Hybrid of Simplified Small World and Group Counseling Optimization Algorithms with Matured Random Initialization and Variable Insertion Neighborhood Search Technique to Solve Resource Constrained Project Scheduling Problems with Discounted Cash Flows

Abstract

For long-run projects, the time and order of each activity or job executed matter to contractor firms in terms of profitability. The resource-constrained project scheduling problem with discounted cash flows (RCPSPDC) studies the scheduling of a project with constrained resources to maximize its net present value (NPV). In addition to the rich literature in this field, we add an implementation of RCPSPDC with three more algorithms: simplified small world optimization (SSWO), group counseling optimization (GCO), and a hybrid of these two algorithms with matured random initialization and variable insertion neighborhood search technique. Hybridization of different algorithms has allowed us to combine different search capabilities of various standalone algorithms and eliminate their demerits. Our algorithms were tested on standard 17,280 project instances. The novel hybrid algorithm has a minimal number of parameters and performs better or on par with other existing state-of-the-art hybrid algorithms.