Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Abstract

The multi-skill resource-constrained project scheduling problem (MS-RCPSP) is a significant management science problem that extends from the resource-constrained project scheduling problem (RCPSP) and is integrated with a real project and production environment. To solve MS-RCPSP, it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme. This paper proposes an improved gene expression programming (IGEP) approach to explore newly dispatching rules that can broadly solve MS-RCPSP. A new backward traversal decoding mechanism, and several neighborhood operators are applied in IGEP. The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process, and improves the algorithm’s performance. Several neighborhood operators improve the exploration of the potential search space. The experiment takes the intelligent multi-objective project scheduling environment (iMOPSE) benchmark dataset as the training set and testing set of IGEP. Ten newly dispatching rules are discovered and extracted by IGEP, and eight out of ten are superior to other typical dispatching rules.