Multi-mode resource-constrained project scheduling and soft and hard time windows for ending activities

This study developed a mathematical model to optimize multi-mode resource-constrained project scheduling problem and soft and hard time windows for ending activities. The developed model optimized project scheduling problem in the near real world, taking into account multi-mode time of activities, as well as hard and soft time windows simultaneously. In order to optimize the model, meta-heuristic genetic algorithms and simulated annealing algorithm were used. Input parameters of these algorithms were set by response level method; then, performance of the algorithms was measured in small sized problems using exact solution software. Statistical tests were applied; efficiency of the algorithms was evaluated in solving large-scale real-world problems. Computational results showed that the genetic algorithm had a higher efficiency in optimizing the suggested model and was able to achieve higher quality solutions in lower computing time