A public logistics network is proposed as an alternative to private logistics networks for the ground transport of parcels. Using the analogy between the packages transported in the network and the packets transmitted through the Internet, a package in a public logistics network could, for example, be sent from a retail store and then routed through a sequence of public distribution centers (DCs) located throughout the metropolitan area and then delivered to a customer’s home in a matter of hours, making a car trip to the store to get the package unnecessary. The DCs in the network, functioning like the routers in the Internet, could also be located at major highway interchanges for longer distance transport.
|Public Logistics Network||Internet|
|Packages transported||Packets transmitted|
|Distribution centers (DCs)||Routers|
Currently, it is common for a single logistics firm like UPS and FedEx to handle a package throughout its transport. The such a private logistics network, much of the technology used to coordinate the operation of the network is proprietary. As a result, the principal competitive advantage that a private logistics company has is the barrier to entry due to the very large scale of operation (national or international) required in order to be able to underwrite the development of private facilities and propriety technologies. Nevertheless, a single firm, unless it becomes a monopoly, is ultimately limited in the scale of its operation, resulting in the use of a limited number of large-scale hub transshipment points that can result in packages making many circuitous hops before reaching their destinations. In a public logistics network, the different functions of the network would be separated so that a single firm is not required for coordination. This would enable scale economies to be realized in performing each logistics function since each element of the network has access to potentially all of the network’s demand. The increase in scale would make it economical to have many more transshipment points. Each transshipment point, or distribution center (DC), could be an independently operated facility that serves as both a freight terminal and a public warehouse, and could be established in small cities and towns that would never have such facilities if they were served as part of a proprietary, private logistics network.
|Public Logistics Network||Private Logistics Network|
|Each truck and DC can be operated by different firm||Single firm (e.g., UPS) handles package throughout its transport|
|Similar to mesh computer network topology, where each node is connected to many other nodes||Similar to star computer network topology, where each node is connected to a central hub|
|Decentralized control via standard coordination protocols||Centralized control via firm-specific coordination procedures|
|Interoperability via standardized public DCs increases density of logistics network||Private hubs/terminals result in sparse logistics network|
|Diseconomies of scale with respect to package size due to modular storage||Economies of scale if traditional logistics technology used|
Home delivery logistics network: Current research is focused on developing a public logistics network in which goods can be delivered to the home in reusable standardized containers transported by driverless delivery vehicles (DDVs). Overall, the research on public logistics networks has produced a set of designs and modeling tools that address the following issues:
Coordination: Develop a distributed mechanism for coordinating the operation of each package, truck, and DC in the network. A pricing mechanism has been developed in which each package, prior to its transport, submits a bid that reflects its desired speed of delivery. This bid is then used to pay each truck that transports the package and each DC that stores the package along the path from its origin to its destination. Thus, at each DC, all of the packages going to the same DC are competing with each other to be in the next load to be transported to that DC, and that next load is competing with the loads going to other DCs to be selected by a truck as the next load for transport. In addition, the trucks are themselves competing with each other to select each load for transport. Because of this competition, truck capacity can automatically adjust to varying transport demand along each link in the network without any type of centralized control. The mechanism consists of truck and package protocols.
Modular storage design: In order to be cost-effective, the loading/unloading, sortation, and storage activities at each DC in the public logistics network must be highly automated since each load might visit a dozen or more DCs while it’s in transit, likely traveling on a different truck between each DC. Since existing automation technologies do not provide the flexibility needed to allow any size load to move to any location at anytime, a new DC design has been developed that would allow packages of varying size to be automatically unloaded at a DC, sorted, stored, and then loaded onto an outbound truck. Such a design would result in diseconomies of scale because it is cheaper to ship a single package compared to a larger consolidated load. Integral to the design is use of arrays of small square modules with orthogonal pop-up powered wheels.
Network design and performance analysis: Develop a set of modeling tools for estimating the performance of logistics networks like the proposed home delivery network. Design of the network would include determining the number and location of the DCs in the network, but this problem does not need to be "solved" in a finite optimization sense since the coordination protocols and modular storage design allows the network to adapt, with DCs added incrementally in response to market conditions. Network design is used just to predict performance.
Kay, Michael G., and Parlikad, Ajithkumar N., Material Flow Analysis of Public Logistics Networks, in Progress in Material Handling Research: 2002, R. Meller et al., Eds., Charlotte, NC: The Material Handling Institute, 2002, pp. 205–218 (presented at the 7th International Material Handling Research Colloquium, June 1–5, 2002, Portland, ME). Compares the average transport time of a hypothetical public logistics network covering the southeastern U.S. to the times of a hub-and-spoke and a point-to-point network covering the same region. The public logistics network provided the minimum average transport time when the time required for loading/unloading at each transshipment point in the network was short. This result is robust with respect to a range of different transport demands and truck capacities considered in the analysis.
Parlikad, Ajithkumar N., Performance Analysis of Intelligent Supply Chain Networks, Master’s Thesis, Dept. of Industrial Eng., North Carolina State Univ., Raleigh, NC, 2002.
Gandlur, Karthik S., Implementation of Adaptive Routing in Public Logistics Networks, Master’s Thesis, Dept. of Industrial Eng., North Carolina State Univ., Raleigh, NC, 2002.
Kay, Michael G., and Jain, Ashish, Issues in Agent-based Coordination of Public Logistics Networks, Tech. Rep. 02-01, Dept. of Industrial Engineering, North Carolina State Univ., Raleigh, NC, October 2002, describes some of the issues involved in research that is just starting to design an agent architecture and protocols that can be used to coordinate the operation of this type of network in order to facilitate adaptive routing and in-transit trade.
Bansal, Amogh, Designing a Public Logistics Network, Master’s Thesis, Dept. of Industrial Eng., North Carolina State Univ., Raleigh, NC, 2004. Initial work on the public logistics network design problem. A GA is used to design a PLN that covers the entire continental U.S.
Kay, Michael G., and Jain, Ashish, Pricing a Public Logistics Network, extended abstract of presentation at the Industrial Engineering Research Conference, May 15-19, 2004, Houston, TX. Initial ideas on protocol requirements.
Kay, Michael G., Protocol Design for a Public Logistics Network, in Progress in Material Handling Research: 2004, R. Meller et al., Eds., Charlotte, NC: The Material Handling Institute, 2004, pp. 181–188 (presented at the International Material Handling Research Colloquium, June 13–17, 2004, Graz, Austria). Initial specification of truck and package protocols.
Kay, Michael G., Design and Coordination of a Public Logistics Network, slides used for Poster Presentation at International Material Handling Research Colloquium, June 13–17, 2004, Graz, Austria and Invited Presentation at Distributed Information and Automation Laboratory, Institute for Manufacturing, University of Cambridge, Cambridge, U.K., June 18, 2004. Presents an overview of PLN and initial ideas for the Design of a Public Logistics Network, Design of a Public DC, and Network Coordination via Bidding.
Kay, Michael G., Design of a Public Distribution Center, Department of Industrial Engineering, North Carolina State University, June 30, 2004. Whitepaper describing proposed design for the DCs in a public logistics network. Main features of DC include: Fully automated loading/unloading; packages can be redirected any time prior to loading; low cost to build small DC, allowing many DCs to cover a single metropolitan area; and a small exterior footprint to allow locating in urban areas.
Jain, Ashish, Protocol Design for a Public Logistics Network, Master’s Thesis, Dept. of Industrial Eng., North Carolina State Univ., Raleigh, NC, 2004. Simulation of a public logistics network in order to estimate the waiting time at each DC, and implementation of a portion of the truck and package protocols specified in . Using a simulation of a public logistics network, weighted average waiting times with and without the use of the protocol were determined and it was found that there was a statistically significant decrease in the waiting time associated with the use of the protocol.
Kay, Michael G., and Jain, Ashish, Implementing a Pricing Mechanism for Public Logistics Networks, in Proceedings of the Industrial Engineering Research Conference, Atlanta, GA, May 14–18, 2005. This paper describes an implementation of the pricing mechanism specified in  and summarizes some of the results of .
Xiang, Ling, Kay, Michael G., and Telford, John S., Public logistic network protocol design and implementation, in Proceedings of the Industrial Engineering Research Conference, Nashville, TN, May 19–23, 2007.
Xiang, Ling, Kay, Michael G., and Telford, John S., Waiting time approximation in public logistics network, in Proceedings of the Industrial Engineering Research Conference, Vancouver, BC, Canada, May 17–21, 2008.
Xiang, Ling, Performance Model for a Public Logistics Network, Doctoral Dissertation, Dept. of Industrial and Syst. Eng., North Carolina State Univ., Raleigh, NC, 2009. In this dissertation, a heuristic approach to approximate the package average waiting time in a PLN is presented; and then based on this waiting time approximation, a PLN design procedure is developed.
Kay, Michael G., and Warsing, Donald P., A Distributed Coordination Mechanism for Shipment Consolidation, Depts. of Industrial and Syst. Eng. and Business Mgmt., North Carolina State Univ., Raleigh, NC, Sep. 22, 2010. Describes a mechanism for constructing consolidated shipment loads that uses only publically available information concerning each shipment along with simple standardized procedures to allocate the cost of transporting the load to the shipments in each load, where a computationally efficient pairwise Shapley value approximation is used for the allocation.
Datar, Mohit A., Priority-based Control Algorithm for Movement of Packages in a Public Distribution Center, Master's Thesis, Dept. of Industrial and Syst. Eng., North Carolina State Univ., Raleigh, NC, 2011. This thesis describes a control system for the movement of packages in a public DC.
The data used for the hypothetical 36-DC public logistics network, shown below, is available as a Matlab file plnex36.mat (8 KB). The file contains the structure DC, with fields containing the name, state, and longitude-latitude of each DC, and the population of the region surrounding each DC; the matrix IJD, which is the arc list for the network that connects adjacent DCs, with distances in miles; and XYLim, which are the limits of the region covered. In IJD, the distance along each arc is the total road distance between its DCs. (See Publication 1, above, for more details and the Matlog toolbox for functions that work with arc lists.)
Hypothetical public logistics network, above, shows 36 public DCs covering the southeastern portion of the USA and connected via interstate highways. Each of the interstate DCs would serve as a hub in a sub-network of local DCs (not shown) covering the region surrounding the interstate DC. A package being transported, for example, from Jacksonville, FL (DC 4) to Richmond, VA (DC 30) would travel on different trucks (operated by different firms) between each of the DCs along the way (DCs 7, 24, 23 and 17) and could be temporarily stored at each of the DCs (e.g., to wait for lower-cost off-peak travel periods).
This research was supported, in part, by the National Science Foundation under Grant CMS-0229720 (NSF/USDOT: Agent-based Coordination in Public Logistics Networks, PI: Michael G. Kay)
Michael G. Kay, Fitts Dept. of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC.