Meta-Heuristic Algorithm Frameworks for a Subcontracted Capital- and Resource-Constrained Project Scheduling Problem with Discounted Cash Flows

Over recent years, a fairly large number of project scheduling problems have been focused on maximizing the Net Present Value (NPV) of cash flows during the project. From the contractor's point of view, cash inflows generally represent their revenues, and cash outflows are expenditures. In most real-life construction projects, the prime contractor typically subcontracts the activities to one or several experienced agents. Considering project scheduling problems with subcontractors is almost ignored in the literature and very little research has been done in the presence of subcontractors. This study aims to develop a Subcontracted Capital and Resource Constrained Project Scheduling Problem with Discounted Cash Flows (SCRCPSPDCF) in which the interactions and negotiations between the client, prime contractor, and subcontractor are considered. The proposed model is discussed under the progress payments that are made to the prime contractor (from the client) and the subcontractors (from the prime contractor). The main advantage of this model is that the cash flow environment is created according to the terms and conditions of all three parties involved in the project. A Hybrid Genetic Algorithm (HGA) which simultaneously benefits from both local search procedure and population-based search is developed to tackle this NP-hard problem. A computational experiment is conducted to evaluate the performance of HGA and to be able to compare the obtained results, Genetic Algorithm (GA) and Multi-Start Iterative Improvement algorithm (MSII) are also utilized. The computational results of the three metaheuristics which are usefully summarized indicate that HGA consistently outperforms the two other metaheuristics. Finally, the impact of two key parameters of the model, related to subcontractors are explored and several conclusions are drawn