Vehicle Scheduling Problem
This addresses scheduling challenges for integrating Unmanned Aerial Vehicles (UAVs) into airspace, presenting a novel framework but with incremental algorithmic contributions.
The authors tackled the Vehicle Scheduling Problem (VSP) to minimize tardy vehicles in transportation networks under constraints like safety distances and deadlines, proving it NP-hard and developing a heuristic algorithm with comparisons to optimal solutions in small cases.
We define a new problem called the Vehicle Scheduling Problem (VSP). The goal is to minimize an objective function, such as the number of tardy vehicles over a transportation network subject to maintaining safety distances, meeting hard deadlines, and maintaining speeds on each link between the allowed minimums and maximums. We prove VSP is an NP-hard problem for multiple objective functions that are commonly used in the context of job shop scheduling. With the number of tardy vehicles as the objective function, we formulate VSP in terms of a Mixed Integer Linear Programming (MIP) and design a heuristic algorithm. We analyze the complexity of our algorithm and compare the quality of the solutions to the optimal solution for the MIP formulation in the small cases. Our main motivation for defining VSP is the upcoming integration of Unmanned Aerial Vehicles (UAVs) into the airspace for which this novel scheduling framework is of paramount importance.