A Biobjective Stochastic Model for Intermodal Supply Chains: Application to the Corn and Soybean Flows.

Area of Science:

• Supply Chain Management
• Operations Research
• Agricultural Economics

Background:

• Recent global disruptions (geopolitical, economic, health, weather) underscore the need for resilient supply chain networks (SCNs).
• Effective planning and management of SCNs are crucial at all decision-making levels to mitigate impacts on businesses and populations.
• Intermodal transportation and distribution networks for agricultural commodities like soybean and corn face significant demand and cost uncertainties.

Purpose of the Study:

• To analyze and optimize soybean and corn supply chain networks (SCNs) across Europe and North Africa.
• To incorporate demand and cost uncertainties into SCN design using stochastic modeling.
• To redefine optimization goals considering expected cost (EC) and conditional value at risk (CVAR), alongside CO2 emissions.

Main Methods:

• Developed a deterministic model as a baseline for SCN establishment.
• Introduced uncertainty through stochastic modeling, adapting optimization objectives.
• Employed a bi-objective optimization model to balance economic costs and environmental emissions (CO2).

Main Results:

• Identified the strategic importance of the port of Itaqui on the supply side.
• Highlighted the port of Sines as a key distribution hub (transshipment point) for efficient SCN design.
• Demonstrated the advantageous role of the port of Sines in creating efficient intermodal SCNs for soybean and corn distribution.

Conclusions:

• The strategic positioning of specific ports, such as Itaqui and Sines, is critical for designing robust and efficient intermodal SCNs.
• Stochastic modeling effectively addresses uncertainties in demand and costs, leading to optimized SCNs.
• Balancing economic objectives (cost minimization) with environmental considerations (CO2 emissions) is essential for sustainable agricultural commodity distribution.