Reconstructing Transportation Cost Planning Theory: A Multi-Layered Framework Integrating Stepwise Functions, AI-Driven Dynamic Pricing, and Sustainable Autonomy

TL;DR

This paper develops a new transportation cost planning framework that integrates stepwise costs, AI-driven dynamic pricing, and autonomous vehicles to better address modern logistical challenges and sustainability.

Contribution

It introduces a comprehensive multi-layered theoretical model combining economic, operational, and environmental factors for transportation cost planning.

Findings

Transition from linear to stepwise fixed costs.

Importance of AI-driven dynamic pricing.

Role of autonomous electric vehicles in cost reduction.

Abstract

The theoretical landscape of transportation cost planning is shifting from deterministic linear models to dynamic, data-driven optimization. As supply chains face volatility, static 20th-century cost assumptions prove increasingly inadequate. Despite rapid technological advancements, a unified framework linking economic production theory with the operational realities of autonomous, sustainable logistics remains absent. Existing models fail to address non-linear stepwise costs and real-time stochastic variables introduced by market dynamics. This study reconstructs transportation cost planning theory by synthesizing Grand, Middle-Range, and Applied theories. It aims to integrate stepwise cost functions, AI-driven decision-making, and environmental externalities into a cohesive planning model. A systematic theoretical synthesis was conducted using 28 high-impact papers published primarily between 2018 and 2025, employing multi-layered analysis to reconstruct cost drivers. The study identifies three critical shifts: the transition from linear to stepwise fixed costs, the necessity of AI-driven dynamic pricing for revenue optimization, and the role of Autonomous Electric Vehicles (AEVs) in minimizing long-term marginal costs. A "Dynamic-Sustainable Cost Planning Theory" is proposed, arguing that cost efficiency now depends on algorithmic prediction and autonomous fleet utilization rather than simple distance minimization.