Multi-Stage Tower-Crane Layout Optimization Incorporating Relocation Penalties and Operational Constraints

Tower-crane layout planning becomes challenging when deployment decisions are coupled across construction stages. This study develops a dynamic multi-stage mixed-integer linear programming (MILP) model that jointly determines crane type, number, and location while considering lifting demand, operational constraints, and inter-stage relocation penalties. BIM is used as a data-extraction and visualization platform to provide stage-dependent model inputs, screen feasible candidate locations, and support spatial verification of the optimized layouts. The model was evaluated using a reconstructed published benchmark and a three-stage convention-center project with irregular geometry. The benchmark demonstrated the transferability of the proposed model to an existing multi-stage problem and revealed rapid nonlinear growth in computational effort: a 3.79-fold increase in the combined number of candidate crane and demand locations led to a 318.39-fold increase in solution time, although all tested instances reached a 0% MIP gap. For the convention-center case, the model contained 365,897 variables and 890,873 constraints and was solved by Gurobi in 1415 s with a 0% MIP gap. The dynamic layout reduced the total cost from $931,020 to $621,880, corresponding to a 33.20% saving relative to the contractor’s plan. It also saved $41,400, or 6.24% compared with the static stage-wise layout by avoiding unnecessary relocation. These results demonstrate solver-certified optimality for medium-scale multi-stage planning within the predefined candidate-location set and quantify the economic effect of inter-stage deployment decisions.