Approximation algorithms for a vehicle routing problem

The EMS vehicle patient transportation problem during a demand surge

“…Krumke et al [ 23 ] study a general vehicle dispatching problem with at most two requests (VDP2), which simply dispatches vehicles so that the total transportation cost is minimized. Note that unlike a k -customer VRP (e.g., Haimovich et al [ 24 ]), all vehicles in VDP2 are geographically dispersed and thus are not necessarily based at one depot.…”

A mathematical programming approach for dispatching and relocating EMS vehicles.

“…In the literature, Krumke et al [49] study the vehicle dispatching problem with at most two requests (VDP2), which simply dispatches vehicles to serve requests with each vehicle serving at most two requests, so that the total transportation cost is minimized. Note that unlike a k-customer VRP (e.g., Haimovich et al [36]), all vehicles in VDP-2 are geographically dispersed and thus are not necessarily based at one depot.…”

“…Note that unlike a k-customer VRP (e.g., Haimovich et al [36]), all vehicles in VDP-2 are geographically dispersed and thus are not necessarily based at one depot. Furthermore, Krumke et al [49] how that the VDP2 is NP-complete. In analyzing the computational complexity for the EMSVPTP, we demonstrate that essentially VDP2 is reducible to the EMSVPTP in polynomial time.…”

“…Below, we first formally introduce the VDP2 in [49], and then show that the vehicle dispatching problem with two requests and a common destination depot (VDP2CDD), a slight variation of VDP2, is NP-complete. Finally we establish the proof that the EMSVPTP is NP-complete through the reduction from VDP2CDD.…”

“…In Krumke et al [49], it is shown that the decision version of the VDP2 is NP-complete, thus VDP2 is NP-hard. Therefore, VDP2CDD is NP-hard.…”