Die amerikanischen Seehäfen sind für die US-Wirtschaft existenziell und stellen ein Tor zum globalen Markt dar. Heute benötigen sie jedoch entscheidende Infrastruktur- und Technologieverbesserungen. Am dringendsten sind ihre Bedürfnisse bei der Modernisierung und Erweiterung ihrer Schieneninfrastruktur und bei der intermodalen Kapazität. Die kanadischen Häfen investieren bereits stark in diesen Bereich. Die Canadian National Railway (CN), eine Güterbahn der Klasse 1, kündigte an, dass sie etwa 445 Millionen kanadische Dollar in Schieneninfrastrukturprojekte der Häfen von Vancouver und der Hafen von Prince Rupert investieren wird.
Der bestehende Bahnterminalbetrieb in den Seehäfen ist komplex und Kapazitätsschwankungen führen zu herausfordernden Betriebsszenarien. Störende Ereignisse wie COVID-19 schaffen zusätzliche Volatilität. Um ihren Betrieb zu optimieren und die intermodale Bahnkapazität zu erhöhen, wenden sich fortschrittliche Häfen einer auf künstlicher Intelligenz (KI) basierenden Optimierungssoftware zu.
Dieser Artikel ist ausschließlich in englischer Sprache erhältlich.
Increasing Container Velocity and Maximizing Critical Space
The American Association of Port Authorities (AAPA) reports that its members identified $20 billion in multimodal port and rail access needs through 2028. Rail access is associated with speedier vessel processing and is crucial to the multimodal movement of goods. Although 93% of ports have rail access, many ports‘ on-dock and near-dock rail systems infrastructure and technology require updates. When asked in an AAPA survey how much more throughput capacity could be added to their ports with improved rail access, 42.86% responded they expected capacity gains of more than 25%.
Last year, AAPA reported cargo activities at U.S. ports alone were responsible for $5.4 trillion in annual economic activity, supporting 30.8 million jobs and providing $378.1 billion in tax revenue to federal, state, and local governments. According to AAPA President and CEO Christopher J. Conner, „The economic downturn this year has caused significant economic damage to our ports, with an estimated decline of 20% to 30% of their total annual receipts.“
Although the port sector has been one of the hardest hit by COVID-19 disruptions to the supply chain, it largely continues to operate without interruption. Pre-pandemic efforts to digitize work processes can partially be credited for this performance. Still, the pandemic has shown the need for real-time optimization of terminal work processes.
Prior to the pandemic, the rise in ultra-large container ship traffic placed considerable strains on port infrastructure and operators‘ ability to expediently process growing cargo volumes. The first two quarters of 2020 marked a continued increase in these ship arrivals, creating congestion peaks and overwhelming landside and yard operations. This resulted in multi-day cargo clearing delays and lost cargo movements. To increase container handling and intermodal loading efficiencies, as well as enhance their existing Terminal Operating Systems‘ (TOS) capabilities, proactive terminal operators are turning to agile optimization software. One example is DP World Vancouver. It plans to increase its handling capacity at its Centerm container terminal by nearly 65% to 1.5 million TEU, while only increasing the terminal’s footprint by 15%. Centerm has been reconfigured and has undergone various road work projects. Its intermodal rail facilities also were expanded and modernized. A key component of the project was the real-time optimization of yard operations. AI-based optimization software helps improve intermodal yard operations, while maximizing productivity and reducing the need for future infrastructure investments.
Terminal Operating Systems
Today’s multimodal terminal operations are complex and have many components. By applying a TOS to gather data, more efficient management of assets, labor and work processes can be achieved. However, larger terminals and increasing container volumes, coupled with heightened volatility and uncertainty, demand improved ad-hoc decision making. Optimization software, powered by AI and Operations Research (OR) based algorithms, has proven to significantly improve the capabilities of existing TOSs. Concurrently, yard, crane, and vehicle utilization, as well as train load planning and real-time train load adjustments are significantly enhanced.
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