Triple-Layer Genetic Algorithm (3LGA) for Project Scheduling and Material Ordering Problem with Limited Storage Space

Efficient project management in the construction industry depends on optimizing project scheduling and material ordering, two interdependent processes that significantly impact both costs and durations. Traditional approaches often address these processes separately, neglecting the trade-offs between scheduling costs and material procurement costs. Furthermore, existing studies frequently overlook real-world constraints, such as limited storage space at construction sites. To bridge these gaps, this paper investigates the project scheduling and material ordering problem with limited storage space (PSMOP-LSS) and introduces an integrated model that simultaneously optimizes storage space allocation, activity scheduling, and material ordering. A novel ordering strategy with time period (OSTP) is employed to enhance material procurement under storage constraints. To solve this NP-hard problem, a triple-layer genetic algorithm (3LGA) is proposed, comprising three layers: space allocation, project scheduling, and material ordering. Computational experiments conducted on a case study demonstrate the effectiveness of the 3LGA, achieving significant reductions in project costs and durations compared to conventional ordering strategies. The results highlight trade-offs between cost and duration, offering actionable insights for project managers. This research provides a robust decision-making framework for balancing inventory costs, ordering costs, and project durations in space-constrained environments. Managerial implications include optimizing ordering strategies based on storage capacity and cost parameters.