Matheuristics in Scheduling Problems: A Global Bibliometric Analysis

Abstract

 Matheuristic approaches, which seamlessly combine exact mathematical programming with heuristic algorithms, have gained significant momentum in solving complex, NP-hard scheduling problems. Despite their growing popularity and effectiveness, there is a notable lack of comprehensive bibliometric mapping in this specific subfield. This study conducts a quantitative bibliometric analysis to uncover the structural and dynamic characteristics of literature at the intersection of matheuristics and scheduling. A total of 370 peer-reviewed articles and conference papers, published between 2007 and 2025, were extracted from the Scopus database and analyzed using the bibliometrix R package. The findings reveal a steady, logarithmic increase in scientific production, driven by the demands of complex, large-scale industrial applications. Co-occurrence network analyses confirm that current research is heavily centered on the integration of Mixed-Integer Linear Programming (MILP) with heuristic frameworks. The analysis identifies critical research gaps, suggesting that future studies should focus on energy-efficient scheduling, dynamic environments with stochastic constraints, and algorithm hybridization with machine learning.