• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.2
• /
• pp.25-33
• /
• 1997

A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.

• 한국정보통신설비학회:학술대회논문집
• /
• 2003.08a
• /
• pp.159-162
• /
• 2003

This paper presents a novel heuristic mechanism, Dynamic-network Adapted Cost selection (DAC-selection), which has higher backup path sharing rate, lower number of blocked channel requests and number of used wavelengths fer reservation of working path and backup path by using unique cost function than that of widely used random selection (R-selection) mechanism and Combined Min-cost selection (CMC-selection) mechanism proposed by Lo, while maintaining 100% restoration capability.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.825-827
• /
• 1989

A heuristic model which determines the scheduling of serial flowshops with minimization of the makespan is proposed for an idealized batch chemical plant. It generates an initial sequence by heuristic reasoning and improves it recursively until no improvement is possible. The heuristic reasoning is based on Johnson's Rule which gives the sequence with the minimum makespan for a two-unit flowshop. The evolutionary step searches the neighborhood of the current sequence for sequences with lower makespan. The robustness of this model is also examined by comparing the minimum makespan of literature examples with the theoretical one.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.609-614
• /
• 1989

A collision avoidance algorithm based on a heuristic graph search and subgoals is presented. The joint angle space is quantized into cells. The evaluation function for a heuristic search is defined by the sum of the distance between the links of a manipulator and middle planes among the obstables and the distance between the end-effector and the subgoals on desired trajectory. These subgoals reduce the combinatorial explosion in the search space. This method enables us to avoid a dead-lock in searching. Its effectiveness has been verified by simulation studies.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.05a
• /
• pp.741-743
• /
• 2003

A surface mount machine operation policy to assemble printed circuit boards is being developed in this research. The policy includes how to assign electronic components to slots on a component rack, and how to determine placement sequences on printed circuit board. The suggested heuristic uses information about component types and closeness relationships in each component on the board to assemble. First, the size of components and closeness ratings are used to divide them into two different size groups. Then rack assignment and placement routes are developed using component type and quantity information for a small size group, and followed by a large size group. Simulation models are developed using Visual C++ for performance evaluation of the heuristic. Necessary statistical analyses are provided to show the effectiveness of the suggested heuristic.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.23 no.61
• /
• pp.105-114
• /
• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.2
• /
• pp.189-195
• /
• 2012

Parcel delivery services are a bit different from general vehicle routing problems since the services require a deliver's walking time to give the parcel to the customer. This time takes more than 1/4 of total delivery time, but it is ignored for the convenience of algorithm design in general. The walking time is too big to be ignored if we need to estimate necessary delivery time precisely as much as possible, or to design a heuristic with varying vehicle moving speeds in metropolitan area. For these reasons, the walking times are surveyed and analyzed to use on the design of VRP heuristic for parcel delivery services in this research.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.3
• /
• pp.154-161
• /
• 2010

This paper considers the simultaneously firing model for the artillery operations. The objective of this paper is to find the optimal fire sequence minimizing the final completion time of the firing missions of multiple artillery units for multiple targets. In the problem analysis, we derive several solution properties to reduce the solution space. Moreover, two lower bounds of objective are derived and tested along with the derived properties within a branch-and-bound scheme. Two efficient heuristic algorithms are also developed. The overall performances of the proposed branch-and-bound and heuristic algorithms are evaluated through various numerical experiments.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.303-304
• /
• 2008

For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.305-312
• /
• 2014

This research deals with a problem that minimizes makespan in a non-identical parallel machine system with sequence and machine dependent setup times and machine dependent processing times. We first present a new mixed integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is large, we propose four heuristic algorithms including genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found the heuristic algorithms show different performances as the problem characteristics are changed and the simple heuristics show better performances than genetic algorithm based heuristics for the case when the numbers of jobs and/or machines are large.