Computational logistics planning has emerged as a transformative paradigm in global supply chain and port management systems, driven by advancements in optimization algorithms, simulation models, and artificial intelligence. While these technologies have significantly improved operational efficiency, cost reduction, and environmental performance, they simultaneously introduce complex ethical challenges related to fairness, transparency, labor displacement, and environmental accountability. This research investigates the ethical dimensions embedded within computational logistics planning frameworks, with a particular focus on balancing performance optimization with social responsibility.

The study synthesizes insights from established literature on intermodal freight systems, port energy efficiency, container terminal optimization, and AI-driven logistics decision-making. Foundational works on intermodal system simulation (Crainic et al., 2018), container terminal scheduling (Li, 2015), and fleet deployment strategies (Wang & Meng, 2017) are integrated with sustainability-oriented research on emissions reduction and energy efficiency (Dulebenets et al., 2017; Iris & Lam, 2019). Additionally, emerging ethical perspectives on AI-based supply chain optimization emphasize fairness and accountability as critical dimensions of computational decision systems (Raikar et al., 2026).

Methodologically, this paper adopts a conceptual synthesis approach, integrating operational research models with ethical theory frameworks such as distributive justice and algorithmic accountability. The findings highlight that while computational optimization significantly enhances throughput and environmental performance, it often introduces hidden biases in resource allocation, disproportionately affects smaller operators, and may prioritize efficiency over equity.

The study further identifies a critical gap in existing literature: the lack of integrated models that simultaneously optimize logistics performance and enforce ethical constraints. This gap becomes increasingly relevant in the context of AI-driven automation, where decision-making systems operate with minimal human intervention. The paper argues for the development of hybrid ethical-optimization frameworks that embed fairness constraints directly into computational logistics models.

Ultimately, this research contributes to the growing discourse on responsible logistics digitalization by proposing a conceptual alignment between operational efficiency and socio-ethical governance, ensuring that technological advancements do not compromise equity or sustainability objectives.

Computational logistics, ethical optimization, supply chain fairness, container terminals

T. G. Crainic, G. Perboli, and M. Rosano, “Simulation of intermodal freight transportation systems: A taxonomy,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 401–408, Oct. 2018.

M. A. Dulebenets, R. Moses, E. E. Ozguven, and A. Vanli, “Minimizing carbon dioxide emissions due to container handling at marine container terminals via hybrid evolutionary algorithms,” IEEE Access, vol. 5, pp. 8131–8147, 2017.

Ç. Iris and J. S. L. Lam, “A review of energy efficiency in ports: Operational strategies, technologies and energy management systems,” Renew. Sustain. Energy Rev., vol. 112, pp. 170–182, Sep. 2019.

K. A. Kuzmicz and E. Pesch, “Approaches to empty container repositioning problems in the context of eurasian intermodal transportation,” Omega, vol. 85, pp. 194–213, Jun. 2019.

B. Li, “Container terminal logistics scheduling and decision-making within the conceptual framework of computational thinking,” in Proc. 54th IEEE Conf. Decis. Control (CDC), Osaka, Japan, Dec. 2015, pp. 330–337.

C.-Y. Lee and D.-P. Song, “Ocean container transport in global supply chains: Overview and research opportunities,” Transp. Res. B, Methodol., vol. 95, pp. 442–474, Jan. 2017.

Raikar, T., Ezeugboaja, F., Bussa, S., Upadhyay, H., &Kalaru, P. (2026). Ethics of AI-based supply chain optimization: a better balance between efficiency and fairness . Future Technology, 5(2), 281–296. Retrieved from https://fupubco.com/futech/article/view/831

S. Wang, Q. Meng, and C.-Y. Lee, “Liner container assignment model with transit-time-sensitive container shipment demand and its applications,” Transp. Res. B, Methodol., vol. 90, pp. 135–155, Aug. 2016.

S. Wang and Q. Meng, “Container liner fleet deployment: A systematic overview,” Transp. Res. C, Emerg. Technol., vol. 77, pp. 389–404, Apr. 2017.

P. Wang, J. Mileski, and Q. Zeng, “Toward a taxonomy of container terminals’ practices and performance: A contingency and configuration study,” Transp. Res. A, Policy Pract., vol. 121, pp. 92–107, Mar. 2019.