Capacity planning for mega container terminals with multi-objective and multi-fidelity simulation optimization

ABSTRACT

Container terminals play a significant role as representative logistics facilities for contemporary trades by handling outbound, inbound, and transshipment containers to and from the sea (shipping liners) and the hinterland (consignees). Capacity planning is a fundamental decision process when constructing, expanding, or renovating a container terminal to meet demand, and the outcome of this planning is typically represented in terms of configurations of resources (e.g., the numbers of quay cranes, yard cranes, and vehicles), which enables the container flows to satisfy a high service level for vessels (e.g., berth-on-arrivals). This study presents a decision-making process that optimizes the capacity planning of large-scale container terminals. Advanced simulation-based optimization algorithms, such as Multi-Objective Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling (MO-MO²TOS), Multi-Objective Optimal Computing Budget Allocation (MOCBA), and Multi-Objective Convergent Optimization via Most-Promising-Area Stochastic Search (MO-COMPASS), were employed to formulate and optimally solve the large-scale multi-objective problem with multi-fidelity simulation models. Various simulation results are compared with one another in terms of the capacities over different resource configurations to understand the effect of various parameter settings on optimal capacity across the algorithms.

KEYWORDS:

• Capacity planning
• container terminals
• resource configuration
• simulation-based optimization
• multi-objective multi-fidelity optimization

Additional information

Notes on contributors

Haobin Li

Haobin Li is a scientist at the Institute of High Performance Computing, A*STAR Singapore. He received his B.Eng. degree (1st Class Honors) in 2009 from the Department of Industrial and Systems Engineering at the National University of Singapore, with a minor in computer science, and a Ph.D. degree from the same department in 2014. He has research interests in operations research, simulation optimization, and designing high-performance optimization tools with application on logistics and maritime studies.

Chenhao Zhou

Chenhao Zhou is a Research Fellow in the Department of Industrial Systems Engineering and Management, National University of Singapore. He received his Ph.D. degree from the same department in 2017. He received his B.Sc. degree in Automation Engineering from Xi'an Jiaotong University in 2012. His research interests include transportation and logistics problems in urban city and maritime industry with simulation, optimization and their combination to solve complex deterministic and stochastic optimization problems.

Byung Kwon Lee

Byung Kwon Lee is a research fellow in the Department of Industrial Systems Engineering and Management, National University of Singapore. He obtained a Ph.D. degree (industrial engineering) from Pusan National University in 2010 and master’s and bachelor’s degrees from Inha University, Korea. His research interests are maritime logistics and supply chain, transportation management, facilities layout and location, and statistical modeling.

Loo Hay Lee

Loo Hay Lee is an associate professor in the Department of Industrial Systems Engineering and Management, National University of Singapore. He received his B.S. (electrical engineering) degree from the National Taiwan University in 1992 and his S.M. and Ph.D. degrees in 1994 and 1997 from Harvard University. He is currently a senior member of IEEE, a committee member of ORSS, and a member of INFORMS. His research interests include production planning and control, logistics and vehicle routing, supply chain modeling, simulation-based optimization, and evolutionary computation.

Ek Peng Chew

Ek Peng Chew is an associate professor in the Department of Industrial and Systems Engineering and Management, National University of Singapore. He received his Ph.D. degree from the Georgia Institute of Technology. His research interests include logistics and inventory management, system modeling and simulation, and system optimization.

Rick Siow Mong Goh

Rick Siow Mong Goh is the Director of the Computing Science Department, A*STAR’s Institute of High Performance Computing. He leads a team of more than 60 scientists in performing world-leading scientific research, developing technology to commercialization, and engaging and collaborating with industry. The three core technology areas of the Computing Science Department are efficient computing, modeling and insights, and intelligent systems. The research focus areas include high-performance computing, artificial intelligence, distributed computing, complex systems, human–machine interaction, and modeling, simulation, and optimization. These scientists develop techniques that draw out efficiency, insight, and intelligence to power scientific discovery and technological advances. We have coupled these areas to collectively work on real-world applications such as in manufacturing, medical imaging and analysis, services and digital economy, maritime and port optimization, and urban transport systems. Dr. Goh received his Ph.D. in electrical and computer engineering from the National University of Singapore.