Connectivity – from sea to rail, sea to road, road to rail

For a nation of 17,000 islands, ports are essential to the delivery of goods to more than 260 million people. Understanding the port‐road‐rail interface is central for port authorities in Indonesia, Australia and worldwide. This project aimed to predict the capacity of this infrastructure under different configurations to facilitate the development of a tool that can be used by infrastructure planners. This project set out to create a better understanding of container logistics infrastructure requirements in Indonesia, particularly the port-rail interface. It was determined early in the research project that land-side transport was the bottleneck and needed far more attention than the details of the logistics inside the port.

Port-road-rail connectivity is crucial for logistics efficiency. Traffic congestion, especially in port access, is one of the main bottlenecks in container distribution in Indonesia. The lack of rail connectivity and low usage of rail transportation is one potential factor that could be improved to address these problems.


The study employed multi-level planning approaches to consider connectivity issues. The planning level models can be categorised into strategic planning and tactical planning. Strategic planning considers factors such as the location of terminals, additional connectivity requirements, and long-term infrastructure planning. In the tactical model, high-level congestion modeling is considered. The model takes into account the mode split availability, flow, capacities, and infrastructure utilisation for given transportation networks.

The study created and published a significant dataset to allow more comprehensive studies of bottlenecks and congestion of container movement. Several new mathematical models were developed to optimise the configuration of the network and determine optimal container flows for a given network design. The final output from the project includes software to allow Indonesian partners to investigate alternative scenarios using one of the optimisation models.

The project employed a novel method, developing mathematical optimisation models that take into account current conditions and future development plans for port-road-rail interface in Indonesia. Optimisation models were produced using commercial optimisation solvers. Heuristic approximation approaches were developed to give a feasible solution in a reasonable computing time. Additionally, numerical simulation was utilised to include uncertainty factors that are difficult to address in the static model.


The research shows that by investing in the establishment of inland container hubs, the total congestion cost and fixed cost is reduced. Introduction of discounted pricing on rail transport was shown to influence network flow and throughput, and can lead to congestion reduction on roads around seaports. Through these capital investment instruments, more containers are transferred to seaports via rail. This causes shorter queues at ports for ships and better utilisation of port facilities.

However, not all seaports automatically see reduced congestion due to increased rail facilities, as rail stations can attract additional truck traffic to transfer containers to rail. A better pricing policy results in more efficient usage of port facilities, as well as reduced road congestion. The tactical model, which uses a given optimised network from the strategic model, supports the finding that multimodal connectivity can help to reduce road load and system costs.

The study found that it is important to consider factors such as location of container terminals, rail and road infrastructure, and future demand. The application can be used by infrastructure planners at various stages of planning and developing transportation networks in Indonesia. The model and solution provide the ability to experiment and find an optimal configuration for container distribution planning.

Several new mathematical models were developed to optimise network configuration and determine optimal container flows for a given network design. The first was the strategic model, a form of optimisation model to address the question of where container hubs should be located to take full advantage of intermodal container transportation. This model was applied to the existing rail/road transportation network in Indonesia to consider several potential intermodal terminals. The second model was the tactical model. This is an optimisation model for route selection to alleviate congestion around container terminals. This model investigated alternative scenarios of infrastructure development.

In addition to developing the models above, the study has created and published a significant dataset representing the Indonesian container distribution network, allowing the study of bottlenecks and congestion in container movements. Indonesia has unique geographical conditions, covering a total area of 1,913,579 square kilometers and consisting of approximately 17,500 islands in 34 provinces. Hence, the Indonesian container distribution network is connected through a sparse network of road, rail, and sea links. Existing datasets do not take into account the sparse network and conditions of intermodal requirements in Indonesia. The Indonesia Container Dataset (ICD) contains an original-destination flow demand matrix, operational and fixed costs, a set of potential hub locations, and sets of links with three transfer modes in the network. The ICD enables researcher and practitioner to experiment with different network and demand configurations.



Journal articles

Mokhtar H., Perwira Redi A.A.N., Krishnamoorthy M., & Ernst A. T. (2018). An intermodal hub location problem for container distribution in Indonesia, Computers and Operations Research, 104, 415-432.

Perwira Redi A.A.N., Krishnamoorthy M., & Ernst A.T. Lagrangian Particle Swarm Optimisation for the Concave-Cost Network Flow Problem


Public Indonesian Logistics Data: Assembled from publicly available information on container logistics, the dataset includes infrastructure, cost information, travel times and estimated demand. Researchers and policy analysts can use this data to compare logistics and port access options, and develop new modelling.

Optimisation-based Logistics Simulation Software: Allows sophisticated simulation of container logistics infrastructure use, subject to intelligent (optimised) allocation of freight demand and non-linear costs.