Open Access Articles

Development of a Scheduling Model for Container Shipping Lines in a Green Supply Chain to Minimize Costs, Considering Port Time Windows and Demand Uncertainty

Authors

  • Shahab Amani * Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.22105/raise.v2i2.51

Abstract

In recent years, the increase in Gross Domestic Product (GDP) and global trade has significantly expanded the role of freight transport, particularly maritime transport. Container trade has experienced notable growth, and shipping lines have become one of the most important container transport methods. Proper scheduling of these shipping lines requires precise planning, with factors such as port service availability playing a crucial role. The term “port time window” refers to specific timeframes during which a port can provide services to ships, significantly affecting shipping line schedules. Well-designed schedules not only affect fuel consumption but also contribute to reducing air pollution. However, uncertainty in various parameters can degrade the quality of scheduling outcomes. The present research proposes a scheduling model for container shipping lines within a green supply chain. It aims to minimize transportation costs, fuel consumption, and environmental pollution while considering port time windows and demand uncertainty. Given the complexity and constraints of the problem, it is classified as NP-hard. Small-scale instances are solved using GAMS software, while metaheuristic algorithms are applied to large-scale problems. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed for comparison, and results are analyzed regarding solution time and accuracy.

Keywords

Scheduling Container transport Shipping lines Green supply chain Uncertainty
References (25)
  1. [1] De Matos Sá, M., da Fonseca, F. X. C., Luís, A., & Castro, R. (2024). Optimising O&M scheduling in offshore wind farms considering weather forecast uncertainty and wake losses. Ocean engineering, 301, 117518. https://doi.org/10.1016/j.oceaneng.2024.117518

  2. [2] Ma, H., Liu, Z., Li, M., Wang, B., Si, Y., Yang, Y., & Mohamed, M. A. (2021). A two-stage optimal scheduling method for active distribution networks considering uncertainty risk. Energy reports, 7, 4633–4641. https://doi.org/10.1016/j.egyr.2021.07.023

  3. [3] Kumar, A., & Kumar, K. (2024). An uncertain sustainable supply chain network design for regulating greenhouse gas emission and supply chain cost. Cleaner logistics and supply chain, 10, 100142. https://doi.org/10.1016/j.clscn.2024.100142

  4. [4] Gharibi, K., & Abdollahzadeh, S. (2025). A mixed-integer linear programming approach for circular economy-led closed-loop supply chains in green reverse logistics network design under uncertainty. Journal of enterprise information management, 38(1), 1–31. https://doi.org/10.1108/JEIM-11-2020-0472

  5. [5] Sadana, U., Chenreddy, A., Delage, E., Forel, A., Frejinger, E., & Vidal, T. (2023). A survey of contextual optimization methods for decision making under uncertainty. https://doi.org/10.48550/arXiv.2306.10374

Article Metrics

How to Cite

Amani, S. . (2025). Development of a Scheduling Model for Container Shipping Lines in a Green Supply Chain to Minimize Costs, Considering Port Time Windows and Demand Uncertainty. Research Annals of Industrial and Systems Engineering, 2(2), 154-169. https://doi.org/10.22105/raise.v2i2.51

Similar Articles

1-10 of 24

You may also start an advanced similarity search for this article.