Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms

Area of Science:

• Operations Research
• Supply Chain Management
• Computational Intelligence

Background:

• Supplier selection and order allocation significantly impact profitability and product costs.
• Existing models may not adequately address the complexities of cost minimization in supply chains.

Purpose of the Study:

• To propose a novel, non-linear model for optimizing supplier selection and order quantity allocation.
• To minimize total cost per time unit, incorporating various cost factors and constraints.

Main Methods:

• Developed a non-linear optimization model for the supplier selection and order quantity allocation problem.
• Implemented metaheuristic algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE).
• Validated the model using a reference problem and compared results with existing literature.

Main Results:

• The proposed model effectively minimizes total supply chain costs.
• Metaheuristic algorithms demonstrated superior performance in finding lower-cost solutions.
• PSO, GA, and DE provided faster solutions compared to analytical methods for the non-linear model.

Conclusions:

• The novel non-linear model is effective for supplier selection and order allocation.
• Metaheuristic algorithms are suitable and efficient for solving complex, non-linear supply chain optimization problems.
• The study highlights the potential for significant cost reduction through advanced optimization techniques.