• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2417-2419
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• 2001

A path planning is one of the main subjects in a mobile robot. It is divided into two parts. One is a global path planning and another is a local path planning. This paper, using the formal two methods, presents that the mobile robot moves to multi-targets with avoiding unknown obstacles. For the shortest time and the lowest cost, the mobile robot has to find a optimal path between targets. To find a optimal global path, we used GA(Genetic Algorithm) that has advantage of optimization. After finding the global path, the mobile robot has to move toward targets without a collision. FLC(Fuzzy Logic Controller) is used for local path planning. FLC decides where and how faster the mobile robot moves. The validity of the study that searches the shortest global path using GA in multi targets and moves to targets without a collision using FLC, is verified by simulations.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.1
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• pp.51-58
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• 1990

A deterministic capacity expansion planning model for a two-capacity type facility is analyzed to determine the sizes to be expanded in each period so as to supply the known demands for two distinct capacity type(product) on time and to minimize the total cost incurred over a finite planning horizon of T periods. The model assumes that capacity unit of the facility simultaneously serves a prespecified number of demand units of each capacity type, that capacity type 1 can be used to supply demands for capacity type 2, but that capacity type 2 can't be used to supply demands for capacity type 1. Capacity expansion and excess capacity holding cost functions considered are nondecreasing and concave. The structure of an optimal solution is characterized and then used in developing an efficient dynamic programming algorithm that finds optimal capacity planning policy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.601-606
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• 1998

In this paper, we propose a new design method of an optimal fuzzy logic controller using co-evolutionary concept. In general, it is very difficult to find optimal fuzzy rules by experience when the input and/or output variables are going to increase. Futhermore proper fuzzy partitioning is not deterministic ad there is no unique solution. So we propose a co-evolutionary method finding optimal fuzzy rules and proper fuzzy membership functions at the same time. Predator-Prey co-evolution and symbiotic co-evolution algorithms, typical approaching methods to co-evolution, are reviewed, and dynamic fitness landscape associated with co-evolution is explained. Our algorithm is that after constructing two population groups made up of rule base and membership function, by co-evolving these two populations, we find optimal fuzzy logic controller. By applying the propose method to a path planning problem of autonomous mobile robots when moving objects applying the proposed method to a pa h planning problem of autonomous mobile robots when moving objects exist, we show the validity of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1038-1043
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• 1997

To adapt the neural network proess for the purpose of determination of optimal utting onditions (optimal cutting speed and feed rate), some selection strategies for the machining factors are necessary, which is considered planning cutting process. In this case, factors that have both nonlinearity and strong relationship must be selected. Although tool grade and chemical properties of workpiece material have strong effect to cutting speed, it's not easy to find a analytic relation between them. In this paper, a mathematical method for determining the optimal amount of cutting (depth of cut, feed rate) is presented by tool goemetry and heat generation during cutting process. And various tool grade and workpiece material groups ase classified based on its chemical properties. Thier chemical composition and hardness are used as input pattern for neural network learnig. The result of learning shows the relationship between tool grade and workpiece material and it is proved that it can be used as a sub-system for automatic process planning system.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.212-215
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• 2008

In this paper, we develop the optimal path planning algorithm using the improved Dijkstra algorithm and the particle swarm optimization. To get the optimal path, at first we construct the MAKLINK on the world environment and then make a graph associated with the MAKLINK. The MAKLINK is a set of edges which consist of the convex set. Some of the edges come from the edges of the obstacles. From the graph, we obtain the Dijkstra path between the starting point and the destination point. From the optimal path, we search the improved Dijkstra path using the graph. Finally, applying the particle swarm optimization to the improved Dijkstra path, we obtain the optimal path for the mobile robot. It turns out that the proposed method has better performance than the result in [1] through the experiment.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.231-249
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• 2014

This paper discusses a green supply chain with a manufacturer and a collection trader, and it proposes an optimal production planning for remanufacturing of parts in used products with quality classification errors made by the collection trader. When a manufacturer accepts an order for parts from a retailer and procures used products from a collection trader, the collection trader might have some quality classification errors due to the lack of equipment or expert knowledge regarding quality classification. After procurement of used products, the manufacturer inspects if there are any classification errors. If errors are detected, the manufacturer reclassifies the misclassified (overestimated) used products at a cost. Accordingly, the manufacturer decides to remanufacture from the higher-quality used products based on a remanufacturing ratio or produce parts from new materials. This paper develops a mathematical model to find how quality classification errors affect the optimal decisions for a lower limit of procurement quality of used products and a remanufacturing ratio under the lower limit and the expected profit of the manufacturer. Numerical analysis investigates how quality of used products, the reclassification cost and the remanufacturing cost of used products affect the optimal production planning and the expected profit of a manufacturer.

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.71-79
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• 1993

Dynamic production planning problems are to determine the optimal production times and production quantities of product for discrete finite periods. In previous many researches, the solutions for these problems have been developed through the algorithms using dynamic programming. The purpose of this research is to suggest the new algorithm using linear programming. This research is to determine optimal production quantities of product in each period to satisfy dynamic for discrete finite periods, minimizing the total of production cost and inventory holding cost. Cost functions are concave, and no backlogging for product is allowed. The new algorithm for capacity constrained problem is developed.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.31-34
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• 2000

In this paper, we design an optimal path for multiple mobile robots. For this purpose, we propose a new method of path planning for multiple mobile robots in dynamic environment. First, every mobile robot searches a global path using a distance transform algorithm. Then we put subgoals at crooked path points and optimize them. And finally to obtain an optimal on-line local path, ever)r mobile robot searches a new path with static and dynamic obstacle avoidance.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.733-743
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• 1989

This paper deals with the control of system with controlled jump Markov disturbances. A such formulation was used by Boukas to model the planning production and maintenance of a FMS with failure machines. The optimal control problem of systems with controlled jump Markov process is addressed. This problem describes the planning production and preventive maintenance of production systems. The optimality conditions in both cases finite and infinite horizon, are derived. A numerical example is presented to validate the proposed results.

• Journal of IKEEE
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• v.13 no.2
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• pp.150-158
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• 2009

For the autonomous movement, the optimal path planning connecting between current and target positions is essential, and the optimal path of mobile robot means obstacle-free and the shortest length path to a target position. Many actual mobile robots should move without any information of surrounded obstacles. Thus, this paper suggests new methods of path planning and obstacle avoidment, suitable in unknown environments. This method of path planning always tracks the local target expected as the optimal one, and the result of continuous tracking becomes the first generated moving path. This path, however, do not regard the collision with obstacles. Thus, this paper suggests a new method of obstacle avoidance resembled with the Potential Field method. Finally, a simulation confirms the performance and correctness of the path planning and obstacle avoidance, suggested in this paper.