• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.451-467
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• 1997

Tabu search (TS) is a useful strategy that has been successfully applied to a number of complex combinatorial optimization problems. By guiding the search using flexible memory processes and accepting disimproved solutions at some iterations, TS helps alleviate the risk of being trapped at a local optimum. In this article, we propose TS-based heuristics for solving a class of clustering problems, and compare the relative performances of the TS-based heuristic and the simulated annealing (SA) algorithm. Computational experiments show that the TS-based heuristic with a long-term memory offers a higher possibility of finding a better solution, while the TS-based heuristic without a long-term memory performs better than the others in terms of the combined measure of solution quality and computing effort required.

• IE interfaces
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• v.16 no.3
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• pp.332-343
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• 2003

This paper deals with pre-erection scheduling and erection scheduling in shipbuilding. Among shipbuilding scheduling, the ship erection scheduling in a dock is one of the most important since the dock is the most critical resource in a shipyard. However, it is more reasonable to consider pre-erection scheduling and erection scheduling as unified because they compete with the common constrained resources such as labor, crane, space, and so on. It is very hard to consider two scheduling problems simultaneously, and hence, we approach them sequentially. At first, we propose space resource leveling heuristics in pre-erection scheduling given erection date. And then, considering the manpower resource determined by pre-erection scheduling, we also propose manpower resource leveling heuristics in erection scheduling. Various experimental results with real world data show that the proposed heuristics have good performance in terms of scheduling quality and time.

• Journal of Navigation and Port Research
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• v.35 no.6
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• pp.501-507
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• 2011

This paper focuses on dispatching strategy for AGVs(Automated Guided Vehicle). The goal of AGV dispatching is assigning AGVs to requested job to minimizing the delay of QCs and the travel distance of AGVs. Due to the high dynamic nature of container terminal environment, the effect of dispatching is hard to predict thus it leads to frequent modification of dispatching decisions. In this situation, approaches based on a single rule are widely used due to its simplicity and small computational cost. However, these approaches have a limitation that cannot guarantee a satisfactory performance for the various performance measures. In this paper, dispatching strategy based on multicriteria heuristics is proposed. The Proposed strategy consists of multiple decision criteria. A multi-objective evolutionary algorithm is applied to optimize weights of those criteria. The result of simulation experiment shows that the proposed approach outperforms single rule-based dispatching approaches.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.218-219
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• 2011

This paper focuses on dispatching strategy for AGVs(Automated Guided Vehicle). The goal of AGV dispatching problem is allocating jobs to AGVs to minimizing QC delay and AGV total travel distance. Due to the highly dynamic nature of container terminal environment, the effect of dispatching is hard to predict thus it leads to frequent modification of dispatching results. Given this situation, single rule-based approach is widely used due to its simplicity and small computational cost. However, single rule-based approach has a limitation that cannot guarantee a satisfactory performance for the various performance measures. In this paper, dispatching strategy based on multicriteria heuristics is proposed. Proposed strategy consists of multiple decision criteria. A muti-objective evolutionary algorithm is applied to optimize weights of those criteria. The result of simulation experiment shows that the proposed approach outperforms single rule-based dispatching approaches.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.45-57
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• 2010

This paper considers a 2-stage assembly flowshop scheduling problem where each job is completed by assembling multiple components. The problem has the objective measure of minimizing total completion time. The problem is shown to be NP-complete in the strong sense. Thus, we derive some solution properties and propose three heuristic algorithms. Also, a mixed-integer programming model is developed and used to generate a lower bound for evaluating the performance of proposed heuristics. Numerical experiments demonstrate that the proposed heuristics are superior over those of previous research.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.1-8
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• 2023

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as "makespan". This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator's experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.906-909
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• 2000

Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristics and simplified algorithm. We use topographical features of characters to extract the character points and use KMST(Kruskal minimum spanning tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

• Earthquakes and Structures
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• v.23 no.5
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• pp.419-430
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• 2022

The metallic energy dissipation device (EDD) has been widely accepted as a useful tool for passive control of buildings against earthquakes. The distribution of metallic EDDs in a multi-story building may have significant influence on its seismic performance, which can be greatly enhanced if the distribution scheme is properly designed. This paper addresses the optimal distribution problem in the aim of achieving a desired level of performance using the minimum number of metallic EDDs. Five local search heuristic algorithms are proposed to solve the problem. Four base structures are presented as numerical examples to verify the proposed algorithms. It is indicated that the performance of different algorithms may vary when applied in different situations. Based on the results of the numerical verification, the recommended guidelines are finally proposed for choosing the appropriate algorithm in different occasions.