Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals

Abstract

At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics—Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search—guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable.

The dynamics of a maritime container terminal complex system: optimization process design

“…The advantage and operational efficiency of these logistics components depends on the fast and efficient movement of goods in the terminal. To increase operating efficiency, it is recommended to use software as suitable operating solutions [1], [2], [3 ] (Figure 1) [4] . *panaitescumariana1@gmail.com; phone +40 755 047 477; fax +40 241 617 260; www.cmu-edu.eu Figure 1.…”

“…From a microeconomic perspective, the implementation of such supply chains is a big challenge because it means involving a large number of operators, with different specializations, whose activity must be synchronized and coordinated. It is recommended that the smart decision support functionality on the sequence of container handling operations in the terminal to allow subordination planning the operation of containers in the terminal in relation to the objectives transmitted by the central system with regarding the terms of directing the containers to other terminals or to the recipients [1][2][3][4][5][6][7][8][9][10].…”

A Model for Developing Existing Ports Considering Economic Impact and Network Connectivity

“…Our investment problem is classified at the terminal design level. In a similar terminal management issue classification proposed in [5], our problem is classified into the terminal layout problem at the strategic and tactical levels. Decisions made at this strategic level determine the overall system's capacity when solving operation planning and real-time control issues-e.g., the amount of investment in logistics infrastructure sets the upper bound for the possible flows within the network [6].…”