper considers a profit-based unit commitment problem with fuel consumption constraint and maintenance cost, which is one of the key decision problems in electricity industry. The nature of non-linearity inherent in the constraints and objective functions makes the problem intractable which have led many researches to focus on Lagrangian based heuristics. To solve the problem more effectively, we propose mixed integer programming based solution algorithm linearizing the complex non-linear constraints and objectives functions. The computational experiments using the real-world operation data taken from a domestic electricity power generator show that the proposed algorithm solves the given problem effectively.

본 연구에서는 부유식 해상풍력발전의 운영 및 유지보수에 필요한 체계적인 정비계획 수립을 위해 최적화기법을 활용한 수리 모형을 제안하고자 한다. 주간 단위로 선박과 기술자를 운용하는 계획정비와 고장정비 작업의 배정에 혼합정수계획법(Mixed Integer Linear Programming, MILP)을 도입하였다. 본 연구의 최적화 모델을 활용한 사례연구에서는 선박과 기술자의 투입 규모가 유지정비 비용에 미치는 영향을 확인하였으며 1년간 정비계획 수립에서 더 나아가 정비작업별 상세 스케줄링까지 연계되는 단계적 최적화 방법론을 함께 제시하였다. 세부적으로는 기상 데이터와 정비 데이터를 활용한 발전량 손실을 비가동 비용으로 반영하여 정비 우선순위를 선정하였으며, 이를 통해 국내 실정에 맞는 해상풍력단지의 유지보수 전략을 제시할 수 있을 것으로 기대한다.

In this paper, we propose operations and maintenance (O&M) planning approach for floating offshore wind farm using the mathematical optimization. To be specific, we present a MILP (Mixed Integer Linear Programming that suggests the composition of vessels, technicians, and maintenance works on a weekly basis. We reflect accessibility to wind turbines based on weather data and loss of power generation using the Jensen wake model to identify downtime cost that vary from time to time. This paper also includes a description of two-stage approach for maintenance planning & detailed scheduling and numeric analysis of the number of vessels and technicians on the O&M cost. Finally, the MILP model could be utilized in order to establish the suitable and effective maintenance planning reflecting domestic situation.

본 논문은 혼합정수계획법(MIP)을 통하여 혼류생산을 실시하는 다품종 제조 시스템을 위한 생산계획수립 방안에 대하여 연구하였다. 특히 자동차 부품 제조 기업의 다양한 제조 품목에 대한 수요 변화에 따라 Rolling Horizon 방식으로 생산계획을 수립하도록 함으로써, 수요 변화에 능동적으로 대응하면서도 한정된 기업의 생산 능력을 최대한 활용하고 생산 비용을 최소화하는 최적 생산계획을 수립하여 실제로 기업에서 활용할 수 있도록 하는 사례를 연구하였다.

In this paper, we discuss the way to establish a production plan using MIP(mixed integer programming) for multiple product manufacturing with considering high cost of set-up in tandem production line. Especially, this study examine the case of developing optimized production plan that cope actively with changes of demand and make full use of firm's limited production capability as well as minimizing production cost. We suggest a efficient method to implement MIP solution using rolling horizon concept to deal with the environment of rapid demand changes in the auto-parts industry.

Increasing peak load is one of the major concerns about operation of urban railroad systems. Since ESSs (Energy Storage Systems) have a great potential for shaving the peak load, there has been a growing interest in the use of ESS for peak load reduction. Also, ESS can be optimally scheduled to minimize the electricity purchasing cost under a given ToU (Time-of-Use) tariff by taking advantage of electricity price difference between peak and off-peak time. This paper presents a Mixed Integer Programming (MIP)-based ESS scheduling method to minimize the electricity purchasing cost under a ToU tariff for an urban railroad system.

This paper presents a short-term scheduling algorithm for the operation of steelmaking processes. The scope of the problem covers refining of the hot iron transferred form a blast furnace, ladle treatment, continuous casting, hot-rolling, and coiling for the final products that should satisfy the given demand. The processing time at each unit depends on how much the batch amount is treated, and te dedicated intermediate storage with finite capacity between the units is considered. Resource constraints and initial amount of each state are incorporated into the presented scheduling model for the algorithm of on-line scheduling. We propose amixed integer linear programming (MILP) model with two objectives for the scheduling. The first is to maximize the total profit while atisfying the due date constraint for each product. And the second is to minimize the total processing time, makespan, while satisfying the demand for each product. Especially, we observe the effect of penalizing the intermediate storage and the inventory level of the final product on the scheduling results.

The topic of this study is the field of humanitarian logistics for disaster response. Many existing studies have revealed that compliance with the golden time in response to a disaster determines the success or failure of relief activities, and logistics costs account for 80% of the disaster response cost. Besides, the agility, responsiveness, and effectiveness of the humanitarian logistics system are emphasized in consideration of the disaster situation's characteristics, such as the urgency of life-saving and rapid environmental changes. In other words, they emphasize the importance of logistics activities in disaster response, which includes the effective and efficient distribution of relief supplies. This study proposes a mathematical model for establishing a transport plan to distribute relief supplies in a disaster situation. To determine vehicles' route and the amount of relief for cities suffering a disaster, it mainly considers the urgency, effectiveness (restoration rate), and uncertainty in the logistics system. The model is initially developed as a mixed-integer nonlinear programming (MINLP) model containing some nonlinear functions and transform into a Mixed-integer linear programming (MILP) model using a logarithmic transformation and piecewise linear approximation method. Furthermore, a minimax problem is suggested to search for breakpoints and slopes to define a piecewise linear function that minimizes the linear approximation error. A numerical experiment is performed to verify the MILP model, and linear approximation error is also analyzed in the experiment.

ELECTRE IS requires the decision maker (DM) to specify several parameters such as weights, pseudo-criteria thresholds and the concordance level. Among these parameters, the concordance level has a significant effect on the outranking relation. And the number of alternatives selected may be sensitive to the value of these parameters. Therefore the DM may have to perform many iterations to obtain the desired number of alternatives in the kernel set. In this study, we developed a mixed-integer programming (MIP) model to elicit the concordance level and thereby to choose the desired number of alternatives in the kernel set. The MIP model can be applied in the interactive process so that the pseudo-criteria thresholds are adjusted according to the results of MIP model. Using the MIP model in the interactive process, we can reduce the number of iterations needed to perform ELECTRE IS.

본 연구에서는 조수조건이 고려된 벌크항만의 하역 최적화를 위한 정수계획모형을 제안한다. 특히, 본 연구에서는 실제 벌크항만의 운영 환경과 조건들을 반영하여 체선료(Demurrage Cost)와 조출료(Dispatch Money)를 모두 고려한 하역비용 최소화를 목적함수로 설정하고, 벌크항만의 서비스 수준을 결정하는 최소재고 제약조건 또한 고려한다. 일반적으로 비선형 함수 형태로 표현되는 체선료 계산식을 선형화(Linearize)하여 스케줄 해상도를 향상하고, 조수조건을 고려한 선석할당 문제의 경우 전통 자원할당 모형에서 필수적인 Big-M 제약식이 대체 가능함을 확인한다. 실험결과를 통해 기존 모형 대비 계산복잡도와 전역최적성이 크게 향상 가능함을 검증한다.

We examine berth allocation problems in tidal bulk ports with an objective of minimizing the demurrage and dispatch associated berthing cost. In the proposed optimization model inventory (or stock) level constraints are considered so as to satisfy the service level requirements in bulk terminals. It is shown that the mathematical programming formulation of this research provides improved schedule resolution and solution accuracy. We also show that the conventional big-M method of standard resource allocation models can be exempted in tidal bulk ports, and thus the computational efficiency can be significantly improved.

In this study, a Multiple Objective Mixed Integer Programming (MOMIP) Model is developed for sewer rehabilitation planning by considering cost, inflow/infiltration. A sewer rehabilitation planning model is required to decide the economic life of the sewer by considering trade-off between cost and inflow/infiltration. And it is required to find the optimal rehabilitation timing, according to the cost effectiveness of each sewer rehabilitation within the budget. To develop such a model, a multiple objective mixed integer programming model is formulated based on network flow optimization. The network is composed of state nodes and arcs. The state nodes represent the remaining life and the arcs represent the change of the state. The model consider multiple objectives which are cost minimization and minimization of inflow/infiltration. Using the multiple objective optimization, the trade-off between the cost and inflow/infiltration is presented to the planner so that a proper sewer rehabilitation plan can be selected.

The purpose of this paper is to develope a large-scale mixed integer program MIPBB. In this paper, the various issues such as branching strategies, searching and bounding strategies, storing basis information, handling numerical instability, that are important for developing a large-scale mixed integer programming program, are considered. And the experimental results of MIPBB are presented and compared to those of GLPK.