Robust capabilities design and optimization for a modular block-based organization

Abstract

Several organizations perform their tasks by combining different groups of people, equipment, etc., in such a way as to reach a goal. These modules, which we call building blocks (BB), have capabilities and their combination provides an overall capability for the whole organization. How to optimally combine those blocks to achieve capabilities for certain goals is what we address in this work. Therefore, we propose a mathematical optimization model in which binary variables represent the allocation of BBs under an additive rule, i.e. all modules linearly contribute with a specific capability which is later aggregated to build the global desired capability balance. An important aspect to consider is the existence of uncertainty, especially in the capabilities. To handle this, we present a reformulation of the optimization model using Robust Optimization, particularly in the cardinality-constrained robust counterpart for the capability balance constraints. Computational results show that the effects of uncertainty can be efficiently controlled by the robust solution at optimality.