MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling Problem
This work addresses the NP-hard RCPSP, a combinatorial optimization problem, by improving upon existing hyper-heuristic methods to enhance scheduling efficiency, though it is incremental as it builds on prior genetic programming approaches.
The paper tackles the resource constrained project scheduling problem (RCPSP) by proposing a MAP-Elites based hyper-heuristic (MEHH) to automatically discover efficient priority rules, resulting in strong improvements in diversity and performance, especially for larger instances.
The resource constrained project scheduling problem (RCPSP) is an NP-Hard combinatorial optimization problem. The objective of RCPSP is to schedule a set of activities without violating any activity precedence or resource constraints. In recent years researchers have moved away from complex solution methodologies, such as meta heuristics and exact mathematical approaches, towards more simple intuitive solutions like priority rules. This often involves using a genetic programming based hyper-heuristic (GPHH) to discover new priority rules which can be applied to new unseen cases. A common problem affecting GPHH is diversity in evolution which often leads to poor quality output. In this paper, we present a MAP-Elites based hyper-heuristic (MEHH) for the automated discovery of efficient priority rules for RCPSP. MAP-Elites uses a quality diversity based approach which explicitly maintains an archive of diverse solutions characterised along multiple feature dimensions. In order to demonstrate the benefits of our proposed hyper-heuristic, we compare the overall performance against a traditional GPHH and priority rules proposed by human experts. Our results indicate strong improvements in both diversity and performance. In particular we see major improvements for larger instances which have been under-studied in the existing literature.