Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach

This paper studies a resource constrained project scheduling problem (RCPSP) under dynamic environments. Along with stochastic activity-durations, resource requests and resource availabilities are also prone to vary with time (i.e., that is, dynamic). To account for stochastic activity durations and the resource dynamic, a chance constrained based mathematical model is proposed. To solve the developed model, an efficient genetic algorithm (GA) based memetic algorithm (MA) is proposed. The proposed algorithm has shown excellent performance for solving 2880 30-activity and 3600 120-activity instances for RCPSP with predefined but time-varying resource requests and availabilities (i.e., RCPSP/t setting). The proposed algorithm has also demonstrated an excellent performance for the extended RCPSP/t setting with stochastic activity durations (i.e., RCPSP/t(d)). Due to the unavailability of any real benchmark instances for this extended RCPSP, this work proposes a pragmatic approach to generate simulated benchmark instances. Numerous time-dependent benchmark instances from the project scheduling library (PSPLIB) are solved to validate the proposed MA. Rigorous experimental analysis with a standard set of 2448 newly generated problem instances for 30, 60, 90, and 120 activities indicates that the proposed meta-heuristic can solve large projects with a reasonable computational burden.

Automated summary

This study introduces a mathematical model based on chance constraints to address the resource constrained project scheduling problem under dynamic environments. An efficient genetic algorithm based memetic algorithm is proposed to solve the model, showcasing excellent performance for solving instances with predefined but time-varying resource requests and availabilities.