  Ant Colony algorithm has been applied to various optimization problems,
however most of the previous work on scaling and parallelism focuses on
Travelling Salesman Problems (TSPs). Although, useful for benchmarks and new
idea comparison, the algorithmic dynamics does not always transfer to complex
real-life problems, where additional meta-data is required during solution
construction. This paper looks at real-life outbound supply chain problem using
Ant Colony Optimization (ACO) and its scaling dynamics with two parallel ACO
architectures - Independent Ant Colonies (IAC) and Parallel Ants (PA). Results
showed that PA was able to reach a higher solution quality in fewer iterations
as the number of parallel instances increased. Furthermore, speed performance
was measured across three different hardware solutions - 16 core CPU, 68 core
Xeon Phi and up to 4 Geforce GPUs. State of the art, ACO vectorization
techniques such as SS-Roulette were implemented using C++ and CUDA. Although
excellent for TSP, it was concluded that for the given supply chain problem
GPUs are not suitable due to meta-data access footprint required. Furthermore,
compared to their sequential counterpart, vectorized CPU AVX2 implementation
achieved 25.4x speedup on CPU while Xeon Phi with its AVX512 instruction set
reached 148x on PA with Vectorized (PAwV). PAwV is therefore able to scale at
least up to 1024 parallel instances on the supply chain network problem solved.
