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Finance-based scheduling multi-objective optimization: Benchmarking of evolutionary algorithms
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DOI：10.1016/j.autcon.2020.103392.png)
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摘要
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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%.
Keyword:
Finance-based scheduling
Multi-objective optimization
Multiple projects
Evolutionary algorithms
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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