Prediction of arrival times and human resources allocation for container terminal

Abstract

Increasing competition in the container shipping sector has meant that\ud terminals are having to equip themselves with increasingly accurate analytical and\ud governance tools. A transhipment terminal is an extremely complex system in terms of both\ud organisation and management. Added to the uncertainty surrounding ships’ arrival time in\ud port and the costs resulting from over-underestimation of resources is the large number of\ud constraints and variables involved in port activities. Predicting ships delays in advance\ud means that the relative demand for each shift can be determined with greater accuracy, and\ud the basic resources then allocated to satisfy that demand. To this end, in this article we\ud propose two algorithms: a dynamic learning predictive algorithm based on neural networks\ud and an optimisation algorithm for resource allocation. The use of these two algorithms\ud permits on the one hand to reduce the uncertainty interval surrounding ships’ arrival in\ud port, ensuring that human resources can be planned around just two shifts. On the other\ud hand, operators can be optimally allocated for the entire workday, taking into account\ud actual demand and operations of the terminal. Moreover, as these algorithms are based on\ud general variables they can be applied to any transhipment terminal. Future integration of\ud the two models within a broader decision support system will provide an important support\ud tool for planners for fast, flexible planning of the terminal’s operations management

A Data Mining Approach to Forecast Late Arrivals in a Transhipment Container Terminal

“…In the event of a ship's delay, its berth space has to be re-allocated and the containers, that are already stacked in the yard on the basis of the original space assigned, have to be re-located as quickly as possible to minimize the berthing time (Berth Allocation Problem) (Zhen et al 2011;Salido et al 2012). Moreover, to optimize resources (personnel and equipment) management for handling operations and for establishing maintenance schedules it is important to know the effective arrival time of vessels (Fancello et al 2011;Gambardella et al 1998). …”

“…The second calculates the percentage of the total number of containers that should be transported by truck to the terminal, as a function of the ship's ETA. In particular, the only model capable of predicting ship arrival times has been calibrated by Fancello et al (2011). The decision support system presented here reduces the interval of uncertainty on ar- rival time in port to around 6 hours, employing a neural network model.…”

“…In particular if we compare it with the Neural Networks model calibrated at the University of Cagliari built to forecast the delay in the same port (Fancello et al 2011). Mean error on delay prediction was significantly lower, passing from around 2 hours 40 minutes to about 1 hour and 30 min.…”

Optimizing maritime travel time reliability

“…Korsvik et al 45 solved a planning problem by developing an efficient tabu search algorithm, and the solver is integrated in a prototype decision support system used by several shipping companies. Fancello et al 46 presented an approach for predicting ships' delays and then proposed two algorithms for resource allocation.…”

Planejamento e aplicação de modelos internacionais de organização do acesso portuário terrestre: o caso do Porto de Santos

“…Fatores importantes, nem sempre fáceis de controlar, podem afetar a qualidade dos serviços prestados e a eficiência geral do sistema. A incerteza da demanda tende a complicar o planejamento das tarefas, os resultados e a efetividade do planejamento como um todo (FANCELLO et al, 2011).…”