• Journal of Korea Water Resources Association
• /
• v.32 no.5
• /
• pp.525-535
• /
• 1999

An optimal unit commitment model for efficient management of water and energy resources in a basin using combined mixed integer programming is developed. The combined mixed integer programming model is able to solve the inconsistency problem that may occur from mixed integer programming models. The technique which enables the use of conditional constraints and either-or constraints in the linear programming is also suggested. As a result of applying the combined mixed integer programming model to Lower Colorado River Basin in United States. the basin efficiency is decreased by 1.53% from the results of the mixed integer programming, while it is increased by 0.67% from the results of the historical operation. It is found that the decreased allowable error between power supplies and demands in the combined mixed integer programming causes the decreased basin efficiency.

• Management Science and Financial Engineering
• /
• v.14 no.2
• /
• pp.25-44
• /
• 2008

This paper concentrates on reduction of a Quadratic Fractional Integer Programming Problem (QFIP) to a 0-1 Mixed Linear Programming Problem (0-1 MLP). The solution technique is based on converting the integer variables to binary variables and then the resulting Quadratic Fractional 0-1 Programming Problem is linearized to a 0-1 Mixed Linear Programming problem. It is illustrated with the help of a numerical example and is solved using the LINDO software.

• Journal of applied mathematics & informatics
• /
• v.17 no.1_2_3
• /
• pp.145-170
• /
• 2005

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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.69.2-69
• /
• 2002

Process measurements contain random and gross errors and the size estimation of gross errors is required for production accounting. Mixed integer programming technique had been applied to identify and estimate the gross errors simultaneously. However, the compensate model based on mixed integer programming used all measured variables or spanning tree as gross error candidates. This makes gross error estimation problem combinatorial or computationally expensive. Mixed integer programming with test statistics is proposed for computationally inexpensive gross error identification /estimation. The gross error candidates are identified by measurement test and the set of gross error candidates are...

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.1
• /
• pp.13-27
• /
• 2014

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.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.102-106
• /
• 2004

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.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.1-8
• /
• 2002

A Branch-and-Cut algorithm is a branch-and-bound algorithm in which rutting planes are generated throughout the branch-and-bound tree. It is now one of the most widespread and successful methods for solving mixed integer programming problems. In this paper we presents efficient implementation techniques of branch-and-cut algorithm for miked integer programming problems.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.166-174
• /
• 2016

In this paper, we developed a mixed-integer linear programming model for transportation planning of athletes in sports events. The transportation planning of athletes involves finding the lowest-cost and fastest-time plan for distributing athletes from multiple accommodation to stadium. The decision variables associate with the number of driving events, and the total transportation cost is the objective function that needs to be minimized. The proposed method uses mixed integer linear programming to solve transportation problem, thus the global optimality is guaranteed. In order to verify the effectiveness of proposed method, we performed simulation and built the sports event management service platform (SEMSP) for transportation planning.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.1
• /
• pp.166-173
• /
• 2013

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.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.4
• /
• pp.717-725
• /
• 2011

In the power system with an electric storage system that can increase utilization rate of the source of such new renewable energy, this paper introduces the approach on the daily unit commitment scheduling that determines simultaneously optimum operational condition and output of thermal generators and electric storage device. The unit commitment is one of the most important issues in economic operation and security of short-term operational plan of the power system. It is to determine on/off status of generator to minimize operational cost during the given period. The committed generator should satisfy various operational limitation such as estimated demand by system, spinning reserve condition within minimum operational cost. In order to determine on/off or charge/discharge/idle condition and output level of units and electric storage system, the MILP(Mixed Integer Linear Programming) is suggested. The proposed approach is the mixed method between LP(Linear Programming) and IP(integer programming) which seeks the value of real number and integer that maximize or minimize function objective within given condition. The daily unit commitment problem with the electric storage system is applied to MILP algorithm through linearization and formulation process. The proposed approach is applied to the test system.