Yard Template Planning in a Transshipment Hub: Gaussian Process Regression

Kang, Bonggwon; Park, Jungtae; Hong, Soondo; Joatiko, Permata Vallentino Eko

doi:10.1109/wsc57314.2022.10015251

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Terminal operators have widely relied on simulation-based decision-making to enhance container terminal performance, which shows a black-box function from complex and uncertain operations [12][13][14]. One practical simulation optimization approach for handshake locations is a manual iterative method.…”

Section: Introductionmentioning

confidence: 99%

Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-Based Automated Container Terminal Under Demand Uncertainty

Kang,

Kim,

Hong

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

A handshake operation can mitigate workload imbalance and interference between twin transporters in a material handling system. Terminal operators in a rail-based automated container terminal can employ the handshake operation to twin overhead shuttle cranes (OSs) under maritime demand uncertainty. Since a handshake location is critical to collaboration performance, terminal operators often rely on simulation experiments with a manual iterative design to determine optimal handshake locations. However, the simulation optimization is still challenging when a simulation execution is computationally expensive. This study proposes a Bayesian optimization-based approach to expedite the decision-making process. The approach infers the conditional outcomes of a simulation and actively searches optimal handshake locations. Our optimization results show that the proposed approach maximizes the collaborations between the twin OSs within fewer simulation runs. This study also provides extensive simulation analysis of the handshake locations. The experiments indicate that a handshake location has a significant influence on the required space for handshake operations and the workloads of the twin OSs.

show abstract

Section: Introductionmentioning

confidence: 99%

Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-Based Automated Container Terminal Under Demand Uncertainty

Kang,

Kim,

Hong

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Bayesian Optimization for the Vehicle Dwelling Policy in a Semiconductor Wafer Fab

Kang,

Park,

Kim

et al. 2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

Ambrosino,

Xie

2024

Modelling

View full text Add to dashboard Cite

This paper proposes an integrated approach to define the best consignment strategy for storing containers in an export yard of a maritime terminal. The storage strategy identifies the rules for grouping homogeneous containers, which are defined simultaneously with the assignment of each group of containers to the available blocks (bay-locations) in the yard. Unlike recent literature, this study focuses specifically on weight classes and their respective limits when establishing the consignment strategy. Another novel aspect of this work is the integration of a data-driven algorithm and operations research. The integrated approach is based on unsupervised learning and optimization models and allows us to solve large instances within a few seconds. Results obtained by spectral clustering are treated as input datasets for the optimization models. Two different formulations are described and compared: the main difference lies in how containers are assigned to bay-locations, shifting from a time-consuming individual container assignment to the assignment of groups of containers, which offers significant advantages in computational efficiency. Experimental tests are organized into three campaigns to evaluate the following: (i) The computational time and solution quality (i.e., space utilization) of the proposed models; (ii) The performance of these models against a benchmark model; (iii) The practical effectiveness of the proposed solution approach.

show abstract

Yard Template Planning in a Transshipment Hub: Gaussian Process Regression

Cited by 3 publications

References 32 publications

Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-Based Automated Container Terminal Under Demand Uncertainty

Simulation Optimization of Collaborative Handshake Operations for Twin Overhead Shuttle Cranes in a Rail-Based Automated Container Terminal Under Demand Uncertainty

Bayesian Optimization for the Vehicle Dwelling Policy in a Semiconductor Wafer Fab

Machine Learning-Based Optimization Models for Defining Storage Rules in Maritime Container Yards

Contact Info

Product

Resources

About