Drone-Delivery Network for Opioid Overdose: Nonlinear Integer Queueing-Optimization Models and Methods

• 成果类型: Article
• 署名作者: Lejeune, Miguel A.; Ma, Wenbo
• 署名单位: George Washington University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0489
• 发表日期: 2025
• 关键词: location problem; server location; PROGRAMS; allocation; facility; queues

摘要

We propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naloxone in response to opioid overdoses. The network is represented as a collection of M/G/K / G / K queueing systems in which the capacity K of each system is a decision variable, and the service time is modeled as a decision -dependent random variable. The model is a queuing -based optimization problem which locates fixed (drone bases) and mobile (drones) servers and determines the drone dispatching decisions and takes the form of a nonlinear integer problem intractable in its original form. We develop an efficient reformulation and algorithmic framework. Our approach reformulates the multiple nonlinearities (fractional, polynomial, exponential, factorial terms) to give a mixed -integer linear programming (MILP) formulation. We demonstrate its generalizability and show that the problem of minimizing the average response time of a collection of M/G/K / G / K queueing systems with unknown capacity K is always MILP-representable. We design an outer approximation branch -and -cut algorithmic framework that is computationally efficient and scales well. The analysis based on real -life data reveals that drones can in Virginia Beach: (1) decrease the response time by 82%, (2) increase the survival chance by more than 273%, (3) save up to 33 additional lives per year, and (4) provide annually up to 279 additional quality -adjusted life years.