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. This problem has been addressed by a competition, and associated set of benchmark instances, as a part of the MISTA 2013 conference. A critical aspect of the benchmarks 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 the resulting competition winning approach; it is 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. The resulting information-sharing multi-component algorithm significantly outperformed the other approaches during the competition, producing the best solution for 17 out of the 20 test instances and performing the best in around 90% of all the trials.