Scheduling of Berthing Resources at a Marine Container Terminal via the use of Genetic Algorithms: Current and Future Research

Abstract

The tremendous increase of containerized trade over the last several years, the resulting congestion in container terminals worldwide, the remarkable increase in containership size and capacity, the increased operating cost of container vessels and the adoption by liner shipping companies of yield management techniques strain the relationships between ocean carriers and container terminal operators. Shipping lines want their vessels to be served upon arrival or according to a favorable priority pattern and complete their loading/unloading operations within a prearranged time window, irrespective of the problems and shortage of resources terminal operators are facing. Therefore, allocating scarce seaside resources is considered to be a problem deserving both practical and theoretical attention. Scientific research has focused on scheduling problems dealing primarily with two of the most important seaside resources: berth space and quay cranes. Comprehensive reviews of applications and optimization models in the field of marine container terminal operations are given by Meersmans and Dekker (2001), Vis and de Koster (2003), Steenken et al. (2004), Vacca et al. (2007), and Stahlbock and Vos (2008). Scheduling of berth space, also called the berth scheduling problem (BSP), can be simply described as the problem of allocating space to vessels at the quay in a container terminal. The quay crane scheduling problem (QSP) can be described as the problem of allocating quay cranes to each vessel and vessel section. Vessels arrive at a container terminal over time and the terminal operator assigns them to berths to be served. To unload/load the containers from/onboard the vessel a number of quay cranes are assigned to each vessel. Ocean carriers, and therefore vessels, compete over the available berths and quay cranes, and different factors affect the berthing position, the start time of service, and the number of quay cranes assigned to each vessel. Several formulations have been presented for the BSP, the QSP, and recently for the combination of the BSP and QSP, the berth and quay crane scheduling problem (BQSP). Most of the model formulations have been single objective and it was not until recently that researchers recognized the multi-objective and multi-level character of these problems and introduced formulations that capture berth scheduling policies using the latter two formulations. The formulations that have appeared in the literature, in most cases, lead to NP-hard problems that require a heuristic or meta-heuristic algorithm to be developed in order to obtain a solution within computationally acceptable