• Journal of the military operations research society of Korea
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• v.6 no.2
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• pp.47-67
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• 1980

Interactions of major activities participating in fleet operations are investigated in the framework of a closed queueing network system with finite aircrafts assigned to it. An implementable algorithm is developed, which is useful for computing the distributions needed to evaluate the effects of the interactions on the fleet operations. The availability management program is focused on seeking an optimal resource allocation to multiple repair-shops to maximize the fleet availability subject to the budget constraint.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.167-180
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• 2006

This study expands limitation of OASIS(Optimal Allocation of Spares for Initial Supports) program, which calculates CSP(Concurrent Spare Part), not only availability but also cost, and developed the program enabling run in WINDOW OS. By considering multi-step repair and logistics support system, repairing capability at the time of deployment, and procurement period, this model is the first local model reflecting circumstances of the armed forces of the Republic of Korea. Furthermore, the programmed model was selected as the military standard software and has being essentially used for CSP calculation.

• 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.