• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2470-2476
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• 1997

We propose a new real-time search algorithm and provide experimental evaluation and comparison of the new algorithm with mini-min lookahead algorithm. Many other real-time heuristic-search approached often divide the problem space to several sub-problems. In this paper, the proposed algorithm guarantees not only the sub-problem deadline but also total deadline. Several heuristic real-time search algorithms such as $RTA^{\ast}$, SARTS and DYNORA have been proposed. The performance of such algorithms depend on the quality of their heuristic functions, because such algorithms estimate the search time based on the heuristic function. In real-world problem, however, we often fail to get an effective heuristic function beforehand. Therefore, we propose a new real-time algorithm that determines the sub-problem deadline based on the status of search space during sub-problem search process. That uses the cut-off method that is a dynamic stopping-criterion-strategy to search the sub-problem.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.8-14
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• 2012

Two dimensional nesting is a common problem in industries such as the shipbuilding, automotive, clothing, shoe-making, and furniture industries, in which various parts are cut off from stock or packed in a flat space while minimizing waste or unoccupied space. Nesting is known as an NP-complete problem, which has a solution time proportional to the superpolynomial of the input size. It becomes practically impossible to find an optimal solution using algorithmic methods as the number of shapes to nest increases. Therefore, heuristic methods are commonly used to solve nesting problems. This paper presents an expert system that uses a heuristic search method based on an evaluation function for nesting problems, in which parts and stock are represented by pixels. The system is developed in CLIPS, an expert system shell, and is applied to four different kinds of example problems to verify its applicability in practical problems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.283-289
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• 2003

This paper proposes an expert system which can determine automatically the shunting routes corresponding to the given shunting works by considering totally the train operating environments in the station. The expert system proposes the multiple shunting routes with priority of selection based on heuristic search strategy. Accordingly, system operator can select a shunting route with the safety and efficiency among the those shunting routes. The expert system consists of a main inference engine and a sub inference engine. The main inference engine determines the shunting routes with selection priority using the segment routes obtained from the sub inference engine. The heuristic rules are extracted from operating knowledges of the veteran route operator and station topology. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique. And, the validity of the builted expert system is proved by a test case for the model station.

• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.157-166
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• 2009

In this study, we developed a heuristic algorithm to get better efficiency of clustering than conventional algorithms. Conventional clustering algorithm had lower efficiency of clustering as there were no solid method for selecting initial center of cluster and as they had difficulty in search solution for clustering. EMC(Expanded Moving Center) heuristic algorithm was suggested to clear the problem of low efficiency in clustering. We developed algorithm to select initial center of cluster and search solution systematically in clustering. Experiments of clustering are performed to evaluate performance of EMC heuristic algorithm. Squared-error of EMC heuristic algorithm showed better performance for real case study and improved greatly with increase of cluster number than the other ones.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.2
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• pp.135-143
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• 1991

The setting problem of protective devices in power distribution systems is a combinatorial search problem involving a lot of trial & errors to search for a proper solution which has a great impact for the system reliability. This paper discusses the nature of the problem and proposes a scheme to prune the decision tree achieving the efficient search for a setting solution by applying heuristic rules which utilize pattern informations of backup and primary devices. Its effectiveness is demonstrated through many tests on real systems.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.33 no.12
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• pp.53-62
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• 2017

Many gradient-based mathematical methods have been developed and are in use for structural size optimization problems, in which the cross-sectional areas or sizing variables are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. The main objective of this paper is to propose an efficient optimization method for structures with discrete-sized variables based on the harmony search (HS) meta-heuristic algorithm that is derived using penalty function. The recently developed HS algorithm was conceptualized using the musical process of searching for a perfect state of harmony. It uses a stochastic random search instead of a gradient search so that derivative information is unnecessary. In this paper, a discrete search strategy using the HS algorithm with a static penalty function is presented in detail and its applicability using several standard truss examples is discussed. The numerical results reveal that the HS algorithm with the static penalty function proposed in this study is a powerful search and design optimization technique for structures with discrete-sized members.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.1
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• pp.90-101
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• 1998

This paper presents a scheduling method that uses Petri net modeling and heuristic search to handle the tool setup. In manufacturing systems, a tool is attached to a particular machine to process a particular operation. The activity to attach a tool to a particular machine and detach the tool from the machine requires time. The processing time of operations varies according to the attached tool and the machine used. The method proposed in this paper uses Petri net to model these characteristics and applies a search algorithm to the reachability graph of the Petri net model to generate an optimal or near-optimal schedule. New heuristic functions are developed for efficient search. The experimental results that show the effectiveness of the proposed method are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.609-614
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• 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
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• 1991.10a
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• pp.281-281
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• 1991

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.159-170
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• 2008

Nowadays, one of the important functionalities required from robot task planners is to generate plans to compose existing component services into a new service. In this paper, we introduce the design and implementation of a heuristic search planner, JPLAN, as a kernel module for component service composition. JPLAN uses a local search algorithm and planning graph heuristics. The local search algorithm, EHC+, is an extended version of the Enforced Hill-Climbing(EHC) which have shown high efficiency applied in state-space planners including FF. It requires some amount of additional local search, but it is expected to reduce overall amount of search to arrive at a goal state and get shorter plans. We also present some effective heuristic extraction methods which are necessarily needed for search on a large state-space. The heuristic extraction methods utilize planning graphs that have been first used for plan generation in Graphplan. We introduce some planning graph heuristics and then analyze their effects on plan generation through experiments.