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

2020-01-22 · Ivars Dzalbs, Tatiana Kalganova

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.

📄 PDF Abstract BibTeX arXiv:2001.08102

Code (0)

등록된 구현이 없습니다.

Tasks

CPU

Methods 이 논문이 사용한 방법론

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Pheromone-Focused Ant Colony Optimization algorithm for path planning

2026-01-12 · Yi Liu, Hongda Zhang, Zhongxue Gan, Yuning Chen 외 arxiv

Ant Colony Optimization (ACO) is a prominent swarm intelligence algorithm extensively applied to path planning. However, traditional ACO methods often exhibit shortcomings, such as blind search behavior and slow converge…

An Ant Colony Optimization Algorithm for Partitioning Graphs with Supply and Demand

2015-03-03 · Raka Jovanovic, Milan Tuba, Stefan Voss

In this paper we focus on finding high quality solutions for the problem of maximum partitioning of graphs with supply and demand (MPGSD). There is a growing interest for the MPGSD due to its close connection to problems…

Accelerating the ANT Colony Optimization By Smart ANTs, Using Genetic Operator

2014-11-11 · Hassan Ismkhan

This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then proposes a new model…

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

2024-11-13 · Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib, Mahathir Mohammad Bappy

Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are…

Anomaly Detection Demand Forecasting Product Categorization Production Forecasting+2

AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services

2025-04-28 · Aspen Hopkins, Sarah H. Cen, Andrew Ilyas, Isabella Struckman 외

The widespread adoption of AI in recent years has led to the emergence of AI supply chains: complex networks of AI actors contributing models, datasets, and more to the development of AI products and services. AI supply …