Optimized Human-Robot Co-Dispatch Planning for Petro-Site Surveillance under Varying Criticalities
Nur Ahmad Khatim, Mansur Arief
TL;DR
The paper tackles securing critical petroleum infrastructure by introducing the Human-Robot Co-Dispatch Facility Location Problem (HRCD-FLP), which integrates tiered asset criticality, service-level agreements, and human-in-the-loop supervision into a multi-level capacitated facility location framework. It presents a formal MILP with decision variables for facility placement, level selection, demand assignment, and resource deployment, along with comprehensive constraints that enforce coverage, SLA compliance, and supervision ratios. To solve the NP-hard problem, the authors propose a two-stage approach: an exact branch-and-bound method for small instances and a scalable two-stage heuristic (greedy constructive then Best Improvement local search) for larger problems. Numerical experiments on small and large testbeds show exact methods excel at small scales, while the heuristic achieves feasible solutions within ~3 minutes for large-scale instances with up to ~14% optimality gap, enabling practical deployment planning. The study demonstrates that relaxing supervision ratios (e.g., from 1:3 to 1:10) can consolidate facilities and yield substantial cost savings, highlighting the value of integrating human-in-the-loop constraints into strategic infrastructure planning for secure, cost-effective, and reliable operations.
Abstract
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.