Collaborative production planning in distributed decision-making supply chain

A Lagrange-relaxation algorithm based model was developed to solve the problem of collaborative production planning in distributed supply chain with multi-stage, multi-product characteristics. In this model, the independent production planning model was constructed for each enterprise. Lagrange-relaxation algorithm was used to relax the material flow balance constraints among different enterprises. Thus the integrated decision-making collaboration problem was divided into some independent decision-making issues scattered among enterprises. Subgradient algorithm was used to update the Lagrange multiplier. Through iteration optimization, the collaborative production planning was realized and the nearest optimal solution was achieved. In addition, some simulation experiments were designed to reveal the effects of collaboration model for complicated supply chain. Results showed that the collaboration approach could reach the best solution. And the convergence speed and collaboration effect were better than Genetic Algorithm (GA).