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.

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Title:	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:	Mota, Alzira^1,2 atm@isep.ipp.pt, Ávila, Paulo^2,3, Afonso, Luís¹, Bastos, João^2,3, Putnik, Goran⁴
Source:	International Journal of Industrial Engineering. 2026, Vol. 33 Issue 2, p218-239. 22p.
Subjects:	Metaheuristic algorithms, Tabu search algorithm, Genetic algorithms, Deadlines, Scheduling
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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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. [ABSTRACT FROM AUTHOR]
ISSN:	10724761
DOI:	10.23055/ijietap.2026.33.2.11255