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运筹学学报 >

2026 , Vol. 30 >Issue 3: 204 - 218

DOI: https://doi.org/10.15960/j.cnki.issn.1007-6093.2026.03.012

运筹与管理前沿：模型、算法及其应用专辑

基于目标鲁棒性优化的两阶段无人机设施选址与调度

  • 过燕晶 ,
  • 高盼 ,
  • 吴中明
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  • 1. 福州大学 数学与统计学院, 福建 福州 350108;
    2. 南京大学工程管理学院, 江苏南京 210093;
    3. 南京信息工程大学管理工程学院, 江苏南京 210044

收稿日期: 2025-12-11

  网络出版日期: 2026-09-12

基金资助

国家自然科学基金(No.12471291),江苏省基础研究计划自然科学基金(No.BK20241899)

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Two-stage drone facility location and scheduling based on robust satisficing

  • GUO Yanjing ,
  • GAO Pan ,
  • WU Zhongming
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  • 1. School of Mathematics and Statistics, Fuzhou University, Fuzhou 350108, Fujian, China;
    2. School of Management and Engineering, Nanjing University, Nanjing 210093, Jiangsu, China;
    3. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Received date: 2025-12-11

  Online published: 2026-09-12

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摘要

本文聚焦不确定需求下的无人机设施选址和应急物资调度问题,提出一种基于目标鲁棒优化的两阶段选址-调度模型。其中第一阶段决策包括设施选址、无人机分配以及应急物资的储备;第二阶段则根据实际需求实现物资调度。模型采用Wasserstein距离刻画需求分布的不确定性,结合对偶理论和仿射决策规则将问题重构为自适应鲁棒优化模型,并运用求解器对模型进行求解。数值结果表明,相较于随机规划和分布鲁棒优化模型,所提模型在设施选址与库存预置决策方面更具鲁棒性,能够有效降低灾后的应急运输成本。

关键词： 无人机运输; 设施选址; 调度; 分布鲁棒优化; 目标鲁棒性优化

本文引用格式

过燕晶 , 高盼 , 吴中明 . 基于目标鲁棒性优化的两阶段无人机设施选址与调度[J]. 运筹学学报, 2026 , 30(3) : 204 -218 . DOI: 10.15960/j.cnki.issn.1007-6093.2026.03.012

Abstract

This paper focuses on the drone facility location and scheduling problem under uncertain demand, and proposes a two-stage location-scheduling model based on robust satisficing. The first-stage decisions cover facility siting, drone allocation, and emergency supply stockpiling. The second-stage operations implement supply dispatching per actual demand. The model employs the Wasserstein distance to characterize demand distribution uncertainty, reconstructs the problem into a solvable adaptive robust optimization model via duality theory and affine decision rules, and solves it using a solver. Numerical results demonstrate that, compared with stochastic programming and distributionally robust optimization models, the proposed model offers stronger robustness in facility location and inventory pre-positioning, and effectively cuts post-disaster emergency transportation costs.

Key words： drone transportation; facility location; scheduling; distributionally robust optimization; robust satisficing

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