优化人机协同调度，实现油田巡检的低成本高可靠部署
Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities
- 构建考虑分级重要性的新型人机协同选址模型
- 人机比从1:3提升至1:10，成本显著下降且覆盖率达100%
- 大问题可在3分钟内获得近似最优解，适合实际系统应用
保障石油设施安全需平衡自主系统效率与人工判断在威胁升级中的作用，传统设施选址模型因假设资源同质而无法应对该挑战。本文提出人机协同调度设施选址问题（HRCD-FLP），一种带容量限制的选址变体，包含分级设施重要性、人机监督比例约束及最低利用率要求。我们评估了三种技术成熟度场景下的指挥中心选择。结果表明，从保守的人机比1:3过渡到未来全自动模式（1:10）可大幅降低运营成本，同时保持关键设施全覆盖。小规模问题中，精确算法在成本和求解时间上均占优；大规模问题下，所提启发式方法能在3分钟内获得可行解，最优性差距约14%。从系统视角看，优化人机协同规划是实现成本效益与任务可靠性兼顾的关键。
原文摘要 · Abstract (English)
Securing petroleum infrastructure requires balancing autonomous system efficiency with human judgment for threat escalation, a challenge unaddressed by classical facility location models assuming homogeneous resources. This paper formulates the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP), a capacitated facility location variant incorporating tiered infrastructure criticality, human-robot supervision ratio constraints, and minimum utilization requirements. We evaluate command center selection across three technology maturity scenarios. Results show transitioning from conservative (1:3 human-robot supervision) to future autonomous operations (1:10) yields significant cost reduction while maintaining complete critical infrastructure coverage. For small problems, exact methods dominate in both cost and computation time; for larger problems, the proposed heuristic achieves feasible solutions in under 3 minutes with approximately 14% optimality gap where comparison is possible. From systems perspective, our work demonstrate that optimized planning for human-robot teaming is key to achieve both cost-effective and mission-reliable deployments.
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