Machine Learning-Based Route-to-Market Optimization for Smart Retail Distribution Networks in Saudi Arabia

References

[1] A brief survey on learning based methods for vehicle routing problems (2023). Procedia Computer Science, 221, 773–780. https://doi.org/10.1016/j.procs.2023.08.050

[2] Bengio Y, Lodi A, Prouvost A (2021). Machine learning for combinatorial optimization: A methodological tour d’horizon. European Journal of Operational Research, 290(2), 405–421. https://doi.org/10.1016/j.ejor.2020.07.063

[3] Bogyrbayeva A, Meraliyev M, Mustakhov T, Dauletbayev B (2024). Machine learning to solve vehicle routing problems: A survey. IEEE Transactions on Intelligent Transportation Systems, 25(6), 4754–4772. https://doi.org/10.1109/TITS.2023.3334976

[4] Hottung A, Tierney K (2022). Neural large neighborhood search for routing problems. Artificial Intelligence. https://doi.org/10.1016/j.artint.2022.103786

[5] Kingdom of Saudi Arabia (n.d.). Saudi Vision 2030: Overview. https://www.vision2030.gov.sa/en/overview

[6] Kingdom of Saudi Arabia (n.d.). Vision 2030 Annual Report 2025. https://www.vision2030.gov.sa/media/ecdjfopq/vision2030_annual_report_2025_en.pdf

[7] Kool W, van Hoof H, Welling M (2019). Attention, learn to solve routing problems!. International Conference on Learning Representations; 2019. https://openreview.net/pdf?id=ByxBFsRqYm

[8] Kwon YD, Choo J, Kim B, Yoon I, Gwon Y, Min S (2020). POMO: Policy optimization with multiple optima for reinforcement learning. Advances in Neural Information Processing Systems 33; 2020. https://proceedings.neurips.cc/paper_files/paper/2020/hash/f231f2107df69eab0a3862d50018a9b2-Abstract.html

[9] Li J (2026). Learning-based optimization for vehicle/robot routing problems: A survey. Journal of Artificial Intelligence and Applications; 2026. https://www.sciltp.com/journals/jaia/articles/2605004025

[10] Ministry of Transport and Logistics Services (Saudi Arabia) (n.d.). About the ministry: Objectives and roles. https://mot.gov.sa/en/web/guest/about-motls

[11] Ministry of Transport and Logistics Services (Saudi Arabia) (n.d.). National Transport and Logistics Strategy. https://mot.gov.sa/en/ntls

[12] Nazari M, Oroojlooy A, Snyder LV, Takáč M (2018). Reinforcement learning for solving the vehicle routing problem. Advances in Neural Information Processing Systems 31; 2018. https://papers.neurips.cc/paper_files/paper/2018/hash/9fb4651c05b2ed70fba5afe0b039a550-Abstract.html

[13] Saudi Data and Artificial Intelligence Authority (SDAIA) (n.d.). Laws and regulations: Personal Data Protection Law and data policies. https://sdaia.gov.sa/en/SDAIA/about/Pages/RegulationsAndPolicies.aspx

[14] The vehicle routing problem in the last decade: Variants, taxonomy and metaheuristics (2023). Procedia Computer Science, 220, 398–404. https://doi.org/10.1016/j.procs.2023.03.051

[15] Zhou F, Lischka A, Kulcsár B, Wu J, Chehreghani MH, Laporte G (2025). Learning for routing: A guided review of recent developments and future directions. Transportation Research Part E, 202, 104278. https://doi.org/10.1016/j.tre.2025.104278

@article{1724747,
      author = {Mohamed Saad Hassan Ismail},
      title = {Machine Learning-Based Route-to-Market Optimization for Smart Retail Distribution Networks in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {721-734},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724747.pdf},
      abstract = {Retail distribution networks in Saudi Arabia must coordinate variable outlet demand, constrained transport capacity, retailer receiving windows and geographically diverse delivery conditions. This critical narrative review evaluates machine-learning-assisted route-to-market optimization in the context of Saudi Vision 2030 and the National Transport and Logistics Strategy. It synthesizes research on vehicle routing, neural combinatorial optimization, predictive demand modeling and learning-assisted local search, and proposes an integrated architecture connecting commercial data, predictive models, constraint-aware optimization and human-supervised dispatch. The review distinguishes operational feasibility from algorithmic benchmark performance and develops a framework for evaluating distance, on-time-in-full service, fleet utilization, cost, reliability and emissions. Two conceptual figures and comparison tables support the discussion; all numerical scenario values are explicitly illustrative rather than empirical findings. The evidence suggests that hybrid approaches are plausible candidates for deployment when prediction quality, governance, commercial service policies and optimization robustness are jointly addressed. Future research should prioritize Saudi-specific datasets, controlled evaluation, heterogeneous fleet constraints, multimodal interfaces and transparent reporting of uncertainty.},
      keywords = {machine learning; route-to-market; Saudi Arabia; Vision 2030; FMCG; vehicle routing; logistics platforms; demand forecasting; retail distribution},
      month = {November},
      doi = {https://doi.org/10.64388/ire1724747}
  }