• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.367-380
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• 2019

The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.780-785
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• 2005

This paper aims to consider an overall benefit-duration optimization (OBDO) problem for the sake of maximizing owner's economic benefits, whilst considering influences of schedule compression incurred opportunity income on the profitability of a large-scale construction project. Unlike previous schedule optimization models and techniques that have focused on project duration or cost minimization, with greater weight on contractors' interests, OBDO facilitates owner's economic benefits through overall benefit-duration optimization. In this paper, the objective function of OBDO model is formulated. An example is illustrated to prove the feasibility and practicability of the overall benefit-duration optimization problem. The significance of employing OBDO model and future research work are also described.

• Coupled systems mechanics
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• v.13 no.1
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• pp.1-19
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• 2024

The effectiveness of coupling with a bolted rim is assessed in this research using a newly designed optimization algorithm. The current study, which is provided here, evaluates 10 contemporary metaheuristic approaches for enhancing the coupling with bolted rim design problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The contribution of the paper isto optimize the coupling with bolted rim problem by comparing these 10 algorithms and to find which algorithm gives the best optimized result. These algorithm's performance is evaluated statistically and subjectively.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1550-1556
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• 1997

In this paper, we apply the evolutionary algorithm to the combinatorial optimization problem. Evolutionary algorithm useful for the optimization of the large space problem. This paper propose a method for the reuse of wastes of light water in atomic reactor system. These wastes contain several reusable elements, and they should be carefully selected and blended to satisfy requirements as an input material to the heavy water atomic reactor system. This problem belongs to an NP-hard like the 0/1 knapsack problem. Two evolutionary strategies are used as approximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method which perform the feasible test and solution evaluation by using the vectored knowledge in problem domain. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.3 s.234
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• pp.395-402
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• 2005

Recently Multidisciplinary Design Optimization Based on Independent Subspaces (MDOIS), an MDO (multidisciplinary design optimization) algorithm, has been proposed. In this research, an MDO problem is defined for design of a belt integrated seat considering crashworthiness, and MDOIS is applied to solve the problem. The crash model consists of an airbag, a belt integrated seat (BIS), an energy absorbing steering system, and a safety belt. It is found that the current design problem has two disciplines - structural nonlin- ear analysis and occupant analysis. The interdisciplinary relationship between the disciplines is identified and is addressed in the system analysis step in MDOIS. Interdisciplinary variables are belt load and stiffness of the seat, which are determined in system analysis step. The belt load is passed to the structural analysis subspace and stiffness of the seat back frame to the occupant analysis subspace. Determined design vari- ables in each subspace are passed to the system analysis step. In this way, the design process iterates until the convergence criterion is satisfied. As a result of the design, the weight of the BIS and Head Injury Crite- rion (HIC) of an occupant are reduced with specified constraints satisfied at the same time. Since the system analysis cannot be formulated in an explicit form in the current example, an optimization problem is formu - lated to solve the system analysis. The results from MDOIS are discussed.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.223-233
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• 2008

In this paper we give sufficient conditions for a point to be a solution of an optimization problem in complex space.

• Journal of Distribution Science
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• v.14 no.2
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• pp.41-45
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• 2016

Purpose - Finding an optimal path is an essential component for the design and operation of smart transportation or logistics network. Many applications in navigation system assume that travel time of each link is fixed and same. However, in practice, the travel time of each link changes over time. In this paper, we introduce a new transportation problem to find a latest departing time and delivery path between the two nodes, while not violating the appointed time at the destination node. Research design, data, and methodology - To solve the problem, we suggest a mathematical model based on network optimization theory and a backward search method to find an optimal solution. Results - First, we introduce a dynamic transportation problem which is different with traditional shortest path or minimum cost path. Second, we propose an algorithm solution based on backward search to solve the problem in a large-sized network. Conclusions - We proposed a new transportation problem which is different with traditional shortest path or minimum cost path. We analyzed the problem under the conditions that travel time is changing, and proposed an algorithm to solve them. Extending our models for visiting two or more destinations is one of the further research topics.

• Journal of Korean Society for Quality Management
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• v.18 no.2
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• pp.43-53
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• 1990

Mechanical components and structures are a major part of complex systems and the conseguences of their failure can be extremely costly. The ultimate goal of design engineers is to optimize these mechanical and structural design from the point of view of cost, reliability, weight, volume, maintainability and safety. An essential requirement of design optimization is to develop mathematical models for reliability at design stage. This paper is to minimize the cost of resources subject to the constraint that the reliability of the system must meet a specified level. The lagrange multiplier method is used to optimize the lognormal stress-lognormal strength problem. This optimization problem can be reduced to a search problem in one variable. A numerical example is presented to illustrate the optimization problem.

• Journal of the Korean Society of Marine Environment & Safety
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• v.6 no.1
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• pp.23-33
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• 2000

This paper is concerned with finding the global optimal solutions for the redundancy optimization problems in series-parallel systems related with system safety. This study transforms the difficult problem, which is classified as a nonlinear integer problem, into a 0/1 IP(Integer Programming) by using binary integer variables. And the global optimal solution to this problem can be easily obtained by applying GAMS (General Algebraic Modeling System) to the transformed 0/1 IP. From computational results, we notice that GA(Genetic Algorithm) to this problem, which is, to our knowledge, known as a best algorithm, is poor in many cases.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.