The layout configuration of a chemical process plant has an immense impact on its efficiency of operation, energy consumption, environmental impact and safety levels. This design decision usually lasts throughout the life span of a plant but with the advent of smart manufacturing systems, changes can be made as often as required. At present, there is also a growing need for new chemical plants to meet the increasing demand for chemical products. Hence, automating the layout design process using highly efficient and realistic mathematical models is vital. To aid this, mixed integer linear programming (MILP) models are proposed in this thesis for the efficient determination of multi-floor chemical process plant layout designs. These MILP models proposed obtain the layout configurations factoring in equipment inter-connectivity by pipes, pumping, construction, land purchase and a more realistic description for tall equipment that span through floors, with/without the availability of pre-defined production sections. Using novel integer cuts, each model obtains globally optimal solutions for larger problem instances in short computational times. Safety factors are also introduced with risk being quantified using the Dow's fire and explosion index and the Domino Hazard Index. Hazardous events including jet fires, pool fires, fireballs, flash fires, explosions and/or the resulting blast wave effects are quantified as a function of inter-equipment distances. The associated financial risks in the event of an accident are also determined using a more accurate evaluation of the separation distance between equipment. The resulting MILP model estimates the optimal layout configuration and protection device choices for a chemical process plant. In each of these cases, the unique characteristics and limitations of the proposed models are shown using industry-relevant case studies having a varying number of equipment and requirements, with the models handling all features described with improved computational performance
