  In this paper, we investigate the constraint typology of mixed-integer linear
programming MILP formulations. MILP is a commonly used mathematical programming
technique for modelling and solving real-life scheduling, routing, planning,
resource allocation, timetabling optimization problems, providing optimized
business solutions for industry sectors such as: manufacturing, agriculture,
defence, healthcare, medicine, energy, finance, and transportation. Despite the
numerous real-life Combinatorial Optimization Problems found and solved, and
millions yet to be discovered and formulated, the number of types of
constraints, the building blocks of a MILP, is relatively much smaller. In the
search of a suitable machine readable knowledge representation for MILPs, we
propose an optimization modelling tree built based upon an MILP ontology that
can be used as a guidance for automated systems to elicit an MILP model from
end-users on their combinatorial business optimization problems.
