Optimization model for multi-products multi-periods multi-suppliers raw-material selection and composition, and order...

Area of Science:

• Operations Research
• Supply Chain Management
• Industrial Engineering

Background:

• Beverage companies face complex challenges in raw material procurement, including supplier selection, composition, and order quantities.
• Balancing multiple selection criteria and minimum order quantity contracts with fluctuating demand is a significant operational hurdle.

Purpose of the Study:

• To develop an optimization model for multi-product, multi-period raw material selection, composition, and order quantity decisions.
• To incorporate supplier-specific criteria and minimum order quantity contracts into a unified objective function.

Main Methods:

• Formulation of a Mixed Integer Linear Programming (MILP) model.
• Development of a penalty function to enforce one-year minimum order quantity contracts.
• Relaxation of the penalty function upon contract fulfillment.

Main Results:

• The MILP model successfully identifies optimal raw material selection, composition, and order quantities.
• Numerical experiments demonstrate the model's effectiveness across various demand scenarios and objective functions.
• The study quantifies the influence of different decision criteria on the optimal procurement strategy.

Conclusions:

• The proposed optimization model provides an effective solution for complex raw material procurement problems in the beverage industry.
• The integrated approach ensures compliance with supplier contracts while optimizing operational efficiency.
• The findings offer valuable insights for improving supply chain management and decision-making in similar industrial contexts.