A robust approximation for yard template optimization under uncertainty. (June 2022)

• Record Type: Journal Article
• Authors: Yang, Lingyi; Ng, Tsan Sheng; Lee, Loo Hay
• Is Part Of: Transportation research. Volume 160(2022)
• Page Start: 21
• Page End: 53
• Publication Date: 2022-06
• Subjects: Robust optimization -- Column generation -- Yard template -- Port operation -- Mixed integer programming; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2022.03.005
• Languages: English
• ISSNs: 0191-2615

Abstract

Abstract: This paper considers a container yard template optimization problem when the arrival schedule of vessels calling at the port is uncertain. First, container storage locations in the yard need to be assigned to vessels, which essentially forms the yard template. When the arrival of the vessels becomes firmed, the operation schedule to store and retrieve containers from the yard is determined, taking into account the vessel turnaround time requirements and activity congestion in the yard. We develop a two-stage robust approximation model in the form of a single linear mixed integer program to minimize the maximum cost associated with excessive activity concentrations and travel distance from the vessel berth to the container storage locations. An accelerated column generation meta-heuristic solution approach is then proposed, which integrates adaptive large neighborhood search, evolutionary algorithm, and column generation algorithm to generate near-optimal solutions in a reasonable time. Numerical results show that the proposed solution algorithm performs favorably compared to other standalone approaches. Highlights: A robust yard template model incorporates vessel arrivals uncertainty. A novel robust approximation model RAYTOP is proposed for the problem. An efficient column generation based hybrid meta-heuristic is developed. The quantitative impact of vessel arrival times on yard congestion is revealed.