Oil Supply Chain Integrated Planning based on Holonic Agents and Constraint Programming

The oil area is one of those that may most benefit from the improved efficiency of supply chain management. However, the dynamic behavior of such chains is too complex to be tackled by traditional approaches. Moreover, these chains show several intrinsic characteristics in common with multi-agent systems, which offer the required flexibility to model the complexities and dynamics of real supply chains without rather simplifying assumptions. Since the problem of managing the supply chain has a recursive structure, it becomes more convenient to use a holonic agent-based model, which show a fractal-type structure. Furthermore, the type of relationship between entities in the chain and the need for global optimization suggest to model their interactions in the form of a constraint network. For this reason, this work defines a new optimization problem called Holonic Constraint Optimization Problem (HCOP), which is based on concepts from Distributed Constraint Satisfaction Optimization Problem (DCOP) and holonic agents. In addition, we developed a meta-algorithm based on DPOP algorithm for solving this type of problem, using the FRODO framework in an environment where available centralized optimization algorithms are integrated so as to obtain the optimization. Finally, experiments were performed on a case study of the PETROBRAS company, where a typical supply chain of the petroleum industry was modeled as HCOP. Those experiments integrated the optimization systems for production and logistics, which are representative in relation to actual situations, and allowed the verification of the feasibility of this model and its comparison with conventional approaches.