Computational methods for the simultaneous strategic planning of supply chains and batch chemical manufacturing sites

• Supply chains design considering the detailed plant performance is addressed.
• We present efficient solution strategies for large-scale optimization problem.
• These strategies are based on bi-level and Lagrangean decomposition methods.
• We propose also a hybrid approach that takes advantage of the first two strategies.
• These methods can lead to significant CPU savings when compared to the full space MILP.

In this work we present efficient solution strategies for the task of designing supply chains with the explicit consideration of the detailed plant performance of the embedded facilities. Taking as a basis a mixed-integer linear programming (MILP) model introduced in a previous work, we propose three solution strategies that exploit the underlying mathematical structure: A bi-level algorithm, a Lagrangean decomposition method, and a hybrid approach that combines features from both of these two methods. Numerical results show that the bi-level method outperforms the others, leading to significant CPU savings when compared to the full space MILP.