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

Accelerating supply chains with Ant Colony Optimization across range of hardware solutions