In the current development trend of increasingly complex and large-scale engineering construction projects, the realization of the whole process cost control has become a key target to guarantee the investment benefits of the project and improve the level of project management. The traditional cost management methods generally have the problems of strong stage, subjectivity and untimely response, which make it difficult to effectively respond to the synergistic optimization demand among multiple objectives, phases and parties. In this paper, a multi-objective optimization method based on the integration of evolutionary algorithm and decision support system is proposed for the multi-objective conflict problems of cost, schedule, quality and risk, which are common in the whole process cost control. By constructing an algorithm model with objective hierarchy analysis, fuzzy comprehensive evaluation and non-dominated sorting genetic algorithm (NSGA-II) as the core, the organic unification of multi-objective trade-offs, data dynamic updating and intelligent recommendation in complex engineering decision-making scenarios is realized. On this basis, this paper develops a prototype decision support system platform for engineering practice, covering data input module, model calling module and result visualization module, which supports users to simulate, compare and optimize the cost control schemes at different stages. The results show that the proposed algorithm model can take into account the objectives of schedule, quality and risk while ensuring the rationality of cost control, which is obviously better than the traditional manual decision-making mode, and has good application prospects and popularization value. The research results of this paper provide a theoretical foundation and technical support for the realization of scientific, systematic and intelligent cost management of engineering projects.
Li et al. (Sun,) studied this question.