Integrated Resource-constrained Project Scheduling and Material Ordering Problem with Limited Storage Space

Limited storage space has become a tricky bottleneck for projects that are executed in downtown areas. To relieve this practical dilemma, this paper addresses the concurrent decisions on storage space allocation, activity scheduling and material ordering under limited storage space. Aiming to minimize the total cost of project execution, an integrated model for the resource-constrained project scheduling and material ordering problem with limited storage space (RCPSMOP-LSS) is constructed, and a space-saving material ordering strategy, namely OSTP (Ordering Strategy with Time Period) is utilized. Due to the NP-hardness of the RCPSMOP-LSS, a double-layer heuristic algorithm (DLHA) is designed to solve the model. In the outer layer, an improved genetic algorithm is proposed to obtain a space allocation plan and a schedule of all activities. Based on the outcome from the outer layer, an exact algorithm is developed in the inner layer to determine the material ordering scheme. Furthermore, numerical experiments are carried out to verify the efficiency of the proposed DLHA. Specifically, the Taguchi method is utilized to calibrate the parameters of DLHA. Besides, the experimental results demonstrate that the DLHA can greatly save the total cost compared to a traditional decentralized method. The efficiency of DLHA is also superior to CPLEX solver and TDM, especially for medium- and large-sized instances. Finally, a sensitivity analysis of key components shows that increasing the storage space exerts an increasing effect on cost reduction.