Recoverable robustness in weekly berth and quay crane planning

• Published in: Transportation research. Part B: methodological 2019-04, Vol.122, p.365-389
• Main Authors: Iris, Çağatay, Lam, Jasmine Siu Lee
• Format: Article
• Language: English
• Subjects: Berth allocation; Berthing; Buffer allocation; Buffers; Computer applications; Container terminals; Containers; Cranes; Cranes & hoists; Monte Carlo simulation; Optimization; Parameter uncertainty; Planning; Production scheduling; Quay crane planning; Recoverable robustness; Recovery plans; Robustness (mathematics); Schedule recovery; Schedules; Transport buildings, stations and terminals; Variation; Vessel delays; Vessels
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2019.02.013

•Recoverable robustness is introduced for the weekly berth and quay crane planning problem.•Uncertainty in vessel arrival times and quay crane handling rates are considered.•A mathematical model and a heuristic method are presented to solve the problem.•Balance between efficiency, robustness and recoverability is discussed.•The value of buffers and stochastic solution, and the effect of scenario solution are presented.•Strict robustness and recoverable robustness are compared for the ports. The performance of a container terminal heavily relies on how efficiently the quayside resources, which are mainly berth and quay cranes, are used. The quayside related planning problems face uncertainty in various parameters, and this makes the efficient planning of these operations even more complicated. This study aims at developing a recoverable robust optimization approach for the weekly berth and quay crane planning problem. In order to build systematic recoverable robustness, a proactive baseline schedule with reactive recovery costs has been suggested. The uncertainty of vessel arrivals and the fluctuation in the container handling rate of quay cranes are considered. The baseline schedule includes berthing positions, times and quay crane assignments for all vessels along with vessel-specific buffer times and buffer quay cranes. The problem also introduces recovery plans for each scenario. The objective is to minimize the cost of baseline schedule, the recovery costs from the baseline schedule and the cost of scenario solutions for different realizations of uncertain parameters. A mathematical model and an adaptive large neighborhood based heuristic framework are presented to solve the novel problem. Computational results point out the strength of the solution methods and practical relevance for container terminals.