Fuzzy Based Project Time-Cost Optimization Using Simulated Annealing Search Technique

Abstract

The Project time-cost optimization is inherently a complex task. Because of various kinds of uncertainties, such as weather, productivity level, inflation, human factors etc. during project execution process, time and cost of each activity may vary significantly. The complexity multiplies several folds when the operational times are not deterministic, rather fuzzy in nature. Therefore, deterministic models for time-cost optimization are not yet efficient. It is very difficult to find the exact solution of savings in both time and cost. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, this paper develops a simple approach with Simulated Annealing (SA) based searching technique. The proposed model leads the decision makers to choose the desired solution under different values of a-cut. Finally, taking a real project, the performance of SA has been tested.

Optimisation of labour-intensive productivity for construction projects in Ghana

“…The ability to accomplish deadlines on time and on a budget is the most important skill in optimisation. Strategies such as the fuzzy-based simulation annealing method are used to study the optimisation problem in terms of time and cost (Haque & Hasin, 2016: 1475. The fuzzy-based simulation annealing method works by first defining the problem and identifying the input parameters such as the type of labourintensive technique used, the number of workers, and the amount of time required to complete the task (Rahmanniyay & Yu, 2019: 233).…”

“…Its advantages include its ability to handle uncertainty and vagueness in the input parameters, and to find the global optimum solution rather than getting trapped in local optima. Its limitations include the need for expert knowledge and experience to create the fuzzy rule-based system and the computational complexity of the simulated annealing algorithm (Haque & Hasin, 2016: 1475.…”