The timely production and distribution of rapidly perishable goods such as concrete is a complex combinatorial optimization problem in the context of supply chain management [
]. The problem involves several tightly interrelated scheduling and routing problems that have to be solved considering a trade-off of production and delivery costs. Different approaches have been developed for this problem: a hybrid meta-heuristic method combining genetic algorithms with constructive heuristics ; a hybrid approach combining genetic algorithms and ant colony optimization [
]. However, all these approaches consider the optimization problems as separate problems. This paper introduces a novel approach, based on he distributed optimization paradigm proposed in [
], which obtained good results for other supply chains examples. In the distributed optimization framework, both problems are optimized in parallel and exchange information during the optimization process through the pheromone matrix. In this way, it is possible to bias the solution of one of the system in order to improve the performance of the other and thus achieve a better global solution for the supplychain. The simulation results show that this approach globally improves the supply chain results.