An Improved Marriage in Honey-Bee Optimization Algorithm for Minimizing Earliness/Tardiness Penalties in Single-Machine Scheduling with a Restrictive Common Due Date

Pedro Palominos, Mauricio Mazo, Guillermo Fuertes () and Miguel Alfaro
Additional contact information
Pedro Palominos: Industrial Engineering Department, University of Santiago de Chile, Avenida Victor Jara 3769, Santiago 9170124, Chile
Mauricio Mazo: Industrial Engineering Department, University of Santiago de Chile, Avenida Victor Jara 3769, Santiago 9170124, Chile
Guillermo Fuertes: Facultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O’Higgins, Avenida Viel 1497, Ruta 5 Sur, Santiago 8370993, Chile
Miguel Alfaro: Industrial Engineering Department, University of Santiago de Chile, Avenida Victor Jara 3769, Santiago 9170124, Chile

Mathematics, 2025, vol. 13, issue 3, 1-29

Abstract: This study evaluates the efficiency of a swarm intelligence algorithm called marriage in honey-bee optimization (MBO) in solving the single-machine weighted earliness/tardiness problem, a type of NP-hard combinatorial optimization problem. The goal is to find the optimal sequence for completing a set of tasks on a single machine, minimizing the total penalty incurred for tasks being completed too early or too late compared to their deadlines. To achieve this goal, the study adapts the MBO metaheuristic by introducing modifications to optimize the objective function and produce high-quality solutions within reasonable execution times. The novelty of this work lies in the application of MBO to the single-machine weighted earliness/tardiness problem, an approach previously unexplored in this context. MBO was evaluated using the test problem set from Biskup and Feldmann. It achieved an average improvement of 1.03% across 280 problems, surpassing upper bounds in 141 cases (50.35%) and matching or exceeding them in 193 cases (68.93%). In the most constrained problems ( h = 0.2 and h = 0.4), the method achieved an average improvement of 3.77%, while for h = 0.6 and h = 0.8, the average error was 1.72%. Compared to other metaheuristics, MBO demonstrated competitiveness, with a maximum error of 1.12%. Overall, MBO exhibited strong competitiveness, delivering significant improvements and high efficiency in the problems studied.

Keywords: scheduling; production planning; just-in-time; MBO algorithm; SMWE (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/3/418/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/3/418/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:3:p:418-:d:1578364

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().