Integration of resource supply management and scheduling of construction projects using multi-objective whale optimization algorithm and NSGA-II

This study explores the intricate integration and synchronization of supplier selection with the optimal scheduling of multi-mode resource-constrained projects, which is a genuine and complex challenge prevalent in the construction industry, by proposing new multi-objective mathematical modeling considering various items. Within this context, a multifaceted network of concurrent projects (multiproject) is examined with different suppliers' resources (multi-supplier) to minimize the overall projects' delay times and associated costs. The mathematical model formulation also incorporates diverse implementation modes (multi-mode) and the time value of money (TVM). In order to use and unravel the complexities of the proposed model, two distinct algorithms, including a multi-objective whale optimization algorithm (WOA) based on the Pareto archive and the well-known non-dominated sorting genetic algorithm II (NSGA-II), are employed. The algorithms were subjected to a comparative analysis of several sample problems and evaluated against multi-objective criteria, including quality metric (QM), diversity metric (DM), spacing metric (SM), number of solutions (NOS), mean Ideal distance (MID), and computational time. The evaluation reveals that the tailored multi-objective WOA outperforms NSGA-II, exhibiting greater solution precision and diversity. The WOA demonstrates an enhanced ability to efficiently explore the problem's feasible solution space, albeit at the increased computational time to pinpoint optimal solutions. Notably, the validity and practicality of the proposed model and method were field-tested within the context of construction projects in Iran, with the obtained results juxtaposed against the real-world data. The comparative analysis indicates that implementing the scheduling approach and solution methodology espoused by the multi-objective WOA led to significant improvements, with financial gains of up to 6% and time savings reaching 16%. Overall, this research substantiates the proposed model and algorithms' benefits in reducing project costs and delays, offering valuable insights for construction industry practitioners.