Sustainable survival pyramid model to balance four factors of cost, quality, risk and time limitation in project management under uncertainty


Creative Commons License

Safaei M.

Pakistan Journal of Statistics and Operation Research, cilt.16, sa.2, ss.287-294, 2020 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 2
  • Basım Tarihi: 2020
  • Doi Numarası: 10.18187/pjsor.v16i2.3203
  • Dergi Adı: Pakistan Journal of Statistics and Operation Research
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, zbMATH
  • Sayfa Sayıları: ss.287-294
  • Anahtar Kelimeler: Model development, Nsga-ii, Survival pyramid, Sustainable decision making
  • İstanbul Gelişim Üniversitesi Adresli: Evet

Özet

© 2020 University of the Punjab.The final agreement on the timing of project completion is one of the obvious problems between project managers and their clients. There have been numerous reports of customers requesting shorter completion times than previously announced. This request will affect the three project factors of overall cost, final quality of the project, and risk of implementation. This paper proposes a multipurpose cumulative complex linear programming to minimize "project overhead, " "increase projects total risk" and "increase overall project quality" due to "time constraints. " In other words, the proposed study is fully implemented among the four goals mentioned to shorten the project duration. Computational experiments have also been used to evaluate the performance of the proposed model. The main objective of this paper is to optimize the integration of the four factors of the survival pyramid (time, cost, quality, and risk) in industrial projects simultaneously under uncertainty. An innovative solution approach based on the multi-objective genetic algorithm (NSGA-II) is presented. This model is then used to solve a problem in another study and its results, strengths, and weaknesses compared to the previous model are evaluated. The results show the performance of the proposed model in all four factors is better than the previous models.