Genetic Algorithm for Solving a Just-In-time Inventory Model With Imperfect Rework Implemented in a Serial Multi-echelon System

Abstract

As global industrial competition intensifies, enterprises can achieve substantial competitive advantages in the supply chain management environment by promptly meeting customer demands and efficiently reducing both supply and demand costs. This paper proposes an inventory model for supply chain optimization that considers uncertain delivery lead times and defective products. Solving the model requires solving a nonlinear mixed-integer problem, which traditionally requires considerable time. Solutions to nondeterministic polynomial-time hard problems with high complexity and difficulty are often obtained using heuristic algorithms. Among these algorithms, genetic algorithms have high efficiency and quality. Therefore, we employed a genetic algorithm to solve the proposed model.