The Permutation Flow Shop Scheduling Problem with Human Resources: MILP Models, Decoding Procedures, NEH-Based Heuristics, and an Iterated Greedy Algorithm

Victor Fernandez-Viagas (), Luis Sanchez-Mediano, Alvaro Angulo-Cortes, David Gomez-Medina and Jose Manuel Molina-Pariente
Additional contact information
Victor Fernandez-Viagas: Department of Industrial Organization and Business Management I, School of Engineering, University of Seville, 41091 Seville, Spain
Luis Sanchez-Mediano: Department of Industrial Organization and Business Management I, School of Engineering, University of Seville, 41091 Seville, Spain
Alvaro Angulo-Cortes: Department of Industrial Organization and Business Management I, School of Engineering, University of Seville, 41091 Seville, Spain
David Gomez-Medina: Department of Industrial Organization and Business Management I, School of Engineering, University of Seville, 41091 Seville, Spain
Jose Manuel Molina-Pariente: Department of Industrial Organization and Business Management I, School of Engineering, University of Seville, 41091 Seville, Spain

Mathematics, 2022, vol. 10, issue 19, 1-32

Abstract: In this paper, we address the permutation flow shop scheduling problem with sequence-dependent and non-anticipatory setup times. These setups are performed or supervised by multiple servers, which are renewable secondary resources (typically human resources). Despite the real applications of this kind of human supervision and the growing attention paid in the scheduling literature, we are not aware of any previous study on the problem under consideration. To cover this gap, we start theoretically addressing the problem by: proposing three mixed-integer linear programming models to find optimal solutions in the problem; and proposing different decoding procedures to code solutions in approximated procedures. After that, the best decoding procedure is used to propose a new mechanism that generates 896 different dispatching rules, combining different measures, indicators, and sorting criteria. All these dispatching rules are embedded in the traditional NEH algorithm. Finally, an iterated greedy algorithm is proposed to find near-optimal solutions. By doing so, we provide academics and practitioners with efficient methods that can be used to obtain exact solutions of the problem; applied to quickly schedule jobs and react under changes; used for initialisation or embedded in more advanced algorithms; and/or easily updated and implemented in real manufacturing scenarios.

Keywords: scheduling; flow shop; MILP; decoding procedure; makespan; flow shop; human resources; multiple servers; sequence-dependent setups; iterated greedy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3446/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3446/ (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:10:y:2022:i:19:p:3446-:d:921821

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 ().