A production planning model considering uncertain demand using two-stage stochastic programming in a fresh vegetable...

Area of Science:

• Operations Research
• Supply Chain Management
• Agricultural Economics

Background:

• Production planning models are increasingly relevant in the agricultural sector.
• Fresh vegetable supply chains face challenges in selecting suppliers to meet demand and quality standards.
• Minimizing procurement costs and farm-to-table time are critical for fresh produce.

Purpose of the Study:

• To develop a two-stage stochastic location model for selecting farms for seasonal contracts.
• To minimize overall procurement costs while ensuring future demand is met.
• To optimize the selection of suppliers for high-quality, fresh vegetables with minimal transit time.

Main Methods:

• Formulation of a two-stage stochastic location model.
• Application of Lagrangian relaxation and parallel computing algorithms for efficient problem-solving.
• Comparison of proposed algorithms against standard solvers like CPLEX.

Main Results:

• The proposed algorithms demonstrate significant computational gains compared to the CPLEX solver.
• The model effectively determines optimal farm selections for seasonal contracts.
• Validation of the model's ability to minimize costs and meet demand.

Conclusions:

• The developed model provides a competitive advantage for purchase managers in the fresh vegetable industry.
• Efficient algorithms ensure practical application of the model for large-scale instances.
• The model contributes to optimizing fresh vegetable supply chain operations.