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<p>Against the backdrop of rising global supply chain volatility, logistics planning in high-risk regions demands robust optimization tools. This paper introduces a novel hybrid memetic computing framework for the Threat-Aware Container Truck Routing Problem (CTRP). The framework integrates three synergistic components: (i) a flocking-inspired mechanism that maintains population diversity through separation, alignment, and cohesion behaviors for global exploration; (ii) an echolocation-inspired frequency-modulated search that dynamically adjusts step sizes for local exploitation; and (iii) a threat-aware evasion operator that functions as a domain-knowledge meme, proactively repelling solutions from hazardous zones via distance-weighted velocity adjustment. Unlike penalty-based methods that evaluate risk after solution generation, this operator operates ex ante, fundamentally altering the search trajectory toward safer regions of the solution space. The multi-objective CTRP model incorporates static spatial threats—including environmental hazards such as flooding and drought-prone areas alongside security risks—with operational cost, distance, and threat exposure as competing objectives. The framework's effectiveness is validated through extensive experiments on modified CVRP benchmarks, large-scale Set X (100–1000 customers) and ultra-large Set XL (1,327–10,000 customers) instances, and a real-world East African Community case study with 16 threat zones. Comparative analysis against five established metaheuristics (ALNS, HGA, HADAD, Hybrid Cell-Wave, MA-PSO) and the commercial solver Gurobi 11.0 demonstrates the framework's superiority across all scales. Statistical validation over 30 independent runs confirms robustness. The proposed framework establishes a validated algorithmic foundation for proactive threat-aware logistics, extensible to dynamic and probabilistic threat environments.</p>

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A hybrid memetic framework with threat-aware evasion for container truck routing in high-risk environments

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Yves Ndikuriyo,
  • Yinggui Zhang,
  • Dung Davou Fom

摘要

Against the backdrop of rising global supply chain volatility, logistics planning in high-risk regions demands robust optimization tools. This paper introduces a novel hybrid memetic computing framework for the Threat-Aware Container Truck Routing Problem (CTRP). The framework integrates three synergistic components: (i) a flocking-inspired mechanism that maintains population diversity through separation, alignment, and cohesion behaviors for global exploration; (ii) an echolocation-inspired frequency-modulated search that dynamically adjusts step sizes for local exploitation; and (iii) a threat-aware evasion operator that functions as a domain-knowledge meme, proactively repelling solutions from hazardous zones via distance-weighted velocity adjustment. Unlike penalty-based methods that evaluate risk after solution generation, this operator operates ex ante, fundamentally altering the search trajectory toward safer regions of the solution space. The multi-objective CTRP model incorporates static spatial threats—including environmental hazards such as flooding and drought-prone areas alongside security risks—with operational cost, distance, and threat exposure as competing objectives. The framework's effectiveness is validated through extensive experiments on modified CVRP benchmarks, large-scale Set X (100–1000 customers) and ultra-large Set XL (1,327–10,000 customers) instances, and a real-world East African Community case study with 16 threat zones. Comparative analysis against five established metaheuristics (ALNS, HGA, HADAD, Hybrid Cell-Wave, MA-PSO) and the commercial solver Gurobi 11.0 demonstrates the framework's superiority across all scales. Statistical validation over 30 independent runs confirms robustness. The proposed framework establishes a validated algorithmic foundation for proactive threat-aware logistics, extensible to dynamic and probabilistic threat environments.