Multi-Objective Dynamic Scheduling for Heterogeneous Emergency Fleets with Breakdown-Resilient Rescheduling

In post-disaster relief operations, emergency fleets typically consist of vehicles with varying load capacities, travel speeds, and operating costs. These heterogeneous vehicles are prone to unexpected breakdowns during delivery, which can severely disrupt supply chains and delay urgent aid. Existing scheduling approaches, however, rarely account for fleet heterogeneity, real-time breakdowns, and the trade-off between delivery speed and cost within a unified framework. This paper addresses this gap by formulating the dynamic scheduling of heterogeneous emergency fleets as a two-stage mixed-integer programming model, where total transportation time and cost are simultaneously minimized. The key algorithmic contribution is a fuzzy robust adaptive multi-objective hybrid algorithm (FR-AMOHA) with three interconnected design components. First, a fuzzy evaluation-based pre-matching strategy uses entropy-weighted multi-criteria assessment to generate high-quality initial solutions. Second, a failure-resilient rescheduling module freezes system state upon breakdown detection and selects recovery plans via multi-dimensional resilience scoring to prevent cascading failures. Third, a Pareto-guided adaptive neighborhood search dynamically adjusts operator selection to balance time and cost optimization. Tests on 40 real-world instances with 50 to 1000 demand nodes show that FR-AMOHA achieves optimal inverted generational distance values on 17 out of 40 instances, improves hypervolume by 15% to 35% on average compared with other metaheuristics, and keeps computation times between 40 and 250 s, which is within acceptable limits for emergency decision-making. FR-AMOHA outperforms Gurobi and six leading metaheuristics in solution quality with comparable computational cost.