Abstract

Many optimal algorithms, heuristics, metaheuristics, simulation approaches, agent-based models, and machine learning tools attempt to solve the job shop scheduling problem (JSSP). This article proposed a model of artificial intelligence with agents representing intelligent products from the perspective of product-driven systems (PDS) to solve this problem at different scales. The intelligent products make all decisions in a distributed way aiming to minimize the makespan and increase the computational efficiency for the JSSP. The agents embed the intelligence function using a based shifting bottleneck heuristic (SBH) approach. The novelty of the proposed approach lies in the automation of decisions in a highly distributed architecture to increase manufacturing flexibility. The results are compared with an optimal integer programming model (IP), SBH, and two conventional heuristics considering instances commonly used in the literature. Concerning the makespan, the proposed approach obtains a fast solution near optimal in instances with a low number of resources and better results than IP and conventional heuristic in instances with a more significant number of resources, increasing the response capacity with a similar computational time.