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

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

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.21-29
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• 2015

The set partitioning problem is a well-known NP-hard combinatorial optimization problem, and it is formulated as an integer programming model. This paper proposes an Integer Programming-based Local Search for solving the set partitioning problem. The key point is to solve the set partitioning problem as the set covering problem. First, an initial solution is generated by a simple heuristic for the set covering problem, and then the solution is set as the current solution. Next, the following process is repeated. The original set covering problem is reduced based on the current solution, and the reduced problem is solved by Integer Programming which includes a specific element in the objective function to derive the solution for the set partitioning problem. Experimental results on a set of OR-Library instances show that the proposed algorithm outperforms pure integer programming as well as the existing heuristic algorithms both in solution quality and time.

• Journal of Korean Society for Quality Management
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• v.38 no.3
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• pp.333-339
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• 2010

It is difficult to determine an optimal inventory level of aircraft engine and modules to achieve the target operational availability since F100-PW-200 & 229 engines of the F-16 & KF-16 aircraft are consisted of 5 modules with different failure rates and costs. This study presents a decision model, combining an integer programming problem and a regression metamodel. Data for the metamodel was attained from results of a simulation model, that represents operational and repair process of F-16 and KF-16. The objective function of an integer programming problem is maximizing the operational availability, representing pessimistic circumstances. Finally, an integer programming problem with a metamodel can make an optimal decision of the inventory level.

• Proceedings of the KSR Conference
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• 2002.10a
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• pp.349-356
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• 2002

This paper describes a mathematical programming model for constructing the rolling stock assignment plan. This plan is a schedule which assigns daily routings, maintenance activities, and other tasks to trainsets. A generalized set partitioning model which is a kind of integer programming model is suggested as a model for constructing the assignment plan. And a column generation method are suggested as a solution method.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.141-153
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• 2017

A binary integer programming model is proposed for a complex timetabling problem in a university faculty which conducts various degree programs. The decision variables are defined with fewer dimensions to economize the model size of large scale problems and to improve modeling efficiency. Binary matrices are used to incorporate the relationships between the courses and students, and the courses and teachers. The model includes generally applicable constraints such as completeness, uniqueness, and consecutiveness; and case specific constraints. The model was coded and solved using Open Solver which is an open-source optimizer available as an Excel add-in. The results indicate that complicated timetabling problems with large numbers of courses and student groups can be formulated more efficiently with fewer numbers of variables and constraints using the proposed modeling framework. The model could effectively generate timetables with a significantly lower number of work hours per week compared to currently used timetables. The model results indicate that the particular timetabling problem is bounded by the student overlaps, and both human and physical resource constraints are insignificant.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.7-13
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• 2004

In this paper, machine cell formation problem is discussed. To reflect precisely actual manufacturing situations such as routing sequences, production quantities, and machining (or operation) characteristics, a new network presentation (or the problem is proposed. It is formulated as a simple 0-1 quadratic programming model with linear constraints. Then, the model is converted into a 0-1 integer programming model using a variable transformation technique. Lastly, some computational results are presented.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.660-667
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• 2003

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.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.109-119
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• 1999

The average shadow price is a substitute for the traditional marginal shadow price. It can serve as a standard for decision making problems about the economic resources where the marginal analysis gives no useful information. This paper treats the average shadow price in pure integer programming and shows some stability properties of it. This implies that the values of the average shadow prices once computed are reliable within some extent of the data perturbations of the integer programming model.

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