A Fuzzy Logic-Based Algorithm to Solve the Slot Planning Problem in Container Vessels

Background: The slot planning problem is a container allocation problem within a certain location on a vessel. It is considered a sub-problem of a successful decomposition approach for the container vessel stowage planning problem. This decision has a direct effect on container handling operations and the vessel berthing time, which are key indicators for the container terminal efficiency. Methods: In this paper, an approach combining a rule-based fuzzy logic algorithm with a rule-based search algorithm is developed to solve the slot planning problem. The rules in the proposed fuzzy logic algorithm aim at improving the objective function and minimizing/eliminating constraint violation. Results: The computational results of 236 slot planning instances illustrate the efficiency and effectiveness of the proposed algorithm. Conclusions: The results show that the proposed approach is fast and can produce optimal or near-optimal solutions for a comprehensive industrial set of instances.

The Development of Stowage Planning Model for General Cargo Ship and Cargo Barge Vessel

As a maritime country whose water area is three times wider than its land, Indonesia has one of the ways to increase the logistic activities especially in adjusting to the development of the industry 4.0 by enhancing the productivity of maritime logistics in a way of streamlining the action at unit terminal containers. Stevedoring is a one of very important logistics activities in port operational ecosystem. In order to optimize the performance of the port, the efficiency of the stowage planning process is done. Some factors which can be evolved in stowage planning are processing time, ship stability, and minimum over stow. This research uses the stowage planning algorithm to develop an application in Python programming language. This application will eventually be used to create a stowage plan map for general cargo ship and cargo barge vessel in by prioritizing the ship stability, as well avoiding low over stow in a short time. With Python programming language a routine operational four hours job that used Microsoft Excel, only requires far less time with 10-15 seconds.

Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

The calculation of lashing forces on containerships is one of the most important aspects in terms of cargo safety, as well as slot utilization, especially for large containerships such as more than 10,000 TEU (Twenty-foot Equivalent Unit). It is a challenge for stowage planners when large containerships are in the last port of region because mostly the ship is full and the stacks on deck are very high. However, the lashing force calculation is highly dependent on the Classification society (Class) where the ship is certified; its formula is not published and it is different per each Class (e.g., Lloyd, DNVGL, ABS, BV, and so on). Therefore, the lashing result calculation can only be verified by the Class certified by the Onboard Stability Program (OSP). To ensure that the lashing result is compiled in the stowage plan submitted, stowage planners in office must rely on the same copy of OSP. This study introduces the model to extract the features and to predict the lashing forces with machine learning without explicit calculation of lashing force. The multimodal deep learning with the ANN, CNN and RNN, and AutoML approach is proposed for the machine learning model. The trained model is able to predict the lashing force result and its result is close to the result from its Class.