Dedicated Transportation Subnetworks: Design, Analysis, and Insights

Abstract

A dedicated subnetwork (DSN) refers to a subset of lanes, with associated loads, in a shipper's transportation network, for which resources—trucks, drivers, and other equipment—are exclusively assigned to accomplish shipping requirements. The resources assigned to a DSN are not shared with the rest of the shipper's network. Thus, a DSN is an autonomously operated subnetwork and, hence, can be subcontracted. We address a novel problem of extracting a DSN for outsourcing to one or more subcontractors, with the objective of maximizing the shipper's savings. In their pure form, the defining conditions of a DSN are often too restrictive to enable the extraction of a sizable subnetwork. We consider two notions—deadheading and lane‐sharing—that aid in improving the size of the DSN. We show that all the optimization problems involved are both strongly NP‐hard and APX‐hard, and demonstrate several polynomially solvable special cases arising from topological properties of the network and parametric relationships. Next, we develop a network‐flow‐based heuristic that provides near‐optimal solutions to practical instances in reasonable time. Finally, using a test bed based on data obtained from a national 3PL company, we demonstrate the substantial monetary impact of subcontracting a DSN and offer useful managerial insights.

Crowdsourcing Last Mile Delivery: Strategic Implications and Future Research Directions

“…Additionally, CSL reduces dependence on the resource‐intensive and complex fleet sizing problem (Rajapakshe et al. ), while increasing responsiveness in the supply chain (Gligor et al. ).…”

“…Demand was generated during an average workday from 0700 to 1600 hr; the run length was set to 30 days, allowing for analysis of a standard contract length for the assignment of a dedicated fleet of about two to three weeks (Rajapakshe et al. ). This run length also provided enough data to discard the warm‐up period, which was set to 2.5 days, the time during which the dependent variables’ transient distributions converged into steady‐state behavior (Law and Kelton ).…”

“…Trial runs were performed, and the number of runs per scenario was calculated using the resulting means and standard deviations with a desired 5% relative-precision level, resulting in a need for 25 runs for each of the 30 scenarios. Demand was generated during an average workday from 0700 to 1600 hr; the run length was set to 30 days, allowing for analysis of a standard contract length for the assignment of a dedicated fleet of about two to three weeks (Rajapakshe et al 2014). This run length also provided enough data to discard the warm-up period, which was set to 2.5 days, the time during which the dependent variables' transient distributions converged into steady-state behavior (Law and Kelton 2000).…”

“…By integrating vehicle supply uncertainty into a fleet management decision, the simulation highlights the importance of effectiveness outcomes, such as on-time delivery rates and TD. Additionally, CSL reduces dependence on the resourceintensive and complex fleet sizing problem (Rajapakshe et al 2014), while increasing responsiveness in the supply chain (Gligor et al 2015).…”

An Integrated Logistic Model for Predictable Disasters

“…, Rajapakshe , et al. , ). However, because of the inherent differences between the supply chains in commercial environment and those in disaster response environment, these models are not directly applicable in our setting.…”