Mobile crane repositioning and on-site logistics coordination constitute a highly coupled, nonlinear decision problem in constrained construction environments. Existing approaches largely decouple these tasks, limiting achievable system-level efficiency. This study introduces EVCrane, a kinematics-informed evolutionary optimization framework that simultaneously optimizes crane stopping positions, stockpile deployment, and task allocation within a unified mixed continuous–binary formulation. Unlike distance-based approximations, the proposed model propagates geometric decisions through coordinated crane motion components—including radial boom adjustment, slewing rotation, and vertical hoisting—ensuring physically consistent cycle-time estimation. A real industrial case study was used to benchmark five optimization algorithms under identical MATLAB R2026a implementations. The Genetic Algorithm (GA) achieved the lowest total crane engaged time (34.516 h), reducing operational duration by 6.45% and utilization cost by 6.32% compared with a deterministic nonlinear programming baseline. Comparative analysis reveals that recombination-based evolutionary search exhibits superior compatibility with assignment-driven non-convex landscapes, outperforming swarm-based and trajectory-based alternatives. Sensitivity analysis confirms structural robustness of optimal spatial configurations under parametric perturbations. The proposed framework advances crane planning from decoupled geometric heuristics toward integrated, physics-consistent, and computationally robust optimization, supporting intelligent and sustainable construction site management.
Srisomboon et al. (Sat,) studied this question.
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