Finance-based scheduling multi-objective optimization: Benchmarking of evolutionary algorithms.

Project scheduling and financing should be adequately integrated during the planning phase to avoid probable cost overruns and delays. Many studies addressed the achievement of integration between project financing and scheduling using multi-objective optimization in particular. However, up to the knowledge of the authors, there is no research conducted to evaluate and assess the performance of the multi-objective optimization techniques employed in this domain. Thus, the current study developed a finance-based scheduling multi-objective optimization model for multiple projects using the elitist non-dominated sorting genetic algorithm (NSGA-II). Further, the obtained results were compared with the results obtained by solving the same problem in another study from the literature using the multi-objective optimization technique of strength Pareto evolutionary algorithm (SPEA). Benchmarking was conducted based on the quality of the obtained solutions and performance. The results indicated that the NSGA-II outperformed SPEA in most aspects with achieved improvements range from 1.7% to 98.2%. • Finance-based scheduling multi-objective optimization NSGA-II model was developed. • NSGA-II was benchmarked against SPEA using a case study from the literature. • Benchmarking aspects are quality solutions, convergence, diversity, and computational time. • NSGA-II outperformed SPEA concerning the majority of the benchmarking aspects. • A procedure for benchmarking new multi-objective techniques was established. [ABSTRACT FROM AUTHOR]