• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.63-69
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• 1999

The research trend of the simulation optimization has been focused on exploring continuous decision variables. Yet, the research in discrete decision variable area has not been fully studied. A new research trend for optimizing discrete decision variables ha just appeared recently. This study, therefore, deals with a discrete simulation method to get the system evaluation criteria required for designing a complex probabilistic discrete event system and to search the effective and reliable alternatives to satisfy the objective values of the given system through a on-line, single run with the short time period. Finding the alternative, we construct an algorithm which changes values of decision variables and a design alternative by using the stopping algorithm which ends the simulation in a steady state of system. To avoid the loss of data while analyzing the acquired design alternative in the steady state, we provide background for estimation of an auto-regressive model and mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data through simulation with the short time period. In numerical experiment we applied the proposed algorithm to (s, S) inventory system problem with varying Δt value. In case of the (s, S) inventory system, we obtained good design alternative when Δt value is larger than 100.

• Korean Journal of Computational Design and Engineering
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• v.13 no.1
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• pp.67-75
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• 2008

In spite of rapidly increasing interests in digital manufacturing, there still lacks of a systematic approach in manufacturing line flow analysis via modeling and simulation; currently, the parameters for designing manufacturing line are defined by being solely based on engineers experiences. The paper proposes an application of the genetic algorithm to a discrete event line simulation finding optimal set of parameters for manufacturing line balancing problem. The proposed method has been applied to two example problems-one is a simple manufacturing model and the other for shipyard industry-in order to demonstrate its validity and usefulness.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1090-1101
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• 2017

This study analyzes spatial density and integration of Space Syntax and Discrete Event Simulation (DEVS) of complex system theory and analyzes spatial structure by property, type and depth. The aim of this study is to secure the validity of the theoretical application. The study evaluated the correlation between spatial density and integration by setting up eight types of analysis models. In addition, analyzed the correlation of structural characteristics and approached the application of discrete event simulation of spatial syntax theory. It is confirmed that the concept of integration of spatial syntax theory and analysis using discrete event simulation are valid as new spatial analysis methodology. Also expect that realistic and concrete predictions will be possible if discrete event simulation evolves into research for space allocation and space efficiency optimization.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.1-16
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• 1999

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event system. The proposed algorithm in this paper searches the effective and reliable alternatives satisfying the target values of the system to be designed through a single run in a relatively short time period. It tries to estimate an autoregressive model, and construct mean and confidence interval for evaluating correctly the objective function obtained by small amount of output data. The experimental results using the proposed method are also shown.

• IE interfaces
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• v.24 no.1
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• pp.40-50
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• 2011

We develop a nonlinear integer programming model which minimizes the total cost with the optimal number of operators to hire and their optimal allocation to the tasks under the diverse constraints such as the weekly, daily, and hourly maximum allowable abandonment rates for the time-varying inbound call volume. We present a case study based on actual data at a call center, in order to prove the validity of applying the optimization method proposed. By the one-sample two-tailed t-test, we confirm that the expected abandonment rates resulting from the optimization method are identical with the ones from the discrete-event simulation within specified confidence intervals.

• ETRI Journal
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• v.40 no.6
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• pp.726-735
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• 2018

Femtocells are good examples of the ultimate networking technology, offering enhanced indoor coverage and higher data rate. However, the dense deployment of femto base stations (FBSs) and the exploitation of subcarrier reuse between macrocell base stations and FBSs result in significant co-tier and cross-tier interference, thus degrading system performance. Therefore, appropriate resource allocations are required to mitigate the interference. This paper proposes a discrete bacterial foraging optimization (DBFO) algorithm to find the optimal resource allocation in two-tier networks. The simulation results showed that DBFO outperforms the random-resource allocation and discrete particle swarm optimization (DPSO) considering the small number of steps taken by particles and bacteria.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.804-814
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• 2019

The patient's waiting time and length of stay have been reported as a factor decreasing their satisfaction in the hospital, especially in developing countries. This paper focuses on modeling hospital's outpatient department workflow in a developing country and optimizing the patient waiting time as well as total length of stay. By using discrete-event simulation, many alternative scenarios have raised, such as adding more working time, altering human resources, and adjusting the staff's responsibility, those scenarios will be examined to explore better settings for the hospital. The results show that possible to achieve a 9.6% reduction in patient total length of stay and it could be accomplished without adding more resources to the hospital.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.