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<p>Strategic maritime chokepoints are critical transportation-security vulnerabilities because local disruptions can propagate through shipping routes, port systems, inventories, delivery reliability, freight costs, insurance conditions, emissions, and downstream supply chains. Existing studies have advanced knowledge on ship routing, maritime disruption, port resilience, and chokepoint vulnerability; however, less attention has been given to the joint optimization of logistics reconfiguration decisions when a chokepoint becomes insecure, capacity-constrained, or unavailable. This paper introduces the Chokepoint-Aware Maritime Logistics Reconfiguration Problem (CAMLRP), a scenario-conditioned decision-support problem for maritime logistics under security-related chokepoint disruption. The framework starts from a nominal pre-disruption logistics plan and determines how cargo flows should be reassigned across alternative maritime routes, substitute ports, multimodal options, and emergency inventory while controlling security-risk exposure and service deterioration. A route-based mixed-integer linear programming formulation is developed to minimize scenario-weighted transportation, route activation, nominal-plan deviation, delay, emergency-inventory, shortage, emission, and chokepoint-risk costs. The model incorporates scenario-dependent route availability, route and port capacities, cargo-flow splitting, deviations from nominal allocations, and explicit risk-budget constraints. Computational experiments on six synthetic benchmark classes, ranging from 10 to 150 cargo demands, show that the formulation produces interpretable indicators on rerouting intensity, risk exposure, shortage, emissions, and computational effort. The computational study further includes a Hormuz case-based stress test calibrated with recent public estimates from the early phase of the disruption. The analysis compares a pre-disruption baseline with early-disruption conditions, evaluates the proposed model against a passive nominal-policy benchmark, and examines the robustness of the results through sensitivity checks. The results show that risk-aware resilience is achieved by actively reconfiguring logistics flows rather than by preserving the nominal plan.</p>

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Chokepoint-aware maritime logistics reconfiguration under security disruptions: a scenario–based mixed–integer optimization framework

  • 期刊论文

  • 发表日期：

    2026年6月19日

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  • Rokaya Lassoued

摘要

Strategic maritime chokepoints are critical transportation-security vulnerabilities because local disruptions can propagate through shipping routes, port systems, inventories, delivery reliability, freight costs, insurance conditions, emissions, and downstream supply chains. Existing studies have advanced knowledge on ship routing, maritime disruption, port resilience, and chokepoint vulnerability; however, less attention has been given to the joint optimization of logistics reconfiguration decisions when a chokepoint becomes insecure, capacity-constrained, or unavailable. This paper introduces the Chokepoint-Aware Maritime Logistics Reconfiguration Problem (CAMLRP), a scenario-conditioned decision-support problem for maritime logistics under security-related chokepoint disruption. The framework starts from a nominal pre-disruption logistics plan and determines how cargo flows should be reassigned across alternative maritime routes, substitute ports, multimodal options, and emergency inventory while controlling security-risk exposure and service deterioration. A route-based mixed-integer linear programming formulation is developed to minimize scenario-weighted transportation, route activation, nominal-plan deviation, delay, emergency-inventory, shortage, emission, and chokepoint-risk costs. The model incorporates scenario-dependent route availability, route and port capacities, cargo-flow splitting, deviations from nominal allocations, and explicit risk-budget constraints. Computational experiments on six synthetic benchmark classes, ranging from 10 to 150 cargo demands, show that the formulation produces interpretable indicators on rerouting intensity, risk exposure, shortage, emissions, and computational effort. The computational study further includes a Hormuz case-based stress test calibrated with recent public estimates from the early phase of the disruption. The analysis compares a pre-disruption baseline with early-disruption conditions, evaluates the proposed model against a passive nominal-policy benchmark, and examines the robustness of the results through sensitivity checks. The results show that risk-aware resilience is achieved by actively reconfiguring logistics flows rather than by preserving the nominal plan.