Seabasing is a United States Navy strategy that allows Joint Forces to be supported from the sea. From a logistics perspective, seabasing will transform a set of vessels into floating distribution centers that are responsible for fulfilling supply orders from troops on shore. Vital components of seabasing include selective offloading capabilities in high-space-utilization environments, ship-to-objective logistics via aerial delivery, and vessel-to-vessel cargo replenishment. In addition, sea-based logistics operate in a challenging and uncertain environment. Thus, our research is interested in developing models to quantify and evaluate sea-based logistic system design in the face of imperfect visibility. We focus on two important sea-based logistics decisions: selective offloading in dense storage environments and prestaging decisions in vessel-to-vessel cargo transfer.
As illustrated in the diagram below, a sea base consists of multiple platforms and hierarchical levels. Two functional types make up a sea base. The first is Storage and Assembly, which is achieved through dynamic vessels and static platforms; the second is Transport, which is achieved through connectors, which can be watercraft, aircraft, unmanned systems or other technologies. For each of the functional types, platforms with varying capabilities and degrees of autonomy are employed. For the Storage and Assembly function, different vessel types are used to store and assemble resources. Examples of dynamic storage and assembly platform types include the T-AKE 1 USNS Lewis and Clarke Class Dry Cargo Vessels, the LMSR (large medium roll-on/roll-off) vessels, and autonomous vessels, among others. Static storage and assembly platform types could include abandoned oil rigs or other fixed structures. Each platform type consists of multiple individual instances of the platforms. Examples of internal platform functions include receiving, storage, retrieval, assembly, packaging, and shipping. For the Transport function, a wide range of connectors with varying capabilities and degrees of automation are available. Platform types include both aerial delivery platforms (such as the MV-22 tilt rotor aircraft and unmanned aerial vehicles), as well as watercraft (like Landing Craft Air Cushion (LCAC), Joint High Speed Vessels (JHSV), and Unmanned Watercraft). For most of the connector types, there are hundreds of individual platforms in operation.
Three different seabasing distribution network scenarios exhibit varying levels of complexity. In Table 1, the three scenarios — Iron Mountain, Skin-to-Skin Replenishment, and Tailored Resupply Packages — are described and mapped to common logistics system characteristics and decision problems.
Dense storage systems provide high-space utilization; however, because not all items are immediately accessible storage and retrieval operations often require shifting of other stored items in order to access the desired item. This shifting creates the propagation of uncertainty in item locations over time. As a result of location uncertainty, before an item can be retrieved, the item needs to be identified, which requires searching. We develop models that describe the propagation of uncertainty over time, as well as search plan optimization and expected search time models.
Underway Replenishment is a method for transferring cargo from one ship to another while the two ships are moving at sea. To reduce the amount of idle time and improve the utilization of the transfer process, the concept of prestaging cargo on the flight deck is used. Prestaging involves retrieving and storing cargo on the flight deck of the supply ship in anticipation of requested demand. The primary research question we are interested in is determining which items, and in what quantity, to prestage that balances the costs with the rewards of prestaging. A secondary objective is to quantify the impact that uncertainty has on the logistics process of transferring cargo between ships.
More information can be found on my ONR Young Investigator Grant to model and design responsive sea-based logistics systems.
For more details, see the following citations:
- Mofidi, Shahab, Pazour, Jennifer A., and Roy, Debit, 2018, “Proactive vs. Reactive Order-Fulllfiment Resource Allocation for Sea-based Logistics,” Transportation Research Part E: Logistics and Transportation Review.
2. Awwad, Mohamed, and Pazour, Jennifer A., 2018, “Search plan for a single item in an inverted T k-deep storage system,” Military Operations Research, 23. 1-18.
- Pazour, Jennifer A. and Shin, Ian, 2016 “Logistics Models to Support Order-fulfillment from the Sea”Progress in Material Handling Research: 2016, Material Handling Institute, Charlotte,NC. [Download Here]
- Reilly, Patrick, Pazour, Jennifer A., and Schneider, Kellie, 2017, “Propagation of Unit Location Uncertainty in Dense Storage Seabasing Environments,” International Journal of Production Research, 18, 5435-5449. [link]
- Scala, Natalie, and Pazour, Jennifer A., 2016, “A Value Model for Asset Tracking Technology to Support Naval Sea-based Resupply,” Engineering Management Journal (Special Issue on Engineering Management in the Military), 28.2, 120-130. [Link]
This work was supported through the Office of Naval Research via a Young Investigator Program: The Design of Responsive Sea-Based Logistic Delivery Systems, Award Number N00014-13-1-0594. Like sea based logistics, this research would not have been possible without the contributions of a number of talented individuals, including graduate students Mohamed Awwad, Kaveh Azadeh, Shahab Mofidi, Faraz Ramtin, Patrick Reilly, Kaan Unnu; undergraduate students Joan Climes, Kristin Elias, Catherine Ninah, Auree Postell, Corinne Skala, and Ian Shin, and faculty Debjit Roy, Natalie Scala, and Kellie Schneider.