Development and Application of Algorithms in Logistics

Purpose: To examine how algorithmic thinking reshapes freight operations beyond dispatcher intuition by synthesizing evidence from twelve peer-reviewed empirical studies (2019–2025) alongside design logics from the OnLogix and Excel Logistics platforms. The focus is on routing, inventory, and risk workflows where efficiency, compliance, and organizational trust intersect. Methodology: A PRISMA-inspired, managerially focused search protocol identified studies on time-dependent vehicle routing, truck–drone collaboration, adaptive inventory control, and risk-aware accounts-receivable scoring. Each paper was coded against nine performance dimensions to enable structured cross-study comparison while avoiding premature meta-analysis given divergent samples and settings. Findings: Hybrid metaheuristics consistently outperform classic tabu or GRASP once stochastic travel times and regulatory constraints are modeled. Machine-learning layers raise prediction accuracy but introduce opacity. Interpreted through a mid-size 3PL lens, three actionable themes emerge: (1) embed compliance and financial-risk logic natively in routing engines; (2) prioritize deployment velocity over marginal optimality where data-science capacity is limited; and (3) treat explainability as essential for shared dashboards used by drivers, dispatchers, and auditors. Unique Contribution to Theory, Policy and Practice: The study reframes “algorithmic logistics” as a socio-technical system, integrating efficiency with governance, explainability, and data stewardship (theory). It signals to policymakers that auditability and transparency should complement classic efficiency metrics in regulatory guidance (policy). It offers practitioners an adoption playbook: build compliance-aware routing from the outset, value speed-to-production, and require interpretable ML in operator-facing tools (practice). It also maps a mixed-methods research agenda coupling large-scale simulation with ethnographic observation and points to future work on reinforcement-learning price engines as carbon markets tighten around fleets.