A multi-attribute GA for piecewise linear Time-Cost Trade-off Scheduling optimization

In this paper, we present a genetic algorithm (GA) for Project Time-Cost Trade-off Scheduling (TCTS) Problem. A piecewise linear function estimates convex non-linear time-cost relation. In the proposed GA, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. The gene value is encoded as the mode index which is selected from among modes of the activity randomly. For indicating construction mode of the activity, integer encoding is applied instead of binary encoding. Additionally, the selection of genes for mutation is based on chromosome value, as solution convergence rate is high. The crossover operator of GA is based on a two-point method. This paper also offers a multi-attribute fitness function for the problem. This function can vary by decision maker (DM) preferences (time or cost). The algorithm is described and evaluated systematically. We also used a case-study to illustrate the proposed GA that is evaluated by comparing to similar algorithms. The computational outcomes validate the effectiveness of the suggested approach.