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Multi-mode resource and precedence-constrained project scheduling is awell-known challenging real-world optimisation problem. An important variant ofthe problem requires scheduling of activities for multiple projects consideringavailability of local and global resources while respecting a range ofconstraints. A critical aspect of the benchmarks addressed in this paper isthat the primary objective is to minimise the sum of the project completiontimes, with the usual makespan minimisation as a secondary objective. Weobserve that this leads to an expected different overall structure of goodsolutions and discuss the effects this has on the algorithm design. This paperpresents a carefully designed hybrid of Monte-Carlo tree search, novelneighbourhood moves, memetic algorithms, and hyper-heuristic methods. Theimplementation is also engineered to increase the speed with which iterationsare performed, and to exploit the computing power of multicore machines.Empirical evaluation shows that the resulting information-sharingmulti-component algorithm significantly outperforms other solvers on a set of"hidden" instances, i.e. instances not available at the algorithm design phase.

Combining Monte-Carlo and Hyper-heuristic methods for the Multi-mode Resource-constrained Multi-project Scheduling Problem