Solving bi-level linear programming problem through hybrid of immune genetic algorithm and particle swarm optimization algorithm

Abstract

Bi-level linear programming, consisting of upper level and lower level objectives, is a technique for modeling decentralized decision. This study presents a hybrid of immune genetic algorithm and vector-controlled particle swarm optimization (IGVPSO) to solve the bi-level linear programming problem (BLPP). It is applied to a supply chain model that is a BLPP. Using four problems from the literature and the supply chain distribution models, the computational results indicate that the proposed method is superior to some algorithms.