A Comparative Study of Metaheuristic Algorithms for Scheduling on Unrelated Parallel Machines: Minimizing Weighted Earliness–Tardiness with Non-Zero Release Times and Distinct Due Dates

Authors

  • Alzira Mota Department of Mathematics, ISEP, Polytechnic of Porto, Porto, Portugal | Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
  • Paulo Ávila Department of Mechanical Engineering, ISEP, Polytechnic of Porto, Porto, Portugal | Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
  • Luís Afonso Department of Mathematics, ISEP, Polytechnic of Porto, Porto, Portugal
  • João Bastos Department of Mechanical Engineering, ISEP, Polytechnic of Porto, Porto, Portugal | Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto, Portugal
  • Goran Putnik Algoritmi Research Center/LASI, University of Minho, Guimarães, Portugal

DOI:

https://doi.org/10.23055/ijietap.2026.33.2.11255

Keywords:

Unrelated parallel machine scheduling, Just-in-time manufacturing, earliness and tardiness, metaheuristics, genetic algorithm, tabu search, variable neighbourhood search, production scheduling, hybrid optimization, operations research

Abstract

This study addresses the unrelated parallel machine scheduling problem in a just-in-time manufacturing context, aiming to minimize total weighted earliness and tardiness. The problem formulation incorporates non-zero release times and distinct due dates, reflecting realistic industrial environments. Three hybrid metaheuristic approaches: Genetic Algorithm, Tabu Search, and Variable Neighborhood Search, are proposed and analyzed. The main contribution of this work lies in integrating a linear-programming-based decoding procedure into each metaheuristic to determine job start times and accurately evaluate solution quality, while preserving the general structure of the unrelated parallel machine scheduling problem. The proposed methods are evaluated using a set of medium- and large-scale instances generated for this study. Computational analysis reveals differences in performance among the metaheuristics, with Tabu Search exhibiting the most consistent and effective behavior in terms of solution quality and convergence speed.

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Published

2026-04-09

How to Cite

Mota, A., Ávila, P., Afonso, L., Bastos, J., & Putnik, G. (2026). A Comparative Study of Metaheuristic Algorithms for Scheduling on Unrelated Parallel Machines: Minimizing Weighted Earliness–Tardiness with Non-Zero Release Times and Distinct Due Dates. International Journal of Industrial Engineering: Theory, Applications and Practice, 33(2). https://doi.org/10.23055/ijietap.2026.33.2.11255

Issue

Vol. 33 No. 2 (2026)

Section

Production Planning and Control

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