  Known for its dynamic utilization of destroy and repair operators, the
Adaptive Large Neighborhood Search (ALNS) seeks to unearth high-quality
solutions and has thus gained widespread acceptance as a meta-heuristic tool
for tackling complex Combinatorial Optimization Problems (COPs). However,
challenges arise when applying uniform parameters and acceptance criteria to
diverse instances of the same COP, resulting in inconsistent performance
outcomes. To address this inherent limitation, we propose the Adaptive Hybrid
Genetic Search and Large Neighborhood Search (AHGSLNS), a novel approach
designed to adapt ALNS parameters and acceptance criteria to the specific
nuances of distinct COP instances. Our evaluation focuses on the
Multi-Attribute Vehicle Routing Problem, a classical COP prevalent in
real-world semi-automated storage and retrieval robotics systems. Empirical
findings showcase that AHGSLNS not only competes effectively with ALNS under
varying parameters but also exhibits superior performance in terms of
convergence and stability. In alignment with our dedication to research
transparency, the implementation of the proposed approach will be made publicly
available.
