• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.481-486
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• 2023

The purpose of this study is to optimize parameters of a contact model to obtain similar ground contact force of human walking. Dynamic walking simulation considering ground contact is performed to determine load specifications when developing walking assist robots. Large contact forces that are not observed in actual experimental data occur during the simulation at the initial contact (e.g., heel contact). The large contact force generates unrealistic large joint torques. A lower exoskeleton robot with no ankles is developed with the Matlab simscape and the nonlinear hyper volumetric contact model is applied. Parameters of the nonlinear hyper volumetric model were optimized using actual walking contact force data. As a result of optimization, it was possible to obtain a contact force pattern similar to actual walking by removing the large contact force generated during initial contact.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.7
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• pp.542-549
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of given design variables and chance them to get a bettor design. Even though commercial simulation codes are used, the computational time and cost remains non-trivial. Therefore, malty researchers have used a mesa model made by sampling data through simulation. In this paper, four mesa-models for each index group such as ride comfort, derailment Quotient, unloading radio and stability index, are constructed by use of neural network. After these meta models are constructed, multi-objective optimization are achieved by using the differential evolution. This paper shows that the optimization of design variables using the neural network model is very efficient to solve the complex optimization Problem.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.282-289
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• 2007

While demands for express couriers service are rapidly increasing due to recent progress of electronic commerce, express courier service companies are struggling to take a larger market share through ongoing improvement in their service processes. Cut-off time is the time limit that all orders delivered before the limit are guaranteed for the delivery within the very next day. Extending cut-off time for express service centers can provide the express company with increase of total sales, but it may also cause increasing the possibility not to satisfy customer needs due to work delay in the consolidation terminal. We develop a design model for express courier service network based on a recursive optimization/simulation procedure. With the optimization model, we seek key design parameters such as the cut-off time for express service centers and the capacity of the consolidation terminal maximizing total sales profit while satisfying the desired level of performances. With the simulation model, we consider the dynamic nature of the network and obtain relationships between the design parameters and the performance measures with the multiple linear regression. The validity of the model is examined with an example.

• The Journal of Asian Finance, Economics and Business
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• v.2 no.2
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• pp.13-18
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• 2015

In this paper the DRC model is presented for solving multi objective problem. The proposed model is a combination of data envelopment analysis, Cuckoo algorithm and the response surface method. Due to reasons like costs, time and irreversible damages, it is not possible to analyze each and every one of the proposed models in practice, so the simulation is used. Since the number of experiments for simulation process is high then the optimization has gone to practice and directs the simulation process. The response surface method is used as one of the approaches of simulation optimization. Furthermore, data envelopment analysis is used to consider several response surfaces as efficiency response surface. Then this efficiency response surface is solved by Cuckoo algorithms. The main advantage of DRC model is to make one efficiency response surface function instate of multi surface function for every output and also using the advantages of Cuckoo algorithms. In order to demonstrate the effectiveness of the proposed approach, the branches of Refah bank in Mashhad is analyzed and the results are presented.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.6
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• pp.461-468
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• 2002

Computer simulation is essential to design the suspension elements of railway vehicle. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such as railway vehicle dynamic, the computational time can become overwhelming. Therefore, many researchers have used a surrogate model that has a regression model performed on a data sampling of the simulation. In general, metamodels(surrogate model) take the form y($\chi$)＝f($\chi$)＋$\varepsilon$, where y($\chi$) is the true output, f($\chi$) is the metamodel output, and is the error. In this paper, a second order polynomial equation is used as the RSM(response surface model) for high speed train that have twenty-nine design variables and forty-six responses. After the RSM is constructed, multi-objective optimal solutions are achieved by using a nonlinear programming method called VMM(variable matric method) This paper shows that the RSM is a very efficient model to solve the complex optimization problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.184-184
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• 2017

A nation-wide water-energy-food (WEF) nexus simulation model has been developed by the authors and successfully applied to South Korea to predict the sustainability of those three resources in the next 30 years. The model was also capable of simulating future scenarios of resources allocation based on priority rules aiming to maximize resources sustainability. However, the process was still relying on several assumptions and trial-and-error approach, which sometimes resulted in non-optimal solutions of resources allocation. In this study, an optimization module was introduced to enhance the model in generating optimal resources management rules. The objective of the optimization was to maximize the reliability index of resources by determining the resources' allocation and/or priority rules for each demand type that accordingly reflect the resources management policies. Implementation of the optimization module would result in balanced allocation and management of limited resources and assist the stakeholders in deciding resources' management plans, either by fulfilling the domestic production or by global trading.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Management Science and Financial Engineering
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• v.13 no.1
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• IE interfaces
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• v.21 no.4
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• pp.444-455
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• 2008

In the present manufacturing environment, the appropriate decision making strategy has a significance and it should count on the fast-changing demand of customers. This research derives the optimal levels of the decision variables affecting the inventory related performance in multi-stage supply chain by using simulation and genetic algorithm. Simulation model helps analyze the customer service level of the supply chain computationally and the genetic algorithm searches the optimal solutions by interaction with the simulation model. Our experiments show that the integration approach of the genetic algorithm with a simulation model is effective in finding the solutions that achieve predefined target service levels.