Though the Genetic Algorithm (GA) has received considerable attention recently in solving multi-objective optimization problems, inefficiency regarding performance has been reported in applications related to project scheduling. The degradation in efficiency was magnificent in applications of highly epistatic objective functions, including scheduling problems wherein the parameters being optimized are highly correlated. Furthermore, the crossover, being the dominant operator in GA, added significantly to the observed inefficiency for causing violations in the dependency between activities. Unlike GA, the Evolutionary Programming (EP) algorithm employs only a mutation operator which makes it less vulnerable to the dependency violation issue. This study proposes a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to model and solve scheduling problems of multi-mode activities, including time–cost trade-off and finance-based scheduling with resource levelling. The modification involves the implementation of a new mutation operator to accommodate the scheduling problems in hand. Furthermore, the modified MOEP algorithm is benchmarked against the two multi-objective algorithms of SPEA-II and NSGA-II which have been used extensively in the literature to solve project scheduling problems. The results indicated that the modified MOEP algorithm outperformed SPEA-II and NSGA-II in terms of the diversity and quality of the Pareto optimal set.

尽管遗传算法 (GA) 最近在解决多目标优化问题方面受到了相当大的关注，但在与项目调度相关的应用中，已经报告了有关性能的低效率。在高度上位目标函数的应用中，效率的下降是巨大的，包括被优化的参数高度相关的调度问题。此外，作为 GA 中主要运算符的交叉，显着增加了观察到的导致活动之间依赖关系违规的低效率。与 GA 不同，进化规划 (EP) 算法仅使用变异算子，这使其不易受到依赖违规问题的影响。本研究提出了一种改进的多目标进化规划 (MOEP) 算法来建模和解决多模式活动的调度问题，包括时间成本权衡和具有资源平衡的基于财务的调度。修改涉及实施新的变异算子以适应手头的调度问题。此外，修改后的 MOEP 算法与 SPEA-II 和 NSGA-II 两种多目标算法进行了基准测试，这两种算法已在文献中广泛用于解决项目调度问题。结果表明，改进后的 MOEP 算法在 Pareto 最优集的多样性和质量方面优于 SPEA-II 和 NSGA-II。修改涉及实施新的变异算子以适应手头的调度问题。此外，修改后的 MOEP 算法与 SPEA-II 和 NSGA-II 两种多目标算法进行了基准测试，这两种算法已在文献中广泛用于解决项目调度问题。结果表明，改进后的 MOEP 算法在 Pareto 最优集的多样性和质量方面优于 SPEA-II 和 NSGA-II。修改涉及实施新的变异算子以适应手头的调度问题。此外，修改后的 MOEP 算法与 SPEA-II 和 NSGA-II 两种多目标算法进行了基准测试，这两种算法已在文献中广泛用于解决项目调度问题。结果表明，改进后的 MOEP 算法在 Pareto 最优集的多样性和质量方面优于 SPEA-II 和 NSGA-II。