Modeling the effects of port disasters

Abstract

A b s t r a c t Weather or terrorism-related disasters at seaports can lead to significant economic losses from vessel and cargo delays. In times of such disasters, port capacities and optimal shipment routings would change rapidly, requiring near real-time analyses for planning response operations. To address this challenge, we offer a decision support system to help port networks analyze disaster response scenarios. As part of the model, an algorithm routes arriving ships to ports to optimize the use of network capacity with respect to ocean and inland transportation, port and inventory capital costs. To reflect changing port congestion conditions, port capacities are dynamically updated in the algorithm by regression-based parametric metamodels derived from the results of a simulation model. Demonstrating application through case studies of the North American container port network, we illustrate the magnitude of cost increases from disaster-based port shutdowns and highlight the practical need for coordinated, network-wide planning for disaster response.

Slow steaming impacts on ocean carriers and shippers

“…Without such work, the overall business case for slow steaming is ambiguous, and an acceptable trade-off between speed and cost will be difficult to determine. We address this gap below through an extension of a simulation-based model developed by Paul and Maloni (2010).…”

“…Following recent research (Luo and Grigalunas, 2003;Fan et al, 2009Fan et al, , 2010Paul and Maloni, 2010), we apply a simulation-based optimization approach to replicate a major ocean container lane under varying vessel speeds associated with slow steaming. Specifically, the model examines container flows to/from Asia through the Port of Los Angeles, the largest North American container port.…”

“…Specifically, the model examines container flows to/from Asia through the Port of Los Angeles, the largest North American container port. The methodology is reviewed in detail in a prior Maritime Economics & Logistics publication (Paul and Maloni, 2010), so we only highlight key elements below.…”

“…Port capacity is represented by critical resources including berth space, cranes and container storage space as obtained from survey data and publicly available information. We employ dynamic capacity modeling via regression-based parametric equations to reflect variable container processing times given potential delays during peak port throughput (Paul and Maloni, 2010). In other words, the simulation accounts for the phenomenon that container processing times will generally slow down when the port is busier.…”

“…We used a fixed 10 per cent capital rate (Saldanha et al, 2009) and a per-TEU cargo value of $20 000, though we vary cargo value below to reflect potential differences of distinct shippers. Port costs cover container loading, unloading and storage, including detention, at the Port of Los Angeles, as derived from actual cost data from container carriers (Paul and Maloni, 2010). We also captured inland costs in the United States based on a mix of rail and truck moves derived from historical data (Paul and Maloni, 2010).…”