• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.105-114
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• 2022

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.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.51-57
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• 2019

A plant layout problem has a large impact on the overall construction cost of a plant. When determining a plant layout, various constraints associating with safety, environment, sufficient maintenance area, passages for workers, etc have to be considered together. In general plant layout problems, the main goal is to minimize the length of piping connecting equipments as satisfying various constraints. Since the process may suffer from the heat and friction loss, the piping length between equipments should be shorter. This problem can be represented by the mathematical formulation and the optimal solutions can be investigated by an optimization solver. General researches have overlooked many constraints such as maintenance spaces and safety distances between equipments. And, previous researches have tested benchmark processes. What the lack of general researches is that there is no realistic comparison. In this study, the plant layout of a real industrial C3MR (Propane precooling Mixed Refrigerant) process is studied. A MILP (Mixed Integer Linear Programming) including various constraints is developed. To avoid the violation of constraints, penalty functions are introduced. However, conventional optimization solvers handling the derivatives of an objective functions can not solve this problem due to the complexities of equations. Therefore, the PSO (Particle Swarm Optimization), which investigate an optimal solutions without differential equations, is selected to solve this problem. The results show that a proposed method contributes to saving the capital expenditures.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.185-193
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• 2009

This paper presents a methodology for photovoltaic (PV) system allocation in distribution systems using a discrete particle swarm optimization (DPSO). The PV allocation problem is in the category of mixed integer nonlinear programming and its formulation may include multi-valued dis-crete variables. Thus, the PSO requires a scheme to deal with multi-valued discrete variables. This paper introduces a novel multi-level quantization scheme using a sigmoid function for discrete particle swarm optimization. The technique is employed to a standard PSO architecture; the same velocity update equation as in continuous versions of PSO is used but the particle's positions are updated in an alternative manner. The set of multi-level quantization is defined as integer multiples of powers-of-two terms to efficiently approximate the sigmoid function in transforming a particle's position into discrete values. A comparison with a genetic algorithm (GA) is performed to verify the quality of the solutions obtained.

• Korean Chemical Engineering Research
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• v.52 no.4
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• pp.475-480
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• 2014

In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines for connecting equipment. However, what is the lacking of considerations in previous researches is to handle the multi floor processes considering the safety distances for domino impacts on a complex plant. The mathematical programming formulation can be transformed into MILP (Mixed Integer Linear Programming) problems as considering safety distances, maintenance spaces, and economic benefits for solving the multi-floor plant layout problem. The objective function of this problem is to minimize piping costs connecting facilities in the process. However, it is really hard to solve this problem due to complex unequality or equality constraints such as sufficient spaces for the maintenance and passages, meaning that there are many conditional statements in the objective function. Thus, it is impossible to solve this problem with conventional optimization solvers using the derivatives of objective function. In this study, the PSO (Particle Swarm Optimization) technique, which is one of the representative sampling approaches, is employed to find the optimal solution considering various constraints. The EO (Ethylene Oxide) plant is illustrated to verify the efficacy of the proposed method.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.106-111
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• 1992

In order to achieve stable and efficient use of energy at iron and steel works, a model for the prediction of supply and demand of electric power system is developed on the basis of the information on operation and particular patterns of electric power consumption. The optimal amount of electric power to be purchased and the optimal fuel allocation for the in-house electric power plants are also obtained by a mixed-integer linear programming(MILP) and a nonlinear programming (NLP) solutions, respectively. The validity and the effectiveness of the proposed model are investigated by several illustrative examples. The simulation results show the satisfactory energy saving by the optimal solution obtained through this research.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.11-18
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• 2006

The logical analysis of data(LAD) is a 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 novel optimization-based pattern generation methodology and propose two mathematical programming models, 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 ease patterns of high complexity that cannot be generated with the conventional approach.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.540-549
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• 2018

This paper proposes the method of active distribution network expansion planning considering distributed generation integration and distribution network reconfiguration. The distribution network reconfiguration is taken as the expansion planning alternative with zero investment cost of the branches. During the process of the reconfiguration in expansion planning, all the branches are taken as the alternative branches. The objective is to minimize the total costs of the distribution network in the planning period. The expansion alternatives such as active management, new lines, new substations, substation expansion and Distributed Generation (DG) installation are considered. Distribution network reconfiguration is a complex mixed-integer nonlinear programming problem, with integration of DGs and active managements, the active distribution network expansion planning considering distribution network reconfiguration becomes much more complex. This paper converts the dual-level expansion model to Second-Order Cone Programming (SOCP) model, which can be solved with commercial solver GUROBI. The proposed model and method are tested on the modified IEEE 33-bus system and Portugal 54-bus system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.48-55
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• 2010

Reliability has been considered as a one of the major design measures in various industrial and military systems. The main objective is to suggest a mathematical programming model and a hybrid parallel genetic algorithm(HPGA) for the problem that determines the optimal component reliability to maximize the system reliability under cost constraint in this study. Reliability optimization problem has been known as a NP-hard problem and normally formulated as a mixed binary integer programming model. Component structure, reliability, and cost were computed by using HPGA and compared with the results of existing meta-heuristic such as Ant Colony Optimization(ACO), Simulated Annealing(SA), Tabu Search(TS) and Reoptimization Procedure. The global optimal solutions of each problem are obtained by using CPLEX 11.1. The results of suggested algorithm give the same or better solutions than existing algorithms, because the suggested algorithm could paratactically evolved by operating several sub-populations and improving solution through swap and 2-opt processes.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.724-737
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• 2011

The goal of solving a contiguous land acquisition problem is to find an optimal cluster of land parcels so that one can move from an acquired parcel to another without leaving the cluster. In many urban and regional planning applications, criteria such as acquisition cost and compactness of acquired parcels are important. Recent research has demonstrated that spatial contiguity can be formulated in a mixed integer programming framework. Spatial compactness, however, is typically formulated in an approximate manner using parameters such as external border length or other shape indices of acquired land parcels. This paper first develops an alternative measure of spatial compactness utilizing the characteristics of the internal structure of a contiguous set of land parcels and then incorporates this new measure into a mixed integer program of contiguous land acquisition problems. A set of computational experiments are designed to demonstrate the use of our model in a land acquisition context.