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Enhancing Accessibility and Sustainability in Maritime Logistics: Evaluating PortalLite Towns

Introduction

This study explores the feasibility and efficiency of the PortalLite system, an innovative maritime logistics concept.

“Direct Trucking” is a system where shipping containers arrive at a central port and are distributed within a region via road and rail networks. This method has high transportation costs, carbon emissions, and road congestion. It is also impractical in areas without direct road connectivity, limiting access to goods and hindering economic growth.

The authors of this research paper propose the idea of “PortalLite Towns” with “PortalLite ports,” which are small, strategically located ports that receive containers from primary ports via small vessels. This innovative maritime logistics system aims to reduce reliance on road transportation, lower environmental impact, and make previously uninhabitable areas viable for settlement and economic activity.

The research uses both a simulation model and a mathematical (discussed in detail in the paper available to download below) model to assess the feasibility and environmental impact of the PortalLite system compared to direct trucking.

The analysis, based on real-world data, focuses on Chicago and identifies optimal locations for PortalLite ports and their investment payback period. The goal is to provide a framework for government officials to evaluate the implementation of the PortalLite system in their regions.


Two illustrations showing the direct trucking system on the left and the PortalLite town system on the right with different points, ports, and routes shown

The direct trucking system illustrated on the left compared with the PortalLite town on the right (click to enlarge)

Simulation model

The simulation model, developed in AnyLogic, uses Chicago to model the PortalLite system, leveraging the city’s waterway network. It includes six agents: demandPoints (customers), orders, largePort, smallPort, smallShips, and trucks.

The demandPoints are clustered from 500 zip codes near the Chicago Port into ten groups. Orders, generated from these demandPoints, follow a Poisson distribution based on population. Orders are processed at the nearest smallPort, which are predefined nodes in a GIS maps network, and then transported by smallShips to these ports before final delivery by trucks.

In the direct trucking system, trucks deliver orders straight from the largePort to their destinations. While the PortalLite system adds an intermediary step, using smallShips to transport orders to smallPorts before truck delivery


Maps showing ten zip code groups on the left and PortalLite ports on the right

Zip codes are clustered into nine demandPoints on the left, while PortalLite ports are nodes located on a GIS map on the right (click to enlarge)

Results

The study highlighted the transformative potential of the PortalLite system in maritime logistics through both simulation and mathematical modeling.

Key findings from the simulation model analysis include:

  • Costs per order decrease significantly when six or more PortalLite ports are opened (figure below, top left).
  • The PortalLite system reduces emissions only if the truck-to-ship emission ratio exceeds three (figure below, top right).
  • Cost savings occur when the truck-to-ship cost-per-mile ratio is greater than three (figure below, bottom left).
  • The PortalLite system’s effectiveness declines under high demand, with its capacity plateauing at maximum throughput (figure below, bottom right).

Different results for the portallite system illustrated graphically

Results of the simulation model analysis (click to enlarge)

Overall, these insights suggest that while the PortalLite system offers potential cost savings and environmental benefits, its performance is influenced by the number of ports and demand levels.

The findings also emphasize the need for further research to fully realize the PortalLite system’s potential, focusing on design, material handling, warehouse management, and operational aspects. This research will also need to address gaps such as demand variability and the impact of rail transportation.

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