Resource constrained project scheduling problem with discounted earliness–tardiness penalties: Mathematical modeling and solving procedure

• We develop a new model for the resource constrained project scheduling (RCPSP).
• We propose two meta-heuristic algorithms to solve the problem.
• We tune the meta-heuristics parameters using statistical methods.
• We compare the performance of proposed algorithms on a set of instances and show the results.

In this paper, we consider the resource-constrained project scheduling problem with a due date for each activity. The objective is to minimize the net present value of the earliness–tardiness penalty costs. The problem is first mathematically modeled. Then, two meta-heuristics, genetic algorithm and simulated annealing are proposed to solve this strongly NP-hard problem. Design of experiments and response surface methodology are employed to fine-tune the meta-heuristics’ parameters. Finally, a comprehensive computational experiment is described, performed on a set of instances and the results are analyzed and discussed.