Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of good solutions and discuss the effects this has on the algorithm design. This paper presents a carefully-designed hybrid of Monte-Carlo tree search, novel neighbourhood moves, memetic algorithms, and hyper-heuristic methods. The implementation is also engineered to increase the speed with which iterations are performed, and to exploit the computing power of multicore machines. Empirical evaluation shows that the resulting information-sharing multi-component algorithm significantly outperforms other solvers on a set of “hidden” instances, i.e. instances not available at the algorithm design phase

On the minimum cost project schedule

This note draws attention to and corrects an error, which repeats that made in the so-called 'crashing algorithm' given in many textbooks, in a note by Wu and Li on minimal cost project networks published in Omega 22(4), 401-407, in 1994.PERT project management

New validations of PERT times

This paper defends the classical PERT formulas for computing the mean and variance of the random activity time. We show three new validations for them by examining the kurtosis of the beta distribution employed in PERT.PERT beta distribution project management