Estimating Discharge Time of Cargo Units – A Case of Ro-Ro Shipping

Abstract

Ro-Ro shipping is a dominant form of short sea freight transport. Ro-Ro ship operators are today unable to provide customers with precise information about when trailers are available for pickup by customers on the terminal despite vessel arrival times being well known in due time. This results in reduced truck utilization, longer waiting time for drivers, less efficient yard space utilization, potential terminal congestion and dissatisfied customers. In this paper the cargo unit discharge time estimation problem of Ro-Ro shipping is solved in collaboration with a European short-sea Ro-Ro shipping company. A module-based framework using statistical analysis for estimating the discharge time is proposed and tested. The initial framework is able to estimate the earliest pickup time of each individual truck or trailer within 1 hour accuracy for up to 70% of all cargo. The results of the study show potential for improving performance and accuracy. Further investigation and testing is currently ongoing by the case company based on the results from this study.

Stowage Planning with Optimal Ballast Water

“…Hence, it is essential to make a good stowage plan, as it impacts ship utilisation, fuel consumption, safety at sea and the ability to execute load and discharge operations efficiently. Moreover, it can also be used to derive accurate information of when cargo is available for pick-up by customers at the destination port [9].…”

Optimal dual cycling operations in roll-on roll-off terminals

“…For instance, the area of RoRo stowage is investigated regarding both optimizing stability, energy efficiency, and safety (Jia et al, 2020;Jia and Fagerholt, 2021), as well as packing and shifting costs (Hansen et al, 2016). Moreover, Jia et al (2019) analyze and estimate the discharge time of cargo units on a RoRo vessel based on different loading positions. Besides, there is research dealing with routing and scheduling of RoRo vessels (Øvstebø et al, 2011) as well as analysis of effects of sulphur emission limits on RoRo operations provided by Zis and Psaraftis (2017).…”

Rolling Cargo Management Using a Deep Reinforcement Learning Approach

“…In the unloading process, it is primordial to provide customers with precise information on when rolling cargo is available to be picked up by customers at the terminal. To fulfil this need, Reference [26] developed and tested a module based framework using statistical analysis for estimating the discharge time per cargo unit. The result achieved was the estimation of the earliest pick-up time of each individual truck or trailer within 1 h accuracy for up to 70% of all cargo.…”