Ri Choe

Samsung (South Korea)

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Dual-population genetic algorithm for nonstationary optimization

Taejin Park Ri Choe Kwang Ryel Ryu

In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic algorithm (DPGA) is a novel evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Preliminary experimental results on various periods and degrees of environmental change have shown that the distance between the two populations of DPGA is one of the most important factors that affect its per-formance. However, it is very difficult to determine the best popu-lation distance without prior knowledge about the given problem. This paper proposes a new DPGA that uses two reserve populations (DPGA2). The reserve populations are at different distances from the main population. The information inflow from the reserve populations is controlled by survival selection. Experimental results show that DPGA2 shows a better performance than other evolutionary algorithms for nonstationary optimization problems without relying on prior knowledge about the problem.

10.1145/1389095.1389286

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Citations (15)

Generating a rehandling-free intra-block remarshaling plan for an automated container yard

Journal of Intelligent Manufacturing (2009)

Ri Choe Taejin Park Myung-Seob Oh Jaeho Kang Kwang Ryel Ryu

Yard

10.1007/s10845-009-0273-y

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Citations (46)

Multi-objective optimization of dispatching strategies for situation-adaptive AGV operation in an automated container terminal

Jeongmin Kim Ri Choe Kwang Ryel Ryu

We propose a multi-criteria dispatching strategy for efficiently operating automated guided vehicles (AGVs) in an automated container terminal. The criteria employed by our strategy are carefully devised so that the enforcement of the strategy can lead to achieving two objectives, which are minimization of the delay of quay cranes and minimization of empty travel by AGVs. The first objective is for the productivity of the terminal and the second the reduction of CO2 emission. Optimization of the strategy is done by using a multi-objective evolutionary algorithm (MOEA) to obtain a set of Pareto optimal strategies. When dispatching AGVs, we can apply any strategy from this set depending on which objective is more important than the other at that moment. Since the evaluation of a strategy during the search requires a computationally expensive simulation, it becomes inaccurate unless done thoroughly. We use a noise-tolerant MOEA that takes multiple evaluations of the candidate strategies for a more accurate estimation. Yet, it saves the computation by tactically allocating more chances of evaluations to better candidates.

Minification

10.1145/2513228.2513277

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Citations (12)

Allelic Variation at Loci Associated with Grain Properties in Korean Wheat Cultivars

Korean Journal of Breeding Science (2020)

Kyeong-Min Kim Seong-Woo Cho Ri Choe Na Kyung Ryu Jung-Yoon Hwang

Kyeong-Min Kim, Seong-Woo Cho, Ri Choe, Na Kyung Ryu, Jung-Yoon Hwang, Ji-Woo Kim, Kyeong-Hoon Kim, Chon-Sik Kang, and Chul Soo Park. Korean J. Breed. Sci. 2020;52:362-73. https://doi.org/10.9787/KJBS.2020.52.4.362

Variation (astronomy)

10.9787/kjbs.2020.52.4.362

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Citations (4)

Quay Crane Scheduling Considering the Workload of Yard Blocks in an Automated Container Terminal

Journal of Intelligence and Information Systems (2008)

Seung-Hwan Lee Ri Choe Tae‐Jin Park Kap Hwan Kim Kwang-Ryel Ryu

This paper proposes quay crane (QC) scheduling algorithms that determine the working sequence of QCs over ship bays in a container vessel in automated container terminals. We propose two scheduling algorithms that examine the distribution of export containers in the stacking yard and determine the sequence of ship bays to balance the workload distribution among the yard blocks. One of the algorithms is a simple heuristic algorithm which dynamically selects the next ship bay based on the entropy of workloads among yard blocks whenever a QC finishes loading containers at a ship bay and the other uses genetic algorithm to search the optimal sequence of ship bays. To evaluate the fitness of each chromosome in the genetic algorithm, we have devised a method that is able to calculate an approximation of loading time of container vessels considering the workloads among yard blocks. Simulation experiments have been carried out to compare the efficiency of the proposed algorithms. The results show that our QC scheduling algorithms are efficient in reducing the turn-around time of container vessels.

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Heuristics

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Citations (0)

Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm

Ri Choe Hui Yuan Youngjee Yang Kwang Ryel Ryu

We address the problem of scheduling twin automated stacking cranes (ASCs) used in automated container terminals. By extending the previous works, we show that it is important to make explicit the hidden jobs needed to prepare for the main requested jobs. Since the preparatory jobs can be done by any of the two ASCs, appropriate assignment of these jobs can help to promote cooperation and avoid interference between the two ASCs. The proposed genetic algorithm (GA) performs search within the framework of iterative rescheduling to cope with the uncertainty of ASC operation. To boost the search performance under tight real-time constraint of iterative rescheduling, our GA uses some of the solutions of the previous iteration to initialize the population of the current iteration. It has also been shown that our GA performs more robustly than other algorithm such as simulated annealing in an uncertain environment.

10.1145/2245276.2245323

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Citations (14)

Optimizing Automated Stacking Crane Dispatching Strategy Using an MOEA for an Automated Container Terminal

한국항해항만학회 학술대회논문집 (2011)

Jiemin Wu Ri Choe Taejin Park Kwang Ryel Ryu

The problem of automated stacking cranes (ASC) dispatching in container terminals is addressed in this paper. We propose a heuristic-based ASC...

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Citations (1)

Real Time Scheduling for Multiple Yard Cranes in an Automated Container Terminal

Journal of Navigation and Port Research (2007)

Tae‐Jin Park Ri Choe Kwang-Ryel Ryu

본 논문은 교행이 불가능한 두 대의 자동화 크레인을 운영하는 자동화 컨테이너 터미널의 장치장을 대상으로 국지적 탐색 알고리즘을 이용해 실시간 작업계획을 수립하는 방안을 제안한다. 제안방안은 실시간 제약조건을 만족시키기 위해 현재 이후 일정 시간의 작업만을 작업 계획의 대상으로 삼으며, 장치장의 동적인 작업 특성을 고려하여 새로운 작업이 요청될 때마다 작업 계획을 다시 수립한다. 또한, 교행이 불가능한 두 대의 크레인을 운영할 때 발생할 수 있는 크레인 간의 작업 부하 불균형을 해소하기 위해 작업 계획 과정에서 상대 크레인에 의한 사전 재취급과 사전 이적을 통한 두 크레인 간의 협업을 가능하게 하였다. 시뮬레이션을 이용한 실험 결과 제안 방안이 휴리스틱 방안에 비해 우수하며, 크레인 간의 협업이 작업 효율 향상에 도움이 됨을 확인하였다. This paper proposes a realtime scheduling method using local search algorithm for non-crossable yard cranes in automated container terminal. To take into consideration the dynamic property of yard crane operation and satisfy the real time constraint, the proposed method repeatedly builds crane schedule for the jobs in a fixed length look-ahead horizon whenever a new job is requested In addition, the proposed method enables the co-operation between yard cranes through prior re-handling and re-positioning in order to resolve the workload imbalance problem between the two cranes, which is one of the primary causes that lower the performance of yard cranes. Simulation-based experiments have shown that the proposed method outperforms the heuristic based methods, and the cooperation scheme contributes a lot to the performance improvement.

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Time horizon

10.5394/kinpr.2007.31.10.869

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Citations (0)

Crane scheduling for opportunistic remarshaling of containers in an automated stacking yard

Flexible Services and Manufacturing Journal (2013)

Ri Choe Taesung Kim Taekwang Kim Kwang Ryel Ryu

Yard

Time horizon

Idle

10.1007/s10696-013-9186-3