University of Tehran Press Iranian Journal of Biosystem Engineering 2008-4803 57 1 2026 04 21 Identification and Analysis of Suitable Optimization Models for Designing the Supply Chain of Marine Cage Aquaculture under Uncertainty Identification and Analysis of Suitable Optimization Models for Designing the Supply Chain of Marine Cage Aquaculture under Uncertainty 87 105 106697 10.22059/ijbse.2026.408905.665634 FA Hamid Sinisaz-Shahshahani Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. 0009-0000-7278-6397 Mohammad Sharifi Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran 0000-0003-4594-4972 Asadollah Akram Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. 0000-0002-0835-0975 Majid Khanali Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran 0000-0001-6133-4862 Journal Article 2025 12 29   Marine cage aquaculture along the southern coasts of Iran (Bushehr, Hormozgan, Khuzestan, and Sistan and Baluchestan) has experienced an annual growth rate of 15–20% in recent years and constitutes a key component of the national strategy to increase total aquaculture production to over 1.8 million tons. Hormozgan serves as the primary hub for marine species production; Bushehr produces approximately 14,000 tons with more than 20 active farms; Khuzestan targets 5,000 tons; and Sistan and Baluchestan benefits from the substantial potential of the Makran coast and the Oman Sea. Despite climatic constraints, infrastructural limitations, and market volatility, the sector’s high regional capacity and governmental support have positioned it as a strategic pillar of sustainable fisheries development in Iran. This study develops a scenario-based multi-objective mixed-integer linear programming (MILP) model to design the aquaculture supply chain under uncertainty. The model simultaneously addresses economic (minimization of total supply chain costs), social (maximization of sustainable employment), and environmental (reduction of fuel consumption and emissions) objectives. Uncertainty is incorporated through three scenarios—optimistic, moderate, and pessimistic. The model is implemented in Python using the PuLP library, and three solution approaches are compared: the ε-constraint method, the weighted-sum method, and robust optimization. Findings indicate that the weighted-sum approach provides the most practical and balanced solution, activating only the strategic node of Chabahar. This configuration yields a total supply chain cost of approximately 7,418,500 million IRR, generates 390,000 sustainable jobs, and results in energy consumption of 830,000 GJ. In contrast, the ε-constraint and robust optimization methods fail to produce feasible solutions due to rigid constraints and high sensitivity to pessimistic scenarios, respectively. Accordingly, the weighted-sum method is recommended as a flexible and context-appropriate approach for southern Iran. However, the relatively high energy consumption underscores the need to increase the environmental weight (w₃=0.3) to enhance sustainability under uncertainty.   Marine cage aquaculture along the southern coasts of Iran (Bushehr, Hormozgan, Khuzestan, and Sistan and Baluchestan) has experienced an annual growth rate of 15–20% in recent years and constitutes a key component of the national strategy to increase total aquaculture production to over 1.8 million tons. Hormozgan serves as the primary hub for marine species production; Bushehr produces approximately 14,000 tons with more than 20 active farms; Khuzestan targets 5,000 tons; and Sistan and Baluchestan benefits from the substantial potential of the Makran coast and the Oman Sea. Despite climatic constraints, infrastructural limitations, and market volatility, the sector’s high regional capacity and governmental support have positioned it as a strategic pillar of sustainable fisheries development in Iran. This study develops a scenario-based multi-objective mixed-integer linear programming (MILP) model to design the aquaculture supply chain under uncertainty. The model simultaneously addresses economic (minimization of total supply chain costs), social (maximization of sustainable employment), and environmental (reduction of fuel consumption and emissions) objectives. Uncertainty is incorporated through three scenarios—optimistic, moderate, and pessimistic. The model is implemented in Python using the PuLP library, and three solution approaches are compared: the ε-constraint method, the weighted-sum method, and robust optimization. Findings indicate that the weighted-sum approach provides the most practical and balanced solution, activating only the strategic node of Chabahar. This configuration yields a total supply chain cost of approximately 7,418,500 million IRR, generates 390,000 sustainable jobs, and results in energy consumption of 830,000 GJ. In contrast, the ε-constraint and robust optimization methods fail to produce feasible solutions due to rigid constraints and high sensitivity to pessimistic scenarios, respectively. Accordingly, the weighted-sum method is recommended as a flexible and context-appropriate approach for southern Iran. However, the relatively high energy consumption underscores the need to increase the environmental weight (w₃=0.3) to enhance sustainability under uncertainty. https://ijbse.ut.ac.ir/article_106697_097146a006f98658f22a67efebaec73c.pdf