Routing for Relief Efforts

TLDR

In disaster relief, vehicle routing that balances speed and fairness is critical, yet traditional cost‑minimizing models do not capture these priorities. This work initiates the development of new routing methodologies tailored to disaster scenarios. We analyze TSP and VRP under minmax and minavg objectives, derive worst‑case performance bounds, assess cost trade‑offs, and propose insertion‑ and local‑search‑based algorithms evaluated through computational experiments to identify instances where optimal solutions diverge from standard approaches.

Abstract

In the aftermath of a large disaster, the routing of vehicles carrying critical supplies can greatly impact the arrival times to those in need. Because it is critical that the deliveries are both fast and fair to those being served, it is not clear that the classic cost-minimizing routing problems properly reflect the relevant priorities in disaster relief. In this paper, we take the first steps toward developing new methodologies for these problems. We focus specifically on two alternative objective functions for the traveling salesman problem (TSP) and the vehicle routing problem (VRP): one that minimizes the maximum arrival time (minmax) and one that minimizes the average arrival time (minavg). To demonstrate the potential impact of using these new objective functions, we bound the worst-case performance of optimal TSP solutions with respect to these new variants and extend these bounds to include multiple vehicles and vehicle capacity. Similarly, we examine the potential increase in routing costs that results from using these alternate objectives. We present solution approaches for these two variants of the TSP and VRP, which are based on well-known insertion and local search techniques. These are used in a series of computational experiments to help identify the types of instances in which TSP and VRP solutions can be significantly different from optimal minmax and minavg solutions.