Berth scheduling at marine container terminals: A universal island-based metaheuristic approach

• Published in: Maritime business review (Online) 2020-01, Vol.5 (1), p.30-66
• Main Authors: Kavoosi, Masoud, Dulebenets, Maxim A, Olumide Abioye, Pasha, Junayed, Oluwatosin Theophilus, Wang, Hui, Kampmann, Raphael, Mikijeljević, Marko
• Format: Article
• Language: English
• Subjects: Algorithms; Berthing; Connecting; Containers; Handling; Islands; Marine transportation; Objective function; Optimization; Scheduling; Sea transport; Subpopulations
• ISSN: 2397-3757; 2397-3765
• DOI: 10.1108/MABR-08-2019-0032

PurposeMarine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT.Design/methodology/approachA universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands.FindingsThe conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s.Research limitations/implicationsSome of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic.Practical implicationsThe developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational