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

Particle morphology change and different experimental condition analysis during composite fabrication process by traditional ball milling with discrete element method (DEM) simulation were investigated. A simulation of the three dimensional motion of balls in a traditional ball mill for research on the grinding mechanism was carried out by DEM simulation. We studied the motion of the balls, the ball behavior energy and velocity; the forces acting on the balls were calculated using traditional ball milling as simulated by DEM. The effect of the operational variables such as the rotational speed, ball material and size on the flow velocity, collision force and total impact energy were analyzed. The results showed that increased rotation speed with interaction impact energy between balls and balls, balls and pots and walls and balls. The rotation speed increases with an increase of the impact energy. Experiments were conducted to quantify the grinding performance under the same conditions. Furthermore, the results showed that ball motion affects the particle morphology, which changed from irregular type to plate type with increasing rotation speed. The evolution was also found to depend on the impact energy increase of the grinding media. These findings are useful to understand and optimize the particle motion and grinding behavior of traditional ball mills.

Up-to-date manufacturing companies have faced a market-driven environment of pull production order. There should be a difference in operating manufacturing resources according to the type, quantity, and delivery time of manufactured products, because the process situation in pull production is changed by customer orders. And it should be taken into account from the stage of preparing for production such as process design and the placement and utilization of manufacturing resources. However, the feasibility of production plans is limited because most of small manufacturing businesses make production/supply plan of the parts and products assuming that equipment abilities in scheduling is sufficient without managing process standard information systemically. In this study, a discrete event simulation system based on BOM (bill of material), that is F-OPIS (online productivity innovation system), is introduced and a case study on application of the system leading to improving productivities is presented. F-OPIS deals with a decision-problem on production management and it is specialized for small-and- medium sized manufacturing companies. The target company of this case study is a typical small-and-medium sized manufacturing company in Korea, that produces various machined parts. The target company adopts make-to-stock production management to prevent tardy delivery because of fluctuations in demand. Therefore, it is required to apply an efficient inventory control solution for improving productivities. In this paper, based on the constraints of working capacity of manufacturing resources, the bottleneck process is analyzed as production conditions are changed. Consequently, an improvement plan is proposed, that eventually enhances overall utilization rates of resources in the bottleneck process and reduces overall production lead-time and inventory level.

This study develops a dynamic scheduling model for parallel machine scheduling problem based on genetic algorithm (GA). GA combined with discrete event simulation to minimize the makespan and verifies the effectiveness of the developed model. This research consists of two stages. In the first stage, work sequence will be generated using GA, and the second stage developed work schedule applied to a real work area to verify that it could be executed in real work environment and remove the overlapping work, which causes bottleneck and long lead time. If not, go back to the first stage and develop another schedule until satisfied. Small size problem was experimented and suggested a reasonable schedule within limited resources. As a result of this research, work efficiency is increased, cycle time is decreased, and due date is satisfied within existed resources.

The free sintering of metallic powders blended with non sintering inclusions is investigated by the Discrete Element Method (DEM). Each particle, whatever its nature (metallic or inclusion) is modeled as a sphere that interacts with its neighbors. We investigate the retarding effect of the inclusions on the sintering kinetics. Also, we present a simple coarsening model for the metallic particles, which allows large particles to grow at the expense of the smallest.