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Samuel Darwisman

Publications and source records attributed to Samuel Darwisman.

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A General Theory of Piping Transportation: Unifying System Dynamics for Resilience and Sustainable Development

The science of pipeline transport is currently governed by a collection of fragmented, discipline-specific theories that are inadequate for addressing the systemic challenges of 21st-century infrastructure. This paper introduces and formalizes a new, unified theory: the General Theory of Piping Transportation (GTPT), formulated by Darwisman. The GTPT posits that a pipeline system is a complex socio-technical entity whose state and long-term viability are determined by the fully coupled interaction of three interdependent domains: Physical Dynamics ({\Phi}), Life-Cycle Dynamics ({\Lambda}), and Socio-Economic Dynamics ({\Sigma}). This paper presents the core postulates of the GTPT, which are derived from a systematic synthesis of the fragmented existing literature. The prescriptive power of the theory is illustrated by contrasting the strategic outcomes derived from the GTPT against those from classical theories. By defining resilience as the primary design objective and operationalizing the UN Sustainable Development Goals (SDGs), the GTPT provides a new theoretical foundation for the design, management, and governance of infrastructure across all critical sectors.

econ.TH

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

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.

econ.TH