• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.11a
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• pp.62-67
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• 2004

Many ships in voyage experience weight and buoyancy distribution change by various reasons such as change of sea water density and waves, weather condition, and consumption of fuel, provisions, etc . The weight and buoyancy distribution change can bring the ships out of allowable trim, heeling angle. In these case, the ships should adjust trim and heeling angle by shifting of liquid cargo or ballasting, deballasting of ballast tanks for recovery of initial state or for a stable voyage. But, if the adjustment is performed incorrectly, ship's safety such as longitudinal strength, intact stability, propeller immersion, wide visibility, minimum forward draft cannot be secured correctly. So it is required that the adjustment of trim and heeling angle should be planned not by human operators but by optimization computer algorithm. To make an optimized plan to adjust trim and heeling angle guaranteeing the ship's safety and quickness of process, Uk! combined mechanical analysis and optimization algorithm. The candidate algorithms for the study were heuristic algorithm, meta-heuristic algorithm and uninformed searching algorithm. These are widely used in various kinds of optimization problems. Among them, heuristic algorithm $A^\ast$ was chosen for its optimality. The $A^\ast$ algorithm is then applied for the study. Three core elements of $A^\ast$ Algorithm consists of node, operator, evaluation function were modified and redefined. And we analyzed the $A^\ast$ algorithm by considering cooperation with loading instrument installed in most ships. Finally, the algorithm has been applied to tanker ship's various conditions such as Normal Ballast Condition, Homo Design Condition, Alternate Loading Condition, Also the test results are compared and discussed to confirm the efficiency and the usefulness of the methodology developed the system.

• 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.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.94-101
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• 2005

This paper presents the first known application of a meta-heuristic algorithm, variable neighbourhood descent (VND), to the redundancy allocation problem (RAP). The RAP, a well-known NP-hard problem, has been the subject of much prior work, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. The variable neighbourhood descent method has not yet been used in reliability design, yet it is a method that fits perfectly in those combinatorial problems with potential neighbourhood structures, as in the case of the RAP. A variable neighbourhood descent algorithm for the RAP is developed and tested on a set of well-known benchmark problems from the literature. Results on 33 test problems ranging from less to severely constrained conditions show that the variable neighbourhood descent method provides comparable solution quality at a very moderate computational cost in comparison with the best-known heuristics. Results also indicate that the VND method performs with little variability over random number seeds.

• Journal of ILASS-Korea
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• v.28 no.4
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• pp.161-168
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• 2023

Droplet impingement on solid surfaces is pivotal for a range of spray and heat transfer processes. This study aims to optimize the cooling performance of single droplet impingement on heated textured surfaces. We focused on maximizing the cooling effectiveness or the total contact area at the droplet maximum spread. For efficient estimation of the optimal values of the unknown variables, we introduced an enhanced Genetic Algorithm (GA) and Particle swarm optimization algorithm (PSO). These novel algorithms incorporate its developed theoretical backgrounds to compare proper optimized results. The comparison, considering the peak values of objective functions, computation durations, and the count of penalty particles, confirmed that PSO method offers swifter and more efficient searches, compared to GA algorithm, contributing finding the effective way for the spray and droplet impingement process.

• Korean Management Science Review
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• v.30 no.2
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• pp.31-42
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• 2013

The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.9-12
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• 2000

컨테이너 터미널에서 효율적인 적하작업 계획을 자동으로 생성하는 알고리즘을 연구하였다. 실제 터미널에서 계획자들이 적하작업 계획시에 고려하는 제약소건 및 효율적인 계획을 위한 고려사항을 조사하였다. 이를 바탕으로 1단계에서는 개미시스템(ant system)이라는 인공지능기법을 적용하여 제약조건을 만족시키면서 원활한 적하작업이 진행될 수 있도록 컨테이너 크레인과 트랜스퍼 크레인의 이동순서와 위치를 결정하고, 2단계에서는 1단계에서의 결과를 바탕으로 빔탐색법(beam search)을 사용하여 컨테이너 개개의 작업순서를 결정하는 알고리즘을 개발하였다. 또한 개발된 시스템의 성능을 검증하기 위하여 최근의 대형선반에 대한 실제 현장자료를 바탕으로 실험을 수행하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.314-321
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• 2019

The Self-Adaptive Vision Correction Algorithm (SAVCA) developed in this study was suggested for improving usability by modifying four parameters (Modulation Transfer Function Rate, Astigmatic Rate, Astigmatic Factor and Compression Factor) except for Division Rate 1 and Division Rate 2 among six parameters in Vision Correction Algorithm (VCA). For verification, SAVCA was applied to two-dimensional mathematical benchmark functions (Six hump camel back / Easton and fenton) and 30-dimensional mathematical benchmark functions (Schwefel / Hyper sphere). It showed superior performance to other algorithms (Harmony Search, Water Cycle Algorithm, VCA, Genetic Algorithms with Floating-point representation, Shuffled Complex Evolution algorithm and Modified Shuffled Complex Evolution). Finally, SAVCA showed the best results in the engineering problem (speed reducer design). SAVCA, which has not been subjected to complicated parameter adjustment procedures, will be applicable in various fields.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.33-36
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• 2006

OR 기법이 현실에 성공적으로 적용된 분야 중 하나가 차량 경로 문제 풀이며 이 분야에 대한 연구는 꾸준히 진행되어 오고 있다. 시간대 제약이 있는 차량 수 배송 문제는 기존의 차량 배송 문제에서 더욱 진보된 형태의 문제이다. 많은 연구가 있어왔지만, 이러한 형태의 문제들은 NP-hard 형태의 문제이므로 meta-heuristic 기법들이 많이 사용되었다. 유전자 알고리즘에 비해 타부 서치 기법이 많이 사용되었는데, 이는 시간대 제약이 있는 차량 수 배송 문제의 경우 유전자 알고리즘의 근간인 염색체 형태로 표현하기가 쉽지 않기 때문이다. 이에 본 논문에서는 시간대 제약이 있는 차량 수 배송 문제를 다수의 차량을 사용하는 Grouping Genetic Algorithms을 적용하여 이러한 어려움을 해결하였다. 또한, 기존의 유전자 알고리즘들은 초기 해 집단을 임의 선택 방식으로 구성하였지만, 초기 해 집단 구성을 heuristic 기법을 사용하여 적합도 함수 값이 좋은 해들로 구성하여 기존에 사용된 알고리즘들과 성능을 비교 분석하고자 한다.

• Structural Engineering and Mechanics
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• v.66 no.4
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• pp.465-476
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• 2018

In this paper, a new meta-heuristic optimization method is presented. This new method is named "Numbers Cup Optimization" (NCO). The NCO algorithm is inspired by the sport competitions. In this method, the objective function and the design variables are defined as the team and the team members, respectively. Similar to all cups, teams are arranged in groups and the competitions are performed in each group, separately. The best team in each group is determined by the minimum or maximum value of the objective function. The best teams would be allowed to the next round of the cup, by accomplishing minor changes. These teams get grouped again. This process continues until two teams arrive the final and the champion of the Numbers Cup would be identified. In this algorithm, the next cups (same iterations) will be repeated by the improvement of players' performance. To illustrate the capabilities of the proposed method, some standard functions were selected to optimize. Also, size optimization of three benchmark trusses is performed to test the efficiency of the NCO approach. The results obtained from this study, well illustrate the ability of the NCO in solving the optimization problems.

• The Journal of Information Systems
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• v.25 no.1
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• pp.159-182
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• 2016

Purpose This paper aims to develop a novel tabu search algorithm for solving the sequencing problems with precedence constraints. Due to constraints, the traditional meta heuristic methods can generate infeasible solutions during search procedure, which must be carefully dealt with. On the contrary, the candidate order based tabu search (COTS) is based on a novel neighborhood structure that guarantees the feasibility of solutions, and can dealt with a wide range of sequencing problems in flexible manner. Design/methodology/approach Candidate order scheme is a strategy for constructing a feasible sequence by iteratively appending an item at a time, and it has been successfully applied to genetic algorithm. The primary benefit of the candidate order scheme is that it can effectively deal with the additional constraints of sequencing problems and always generates the feasible solutions. In this paper, the candidate order scheme is used to design the neighborhood structure, tabu list and diversification operation of tabu search. Findings The COTS has been applied to the single machine job sequencing problems, and we can see that COTS can find the good solutions whether additional constraints exist or not. Especially, the experiment results reveal that the COTS is a promising approach for solving the sequencing problems with precedence constraints. In addition, the operations of COTS are intuitive and easy to understand, and it is expected that this paper will provide useful insights into the sequencing problems to the practitioners.