Open Access

ARTICLE

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

by Min Hu^1,2,3, Zhimin Chen⁴, Yuan Xia⁴, Liping Zhang^1,2,3,*, Qiuhua Tang^1,2,3

1 Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, 430081, China
2 Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, 430081, China
3 Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, 430081, China
4 China Ship Development and Design Center, Wuhan, 430060, China

* Corresponding Author: Liping Zhang. Email:

(This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)

Computer Modeling in Engineering & Sciences 2023, 136(3), 2815-2840. https://doi.org/10.32604/cmes.2023.027146

Received 16 October 2022; Accepted 25 November 2022; Issue published 09 March 2023

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.

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