不确定需求下无人机任务分配的两阶段鲁棒优化方法

王巍; 解慧; 魏忠诚; 赵继军; 彭力

doi:10.12263/DZXB.20240444

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不确定需求下无人机任务分配的两阶段鲁棒优化方法

学术论文 | 更新时间：2026-05-07

- 不确定需求下无人机任务分配的两阶段鲁棒优化方法
- Two-Stage Robust Optimization Method for UAV Task Assignment Under Uncertain Demand
- 电子学报 2024年52卷第10期页码：3552-3561
- 作者机构：
  
  1.河北工程大学信息与电气工程学院，河北邯郸 056038
  2.河北省安防信息感知与处理重点实验室，河北邯郸 056038
  3.西南石油大学电气信息学院，四川成都 610500
  4.江南大学物联网工程学院，江苏无锡 214122
- 作者简介：
  
  [ "王巍男，1983年11月出生于河北省邯郸市.现为西南石油大学电气信息学院教授.主要研究方向为无人机、物联网、优化理论和推荐系统.在国内外发表学术论文40余篇.E-mail: wwangcse@163.com" ]
  [ "解慧女，1999年6月出生于山西省吕梁市.河北工程大学信息与电气工程学院研究生，主要研究方向为优化理论，无人机任务分配.E-mail: jiehui0618@163.com" ]
  [ "魏忠诚男，1987年9月出生于河南省商丘市.现为河北工程大学信息与电气工程学院副教授、硕士生导师.主要研究方向为人工智能与物联网.在国内外发表学术论文50余篇.中国电子学会会员编号：E190159484M.E-mail: weizhongcheng@hebeu.edu.cn" ]
  [ "赵继军男，1970年9月出生在河北省邯郸市.现为河北工程大学信息与电气工程学院教授、博士生导师.主要研究方向为光通信、物联网、城市公共安全. 在国内外发表学术论文150余篇.中国电子学会会员编号：E190131457M.E-mail: zjijun@hebeu.edu.cn" ]
  [ "彭力男，1967年3月出生在河北省唐山市.现为江南大学物联网工程学院教授，博士生导师.主要研究方向为计算机仿真、智能控制和视觉无线传感网络.在国内外发表学术论文200余篇.E-mail: penglimail2002@163.com" ]
- 基金信息：
  
  国家重点研发计划(2018YFF0301004);国家自然科学基金(61802107);河北省高等学校科学技术研究项目(ZD2020171);江苏省博士后科研资助计划项目(1601085C)
- DOI：10.12263/DZXB.20240444
  中图分类号： TP18;V279
- 收稿：2024-05-14，
  
  修回：2024-09-09，
  
  纸质出版：2024-10-25
- 稿件说明：
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王巍, 解慧, 魏忠诚, 等. 不确定需求下无人机任务分配的两阶段鲁棒优化方法[J]. 电子学报, 2024, 52(10): 3552-3561.

WANG Wei, XIE Hui, WEI Zhong-cheng, et al. Two-Stage Robust Optimization Method for UAV Task Assignment Under Uncertain Demand[J]. Acta Electronica Sinica, 2024, 52(10): 3552-3561.
王巍, 解慧, 魏忠诚, 等. 不确定需求下无人机任务分配的两阶段鲁棒优化方法[J]. 电子学报, 2024, 52(10): 3552-3561. DOI：10.12263/DZXB.20240444

WANG Wei, XIE Hui, WEI Zhong-cheng, et al. Two-Stage Robust Optimization Method for UAV Task Assignment Under Uncertain Demand[J]. Acta Electronica Sinica, 2024, 52(10): 3552-3561. DOI：10.12263/DZXB.20240444

摘要

灾害场景下依托无人机配送资源应用前景广阔，但应急场景环境复杂多变，各类突发事件在时空上的不确定性会导致目标点对资源需求评估的不准确，进而影响到资源配送中无人机任务分配方案.针对此问题，在无人机任务分配模型中引入两阶段鲁棒优化方法.模型通过将无人机分配和任务分配相结合，充分利用无人机集群资源，实现需求变化最大化时的任务分配成本最低.本文对受伤人数等级与资源需求变化关系建模，将资源需求划分为3 个等级，实现了任务分配总成本变化的精确化表达，并采用列和约束生成（Column-and-Constraint Generation，C&CG）算法实现了资源需求不确定条件下的无人机任务分配.最后设计了3 种类型的实验，仿真结果验证了算法的有效性和优越性，相比确定性模型，该算法在应对需求变化时展现出更好的鲁棒性.

Abstract

In disaster scenarios

the application of UAV (Unmanned Aerial Vehicle) for resource delivery holds considerable promise. However

the complexity and volatility of emergency environments

along with the spatial and temporal uncertainties associated with various unexpected events

can lead to inaccuracies in assessing resource demands at target points

which in turn may affect the UAV task allocation strategies in resource distribution. To address this issue

a two-stage robust optimization approach is introduced into the UAV task assignmet model. By integrating UAV assignment with task allocation

the model leverages the resources of the UAV fleet to minimize task assignment costs under maximum demand variability. This paper models the relationship between injury severity levels and resource demand variations

categorizing resource demand into three levels to achieve an accurate representation of total task allocation cost variations. The C&CG (Column-and-Constraint Generation) algorithm is used to address UAV task assignment under uncertain resource demand conditions. Finally

three types of experiments were designed and the simulation results validated the effectiveness and superiority of the algorithm. Compared to the deterministic model

this algorithm showed greater robustness in handling demand variation.

关键词

Keywords

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