Solving the berth allocation problem (BAP) in ports is not trivial where the berth resources are limited and various sizes of vessels arrive with dramatically dissimilar loads. Especially in real scenarios, arriving vessels are accepted for a berth with the first come first served (FCFS) priority rule. This study proposes a decision support system coupled with a simulation optimization module based on the swarm-based Artificial Bee Colony optimization algorithm for solving the BAP. The proposed methodology was implemented for the Izmir port in Turkey. To investigate the influences of the vessel priorities on the BAP, four different experimental scenarios based on the single (SQM) and multiple queue models (MQM) were coupled with FCFS and proposed hybrid queue priority (HQP) rule. The results indicated that SQM scenarios were superior to MQM scenarios in a manner of minimizing the average vessel waiting times and the implementation of a dynamic berth allocation strategy for the MQM significantly decreases the vessel waiting times. Results of the SQM also imply that utilization of the HQP approach further minimizes the average vessel waiting times and increases the berth utilization and port throughput without yielding excessive waiting times for the larger vessels compared with the FCFS priority rule.

Container traffic forecasting is important for the operations and the design steps of a seaport facility. In this study, performances of the novel soft computing models were compared for the container traffic forecasting of principal Turkish seaports (Istanbul, Izmir, and Mersin seaports) with excessive container traffic. Four forecasting models were implemented based on Artificial Neural Network with Artificial Bee Colony and Levenberg-Marquardt Algorithms (ANN-ABC and ANN-LM), Multiple Nonlinear Regression with Genetic Algorithm (MNR-GA), and Least Square Support Vector Machine (LSSVM). Forecasts were carried out by using the past records of the gross domestic product, exports, and population of the Turkey as indicators of socioeconomic and demographic status. Performances of the forecasting models were evaluated with several performance metrics. Considering the testing period, the LSSVM, ANN-ABC, and ANN-LM models performed better than the MNR-GA model considering overall fitting and prediction performances of the extreme values in the testing data. The LSSVM model was found to be more reliable compared to the ANN models. Forecasting part of the study suggested that container traffic of the seaports will be increased up to 60%, 67%, and 95% at the 2023 for the Izmir, Mersin, and Istanbul seaports considering official growth scenarios of Turkey.

As marine cargo traffic continues to grow, ports are experiencing increasing problems with congestion. To address this issue without requiring significant capital investment, neighboring ports can share their capacity to meet the rising demand for cargo throughput. While there are many planning level studies on inter-port cooperation, there is a scarcity of operational-level studies, and there is currently no available dry port integrated cooperation scheme for port clusters that utilizes a microsimulation approach. This study aims to contribute to the existing literature by proposing a conceptual port integration scheme that includes a dry port for improved coordination between ports in clusters. The discrete event simulation (DES) approach was used to construct three representative microsimulation models with dry port integration considering vessel transfer policies and no-cooperation scenario. The outputs of the models were evaluated using performance metrics (vessel delays, storage capacities, and the number of serviced vessels) using t-test statistics. The results show that the cooperation scheme with the vessel transfer policy and the strategic management of vessel transfer can significantly reduce the vessel operation delay by over 39% for the no-cooperation scenario with an integrated dry port and this value is further improved to 62% if a simulation-based port selection module (PSM) is used for vessel transfer policy. Additionally, the mean number of containers of the average of two port storages decreased by 40% and further decreased by 69% with the PSM. In terms of decision-making performance for vessel transfer decisions with varying quay lengths, PSM was determined to be superior to the vessel transfer policy considering the number of vessels in port queues. The proposed conceptual port integration model and approach can assist decision-makers in evaluating the effectiveness of different cooperation schemes and vessel transfer policies for adjacent ports in port clusters.

The port induced freight can cause traffic congestion problems in city ports if road freight transportation is used. The secondary congestion problem arises from the pooling of the trucks at the port gates because of the delays of the port operations. The absence of the truck parking areas inside the port causes additional truck trips between the port and auxiliary truck servicing areas around the port. For reducing the impacts of the associated problems of the port induced truck traffic, truck parking areas can be used as buffer zones between the port and city. The purpose of this study is to develop a Decision Support System (DSS) with using multimethod simulation and cost optimization model for the capacity design of a truck parking area for a city port. The preliminary design of the parking area is used to estimate the development cost and outputs of the simulation model is coupled for the capacity optimization for truck arrival scenarios. The methodology is implemented for a case study of the İzmir city port in Turkey. The results of the study indicated that significantly different parking area capacities are required for different truck dwell times for the time restricted and unrestricted truck arrivals