A quantitative analysis of container vessel arrival planning strategies

Abstract

In maritime container transport, the random nature of vessel arrival and terminal service processes often lead to significant handling delays and/or resource underutilization. Arrival planning strategies (APS) promise to mitigate such undesirable effects by managing the variance of the terminal arrival process, taking different cost components and situational dynamics into account. We present a quantitative arrival scheduling simulation to analyze contrasting APS, in order to identify promising strategy design directions. Results are presented for central and decentral strategies, under high and low fuel price regimes. The analysis results in significant quantitative and qualitative differences between the strategies.

Vessel capacity restrictions in the fleet deployment problem: an application to the Panama Canal

“…Since the fuel consumption of a ship is nonlinearly related to its sailing speed, a higher speed mean significant consumption figures, with the consequent impact on the operating costs. Speed optimization has been widely studied (Cheaitou and Cariou 2012;Corbett et al 2009;Du et al 2011;Golias et al 2010;Lang and Veenstra 2010;Meng and Wang 2011b;Notteboom and Vernimmen 2009;Kontovas and Psaraftis 2011;Ronen 2011;Wang and Meng 2012c;. Schedulling (port departure and arrival times), conditions sailing speed and consequently transit times, so literature treating speed optimization usually treats schedule design jointly with transit times and transshipments (Gelareh and Meng 2010;Meng and Wang 2011b;Meng 2011, 2012a, d).…”

Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving

“…Most of works are related to the energy-saving of vessels in sailing period. In order to minimize the fuel consumption of vessels, Golias et al [21] and Lang and Veenstra [22] proposed that it can be realized by the way of potential coordination opportunity between terminal operators and shipping line. Therefore, the arrival time of vessels was regarded as decision variables instead of previously-known parameters when formulating the berth allocation problem in their works.…”

“…Constraints (21) ensure that a vessel cannot be located below and above another vessel in the berth-time plane. Constraints (22) and (23) define the number of berth deviation segments of Vessel i. Constraints (24) ensure that the number of berth deviation segments of Vessel i takes only one specific value.…”

“…Golias et al [21] tried to reduce fuel consumption and vessel emissions by minimizing the total waiting time of vessels, based on the assumption that the shorter the waiting time is, the less the fuel consumption and vessel emissions. Lang and Veenstra [22] provided a direct quantitative analysis on fuel consumption of a vessel and minimized the fuel consumption for sailing with a customized simulation tool. The optimization approaches of vessel sailing speed for minimizing the fuel consumption were proposed by Du et al [23] and Wang et al [24].…”

Bunker consumption optimization methods in shipping: A critical review and extensions

“…In the third category, port operators take into account the bunker cost of the shipping companies (Golias et al, 2010;Lang and Veenstra, 2010;Du et al, 2011;Wang et al, 2013) knots is berthed first, the resulting bunker cost reduction is smaller, because the bunker consumption is more sensitive to speed when the speed is higher.…”