Using Meta-Heuristic Optimization Methods in Solving Time–Cost-Quality Trade-Off Problems

Mohammadi M. O., DEDE T., Rao R. V.

International Conference on Advanced Engineering Optimization Through Intelligent Techniques, AEOTIT 2022, Surat, India, 28 - 30 January 2022, pp.107-114 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-981-19-9285-8_11
  • City: Surat
  • Country: India
  • Page Numbers: pp.107-114
  • Keywords: Multi-objective function, Non-dominating sorting-II (NDS-II), Teaching learning-based optimization (TLBO), Time–cost-quality trade-off optimization
  • Karadeniz Technical University Affiliated: Yes


Optimization techniques have clearly been employed for single-objective optimization for several years. Following that, the unification of many objectives in the fitness function has become increasingly prevalent in research studies. The term “multi-objective function” refers to the unification of many objectives in the fitness function. Multi-objective functions are considered in this study to minimize the time of the project and total cost while maximizing project overall quality. A multi-objective optimization strategy is required for satisfying time–cost-quality trade-off optimization. Therefore, the non-dominating sorting-II (NDS-II) concept and the crowding distance mechanism computation are incorporated with the teaching learning-based optimization (TLBO) algorithm to optimize time–cost-quality optimization problems. In the NDS-TLBO-II model, the NDS-II approach and crowding distance computation mechanism are in charge of achieving goals in an effective and efficient way. TLBO’s teacher and learner phases also ensure that the searched solution space is explored and exploited. To optimize the trade-off between time, cost, and quality optimization problems, NDS-TLBO-II is coded in MATLAB. NDS-TLBO-II is utilized to optimize different example problems, and the results show that the NDS-TLBO-II produces satisfactory solutions.