Data-model driven intelligent optimization scheduling for distributed production material supply

WANG Jing-jing; GUO Qi; HAN Hong-gui

doi:10.7641/CTA.2025.50266

Abstract：With the increasing prevalence of cooperation among manufacturing enterprises,distributed manufacturing characterized by the optimal sharing of resources has emerged as a modern production paradigm.As a core resource in production manufacturing,efficient scheduling of material supply between suppliers and manufacturers can significantly enhance distributed production efficiency and reduce production costs.To addresse the distributed production material supply scheduling problem,a data-model driven intelligent optimization approach is proposed to simultaneously optimize both manufacturer satisfaction and material tardiness.Firstly,a mixed-integer programming model incorporating a dy-namic replenishment mechanism is formulated for the complex supply network comprising multiple warehouses,factories,and material types.Secondly,the mathematical solver Gurobi and heuristic rules are employed respectively to maximize satisfaction and minimize tardiness for the small-scaled problems and large-scaled problems.Thus,two high-quality single-objective optimal solutions are yielded as the start and end points for multi-objective optimization.Thirdly,an adaptive path-relinking mechanism is designed based on initial solutions,utilizing a difference-driven adaptive exploration strategy to enhance diversity of the multi-objective solutions.Finally,a goal-driven local intensification is proposed to further im-prove exploitation.Experimental results on the instances with varying scales demonstrate that the proposed algorithm can effectively solve the distributed material supply scheduling problem.