Table 3 Algorithmic approaches for scheduling and logistics in prefabrication.
From: Dual-objective optimization of prefabricated component logistics based on JIT strategy
Research content | References | Objective and method |
|---|---|---|
Algorithm applications | Fang et al.54 | Enhances classical genetic algorithm using activity-based encoding, setting start time and transport frequency as genes, to optimize project duration and logistics costs. Employs neighborhood-based crossover and mutation for improved local search |
Proposes a metaheuristic for vehicle routing with time window and synchronization constraints in prefabrication, combining hybrid initialization, encoding repair, and variable-length local search to minimize combined transport and inventory costs efficiently | ||
Deb et al.57 | Introduces NSGA for multi-objective optimization but highlights issues like high computation and potential premature convergence, risking loss of quality solutions during crossover and mutation | |
Deb et al.58 | Develops NSGA-II, integrating fast non-dominated sorting, crowding distance, and elitism to retain Pareto optimal solutions, enhancing diversity and convergence in multi-objective optimization. Widely applied in complex optimization tasks |
