Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal

Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal

Simultaneous allocation and scheduling of quay cranes, yard cranes, and trucks in dynamical integrated container terminal operations

We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use real-time trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature

Container vessel stowage plan using genetic, hill-climbing and simulated annealing algorithms

Container vessel stowage is vital for reducing extra cost and the vessel’s turn-around time in ports. Several studies have been conducted to be able to solve this problem. However, this problem has not been dealt with completely due to container stowage problem is complex problem which is literally considered as a NP-hard problem. Hence, the principal objective of this study is to find optimal container stowage plan for container vessel calling at multiple ports. Algorithms are the most widely-used methods for finding optimal solution in container vessel stowage problem. In this study, Genetic, Hill-Climbing and Simulated Annealing algorithm are implemented considering same size and two different types of containers (refrigerated and standard containers). After finding the numerical test results from these three algorithms, the effectiveness of the algorithms for the problem are evaluated