Capacity planning for mega container terminals with multi-objective and multi-fidelity simulation optimization. (2nd September 2017)

• Record Type: Journal Article
• Authors: Li, Haobin; Zhou, Chenhao; Lee, Byung Kwon; Lee, Loo Hay; Chew, Ek Peng; Goh, Rick Siow Mong
• Is Part Of: IISE transactions. Volume 49:Number 9(2017)
• Page Start: 849
• Page End: 862
• Publication Date: 2017-09-02
• Subjects: Capacity planning -- container terminals -- resource configuration -- simulation-based optimization -- multi-objective multi-fidelity optimization; Industrial engineering -- Periodicals; Systems engineering -- Periodicals; Industrial engineering; Systems engineering; Electronic journals; Periodicals; 670.285
• DOI: 10.1080/24725854.2017.1318229
• Languages: English
• ISSNs: 2472-5854

Abstract

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-MO 2 TOS), 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.