NSGA-II for Parallel Machine Scheduling with Tardiness and Extra QoS Cost Considerations

We investigate parallel machine scheduling problem of matching the orders and factories with the multiobjective of minimizing the total tardiness and the extra quality of service (QoS) cost in this work. We establish a multi-objective integer programming (MOIP) model, and devise an epsilon constrain...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings of ICSSSM (Print) S. 1 - 7
Hauptverfasser: Zheng, Feifeng, Jin, Kaiyuan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2019
Schlagworte:
Epsilon constraint algorithm
Genetic algorithms
Mathematical model
Non-dominated sorting genetic algorithm II (NSGA-II)
Parallel machine
Parallel machines
Production facilities
Quality of service
Quality of service (QoS)
Scheduling
ISSN:2161-1904
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We investigate parallel machine scheduling problem of matching the orders and factories with the multiobjective of minimizing the total tardiness and the extra quality of service (QoS) cost in this work. We establish a multi-objective integer programming (MOIP) model, and devise an epsilon constraint ( ε-constraint) algorithm together with Non-dominated Sorting Genetic Algorithm II (NSGA-II). The ε-constraint method produces exact solutions for small order instances, while the NSGA-II can efficiently solve large-scale instances. Numerical experiments validate the proposed model and algorithms.
ISSN:2161-1904
DOI:10.1109/ICSSSM.2019.8887842