A genetic algorithm with local search using activity list characteristics for solving resource-constrained project scheduling problem with multiple modes

In this paper, we aim to solve the problem of resource-constrained project scheduling with multiple modes (rc-PSP/mM), in which multiple execution modes are available for each of the project's activity and with minimization of makespan as objective. We present a new genetic algorithm approach in order to solve this problem. In this procedure, we propose a new mutation operator that exploits a critical path and two new local search procedures, i.e. critical path improvement local search (cpiLS) and iterative forward/backward local search (ifbLS), using activity list characteristics. The cpiLS reduces the critical path and the ifbLS improves resource allocation of the schedule of rc-PSP/mM. Also, to evaluate the proposed approach, we compare the results of the computational experiment on certain standard project instances with the several competing heuristic procedures presented in the literature, and it has been revealed that our procedure is one of the most competitive among the algorithms.